Epi/0000755000175100001440000000000012576452270011017 5ustar hornikusersEpi/inst/0000755000175100001440000000000012531361114011757 5ustar hornikusersEpi/inst/CITATION0000644000175100001440000000465512531361114013126 0ustar hornikuserscitHeader("To cite Epi in publications use:") ## R >= 2.8.0 passes package metadata to citation(). if(!exists("meta") || is.null(meta)) meta <- packageDescription("Epi") year <- sub("-.*", "", meta$Date) note <- sprintf("R package version %s", meta$Version) citEntry(entry = "Manual", title = "{Epi}: A Package for Statistical Analysis in Epidemiology", author = personList(as.person("Bendix Carstensen"), as.person("Martyn Plummer"), as.person("Esa Laara"), as.person("Michael Hills")), year = year, note = note, url = "http://CRAN.R-project.org/package=Epi", textVersion = paste("Bendix Carstensen, Martyn Plummer, Esa Laara, Michael Hills", sprintf("(%s).", year), "Epi: A Package for Statistical Analysis in Epidemiology.", paste(note, ".", sep = ""), "URL http://CRAN.R-project.org/package=Epi") ) citEntry(entry = "Article", title = "{Lexis}: An {R} Class for Epidemiological Studies with Long-Term Follow-Up", author = personList(as.person("Martyn Plummer"), as.person("Bendix Carstensen")), journal = "Journal of Statistical Software", year = "2011", volume = "38", number = "5", pages = "1--12", url = "http://www.jstatsoft.org/v38/i05/", textVersion = paste("Martyn Plummer, Bendix Carstensen (2011).", "Lexis: An R Class for Epidemiological Studies with Long-Term Follow-Up.", "Journal of Statistical Software, 38(5), 1-12.", "URL http://www.jstatsoft.org/v38/i05/."), header = "If you use Lexis objects/diagrams, please also cite:" ) citEntry(entry = "Article", title = "Using {Lexis} Objects for Multi-State Models in {R}", author = personList(as.person("Bendix Carstensen"), as.person("Martyn Plummer")), journal = "Journal of Statistical Software", year = "2011", volume = "38", number = "6", pages = "1--18", url = "http://www.jstatsoft.org/v38/i06/", textVersion = paste("Bendix Carstensen, Martyn Plummer (2011).", "Using Lexis Objects for Multi-State Models in R.", "Journal of Statistical Software, 38(6), 1-18.", "URL http://www.jstatsoft.org/v38/i06/."), header = "For use of Lexis objects in multi-state models, please also cite:" ) Epi/inst/doc/0000755000175100001440000000000012576333004012532 5ustar hornikusersEpi/inst/doc/Follow-up.pdf0000644000175100001440000065361612531361114015124 0ustar hornikusers%PDF-1.5 % 1 0 obj << /S /GoTo /D (section.1) >> endobj 4 0 obj (Follow-up data in the Epi package) endobj 5 0 obj << /S /GoTo /D (section.2) >> endobj 8 0 obj (Timescales) endobj 9 0 obj << /S /GoTo /D (section.3) >> endobj 12 0 obj (Splitting the follow-up time along a timescale) endobj 13 0 obj << /S /GoTo /D (section.4) >> endobj 16 0 obj (Splitting time at a specific date) endobj 17 0 obj << /S /GoTo /D (section.5) >> endobj 20 0 obj (Competing risks \204 multiple types of events) endobj 21 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Epi/inst/doc/index.html0000644000175100001440000000101012531361114014511 0ustar hornikusers Vignettes for the Epi package</a>

Vignettes for the Epi package

 Here is the website for the Epi package. Epi/inst/doc/sim-Lexis.rnw0000644000175100001440000014272412576333004015146 0ustar hornikusers\SweaveOpts{results=verbatim,keep.source=TRUE,include=FALSE,eps=FALSE,prefix.string=sL} %\VignetteIndexEntry{Simulation of multistate models with multiple timescales: simLexis} \documentclass[a4paper,twoside,12pt]{report} %---------------------------------------------------------------------- % General information \newcommand{\Title}{Simulation of\\ multistate models with\\ multiple timescales:\\ \texttt{simLexis} in the \texttt{Epi} package} \newcommand{\Tit}{Multistate models with multiple timescales} \newcommand{\Version}{Version 2.2} \newcommand{\Dates}{\today} \newcommand{\Where}{SDC} \newcommand{\Homepage}{\url{http://BendixCarstensen.com/Epi}} \newcommand{\Faculty}{\begin{tabular}{rl} Bendix Carstensen & Steno Diabetes Center, Gentofte, Denmark\\ & {\small \& Department of Biostatistics, University of Copenhagen} \\ & \texttt{bxc@steno.dk}\\ & \url{http://BendixCarstensen.com} \\[1em] \end{tabular}} %---------------------------------------------------------------------- % Packages %\usepackage[inline]{showlabels} \usepackage[T1]{fontenc} \usepackage[latin1]{inputenc} \usepackage[english]{babel} \usepackage[font=it,labelfont=normalfont]{caption} \usepackage[colorlinks,urlcolor=blue,linkcolor=red]{hyperref} \usepackage[ae,hyper]{Rd} \usepackage[dvipsnames]{xcolor} \usepackage{makeidx,Sweave,floatflt,amsmath,amsfonts,amsbsy,enumitem,verbatim,dcolumn,needspace} \usepackage{booktabs,longtable,rotating,graphicx,verbatim,fancyhdr,datetime,afterpage} % \usepackage{times} \renewcommand{\textfraction}{0.0} \renewcommand{\topfraction}{1.0} \renewcommand{\bottomfraction}{1.0} \renewcommand{\floatpagefraction}{0.9} % \usepackage{mslapa} \definecolor{blaa}{RGB}{99,99,255} \DeclareGraphicsExtensions{.pdf,.jpg} %---------------------------------------------------------------------- % The usual usefuls % \input{c:/util/tex/useful.tex} \providecommand{\pmat}[1]{\text{\rm P}\left\{#1\right\}} \providecommand{\dif}{{\,\mathrm d}} % Simple insertion of figures \newcommand{\rwpre}{sL} \newcommand{\insfig}[3]{ \begin{figure}[h] \centering \includegraphics[width=#2\textwidth]{\rwpre-#1} \caption{#3} \label{fig:#1} \end{figure}} %---------------------------------------------------------------------- % Layout of pages \oddsidemargin 1mm \evensidemargin 1mm \topmargin -5mm \headheight 8mm \headsep 5mm \textheight 240mm \textwidth 165mm %\footheight 5mm \footskip 15mm \renewcommand{\topfraction}{0.9} \renewcommand{\bottomfraction}{0.9} \renewcommand{\textfraction}{0.1} \renewcommand{\floatpagefraction}{0.9} \renewcommand{\headrulewidth}{0.1pt} \setcounter{secnumdepth}{4} \setcounter{tocdepth}{4} %---------------------------------------------------------------------- % Here is the document starting with the titlepage \begin{document} %---------------------------------------------------------------------- % The title page \setcounter{page}{1} \pagenumbering{roman} \pagestyle{plain} \thispagestyle{empty} \vspace*{0.05\textheight} \flushright % The blank below here is necessary in order not to muck up the % linespacing in the title if it has more than 2 lines {\Huge \bfseries \Title }\ \\[-1.5ex] \noindent\textcolor{blaa}{\rule[-1ex]{\textwidth}{5pt}}\\[2.5ex] \large \Where \\ \Dates \\ \Homepage \\ \Version \\[1em] \normalsize Compiled \today,\ \currenttime\\ \normalsize \vfill \Faculty % End of titlepage % \newpage %---------------------------------------------------------------------- % Table of contents \tableofcontents %---------------------------------------------------------------------- % General text layout \raggedright \parindent 1em \parskip 0ex \cleardoublepage %---------------------------------------------------------------------- % General page style \pagenumbering{arabic} \setcounter{page}{1} \pagestyle{fancy} \renewcommand{\chaptermark}[1]{\markboth{\textsl{#1}}{}} \renewcommand{\sectionmark}[1]{\markright{\thesection\ \textsl{#1}}{}} \fancyhead[EL]{\bf \thepage \quad \rm \leftmark} \fancyhead[ER]{\sl \Tit} \fancyhead[OR]{\rm \rightmark \quad \bf \thepage} \fancyfoot{} %---------------------------------------------------------------------- % Here comes the substance \chapter{Using \texttt{simLexis}} \section{Introduction} This vignette explains the machinery behind simulation of life histories through multistate models implemented in \texttt{simLexis}. In \texttt{simLexis} transition rates are allowed to depend on multiple time scales, including timescales defined as time since entry to a particular state (duration). This therefore also covers the case where time \emph{at} entry into a state is an explanatory variable for the rates, since time at entry is merely time minus duration. Thus, the set-up here goes beyond Markov- and semi-Markov-models, and brings simulation based estimation of state-occupancy probabilities into the realm of realistic multistate models. The basic idea is to simulate a new \texttt{Lexis} object \cite{Plummer.2011,Carstensen.2011a} as defined in the \texttt{Epi} package for \R, based on 1) a multistate model defined by its states and the transition rates between them and 2) an initial population of individuals. Thus the output will be a \texttt{Lexis} object describing the transitions of a predefined set of persons through a multistate model. Therefore, if persons are defined to be identical at start, then calculation of the probability of being in a particular state at a given time boils down to a simple enumeration of the fraction of the persons in the particular state at the given time. Bar of course the (binomial) simulation error, but this can be brought down by simulation a sufficiently large number of persons. An observed \texttt{Lexis} object with follow-up of persons through a number of states will normally be the basis for estimation of transition rates between states, and thus will contain all information about covariates determining the occurrence rates, in particular the \emph{timescales} \cite{Iacobelli.2013}. Hence, the natural input to simulation from an estimated multistate model will typically be an object of the same structure as the originally observed. Since transitions and times are what is simulated, any values of \texttt{lex.Xst} and \texttt{lex.dur} in the input object will of course be ignored. This first chapter of this vignette shows by an example how to use the function \texttt{simLexis} and display the results. The subsequent chapter discusses in more detail how the simulation machinery is implemented and is not needed for the practical use of \texttt{simLexis}. \section{\texttt{simLexis} in practice} This section is largely a commented walk-trough of the example from the help-page of \texttt{simLexis}, with a larger number of simulated persons in order to minimize the pure simulation variation. When we want to simulate transition times through a multistate model where transition rates may depend on time since entry to the current or a previous state, it is essential that we have a machinery to keep track of the transition time on \emph{all} time scales, as well as a mechanism that can initiate a new time scale to 0 when a transition occurs to a state where we shall use time since entry as determinant of exit rates from that state. This is provided by \texttt{simLexis}. \subsection{Input for the simulation} Input for simulation of a single trajectory through a multistate model requires a representation of the \emph{current status} of a person; the starting conditions. The object that we supply to the simulation function must contain information about all covariates and all timescales upon which transitions depend, and in particular which one(s) of the timescales that are defined as time since entry into a particular state. Hence, starting conditions should be represented as a \texttt{Lexis} object (where \texttt{lex.dur} and \texttt{lex.Xst} are ignored, since there is no follow-up yet), where the time scale information is in the attributes \texttt{time.scale} and \texttt{time.since} respectively. Thus there are two main arguments to a function to simulate from a multistate model: \begin{enumerate} \item A \texttt{Lexis} object representing the initial states and covariates of the population to be simulated. This has to have the same structure as the original \texttt{Lexis} object representing the multistate model from which transition rates in the model were estimated. As noted above, the values for \texttt{lex.Xst} and \texttt{lex.dur} are not required (since these are the quantities that will be simulated). \item A transition object, representing the transition intensities between states, which should be a list of lists of intensity representations. As an intensity representation we mean a function that for given a \texttt{Lexis} object that can be used to produce estimates of the transition intensities at a set of supplied time points since the state represented in the \texttt{Lexis} object. The names of the elements of the transition object (which are lists) will be names of the \emph{transient} states, that is the states \emph{from} which a transition can occur. The names of the elements of each of these lists are the names of states \emph{to} which transitions can occur (which may be either transient or absorbing states). Hence, if the transition object is called \texttt{Tr} then \verb+TR$A$B+ (or \verb+Tr[["A"]][["B"]]+) will represent the transition intensity from state \texttt{A} to the state \texttt{B}. The entries in the transition object can be either \texttt{glm} objects, representing Poisson models for the transitions, \texttt{coxph} objects representing an intensity model along one time scale, or simply a function that takes a \texttt{Lexis} object as input returns an estimated intensity for each row. \end{enumerate} In addition to these two input items, there will be a couple of tuning parameters. The output of the function will simply be a \texttt{Lexis} object with simulated transitions between states. This will be the basis for deriving sensible statistics from the \texttt{Lexis} object --- see next section. \section{Setting up a \texttt{Lexis} object} As an example we will use the \texttt{DMlate} dataset from the \texttt{Epi} package; it is a dataset simulated to resemble a random sample of 10,000 patients from the Danish National Diabetes Register. We start by loading the \texttt{Epi} package: <>= options( width=90 ) library( Epi ) print( sessionInfo(), l=F ) @ % First we load the diabetes data and set up a simple illness-death model: <>= data(DMlate) dml <- Lexis( entry = list(Per=dodm, Age=dodm-dobth, DMdur=0 ), exit = list(Per=dox), exit.status = factor(!is.na(dodth),labels=c("DM","Dead")), data = DMlate ) @ % This is just data for a simple survival model with states ``DM'' and ``Dead''. Now we cut the follow-up at insulin start, which for the majority of patients (T2D) is a clinical indicator of deterioration of disease regulation. We therefore also introduce a new timescale, and split the non-precursor states, so that we can address the question of ever having been on insulin: <>= dmi <- cutLexis( dml, cut = dml$doins, pre = "DM", new.state = "Ins", new.scale = "t.Ins", split.states = TRUE ) summary( dmi ) str(dmi) @ % $ We can show how many person-years we have and show the number of transitions and transition rates (per 1000), using the \texttt{boxes.Lexis} function to display the states and the number of transitions between them: <>= boxes( dmi, boxpos = list(x=c(20,20,80,80), y=c(80,20,80,20)), scale.R = 1000, show.BE = TRUE ) @ % \insfig{boxes}{0.8}{Data overview for the \textrm{\tt dmi} dataset. Numbers in the boxes are person-years and the number of persons who begin, resp. end their follow-up in each state, and numbers on the arrows are no. of transitions and rates (transition intensities) per 1000 PY.} \section{Analysis of rates} In the \texttt{Lexis} object (which is just a data frame) each person is represented by one record for each transient state he occupies, thus in this case either 1 or 2 records; those who have a recorded time both without and with insulin have two records. In order to be able to fit Poisson models with occurrence rates varying by the different time-scales, we split the follow-up in 6-month intervals for modeling: <>= Si <- splitLexis( dmi, 0:30/2, "DMdur" ) dim( Si ) print( subset( Si, lex.id==97 )[,1:10], digits=6 ) @ % Note that when we split the follow-up each person's follow up now consists of many records, each with the \emph{current} values of the timescales at the start of the interval represented by the record. In the modelling we must necessarily assume that the rates are constant within each 6-month interval, but the \emph{size} of these rates we model as smooth functions of the time scales (that is the values at the beginning of each interval). The approach often used in epidemiology where one parameter is attached to each interval of time (or age) is not feasible when more than one time scale is used, because intervals are not classified the same way on all timescales. We shall use natural splines (restricted cubic splines) for the analysis of rates, and hence we must allocate knots for the splines. This is done for each of the time-scales, and separately for the transition out of states ``DM'' and ``Ins''. For age, we place the knots so that the number of events is the same between each pair of knots, but only half of this beyond each of the boundary knots, whereas for the timescales \texttt{DMdur} and \texttt{tIns} where we have observation from a well-defined 0, we put knots at 0 and place the remaining knots so that the number of events is the same between them and beyond the last: <>= nk <- 5 ( ai.kn <- with( subset(Si,lex.Xst=="Ins"), quantile( Age+lex.dur , probs=(1:nk-0.5)/nk ) ) ) ( ad.kn <- with( subset(Si,lex.Xst=="Dead"), quantile( Age+lex.dur , probs=(1:nk-0.5)/nk ) ) ) ( di.kn <- with( subset(Si,lex.Xst=="Ins"), c(0,quantile( DMdur+lex.dur, probs=(1:(nk-1))/nk ) )) ) ( dd.kn <- with( subset(Si,lex.Xst=="Dead"), c(0,quantile( DMdur+lex.dur, probs=(1:(nk-1))/nk ) )) ) ( ti.kn <- with( subset(Si,lex.Xst=="Dead(Ins)"), c(0,quantile( t.Ins+lex.dur, probs=(1:(nk-1))/nk ) )) ) @ % We then fit Poisson models to transition rates, using the wrapper \texttt{Ns} from the \texttt{Epi} package to simplify the specification of the rates: <>= library( splines ) DM.Ins <- glm( (lex.Xst=="Ins") ~ Ns( Age , knots=ai.kn ) + Ns( DMdur, knots=di.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="DM") ) DM.Dead <- glm( (lex.Xst=="Dead") ~ Ns( Age , knots=ad.kn ) + Ns( DMdur, knots=dd.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="DM") ) Ins.Dead <- glm( (lex.Xst=="Dead(Ins)") ~ Ns( Age , knots=ad.kn ) + Ns( DMdur, knots=dd.kn ) + Ns( t.Ins, knots=ti.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="Ins") ) @ % Note the similarity of the code used to fit the three models, is is mainly redefining the response variable (``to'' state) and the subset of the data used (``from'' state). \section{The mortality rates} This section discusses in some detail how to extract ad display the mortality rates from the models fitted. But it is not necessary for understanding how to use \texttt{simLexis} in practice. \subsection{Proportionality of mortality rates} Note that we have fitted separate models for the three transitions, there is no assumption of proportionality between the mortality rates from \texttt{DM} and \texttt{Ins}. However, there is nothing that prevents us from testing this assumption; we can just fit a model for the mortality rates in the entire data frame \texttt{Si}, and compare the deviance from this with the sum of the deviances from the separate models: <>= with( Si, table(lex.Cst) ) All.Dead <- glm( (lex.Xst %in% c("Dead(Ins)","Dead")) ~ Ns( Age , knots=ad.kn ) + Ns( DMdur, knots=dd.kn ) + lex.Cst + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = Si ) round( ci.exp( All.Dead ), 3 ) @ From the parameter values we would in a simple setting just claim that start of insulin-treatment was associated with a slightly more than doubling of mortality. The model \texttt{All.dead} assumes that the age- and DM-duration effects on mortality in the ``DM'' and ``Ins'' states are the same, and moreover that there is no effect of insulin duration, but merely a mortality that is larger by a multiplicative constant not depending on insulin duration. The model \texttt{DM.Dead} has 8 parameters to describe the dependency on age and DM duration, the model \texttt{Ins.Dead} has 12 for the same plus the insulin duration (a natural spline with $k$ knots gives $k-1$ parameters, and we chose $k=5$ above). We can compare the fit of the proportional hazards model with the fit of the separate models for the two mortality rates, by adding up the deviances and d.f. from these: <>= what <- c("null.deviance","df.null","deviance","df.residual") ( rD <- unlist( DM.Dead[what] ) ) ( rI <- unlist( Ins.Dead[what] ) ) ( rA <- unlist( All.Dead[what] ) ) round( c( dd <- rA-(rI+rD), "pVal"=1-pchisq(dd[3],dd[4]+1) ), 3 ) @ % Thus we see there is a substantial non-proportionality of mortality rates from the two states; but a test provides no clue whatsoever to the particular \emph{shape} of the non-proportionality. To this end, we shall explore the predicted mortalities under the two models quantitatively in more detail. Note that the reason that there is a difference in the null deviances (and a difference of 1 in the null d.f.) is that the null deviance of \texttt{All.Dead} refer to a model with a single intercept, that is a model with constant and \emph{identical} mortality rates from the states ``DM'' and ``Ins'', whereas the null models for \texttt{DM.Dead} and \texttt{Ins.Dead} have constant but \emph{different} mortality rates from the states ``DM'' and ``Ins''. This is however irrelevant for the comparison of the \emph{residual} deviances. \subsection{How the mortality rates look} If we want to see how the mortality rates are modelled in \texttt{DM.Dead} and \texttt{Ins.Dead} in relation to \texttt{All.Dead}, we make a prediction of rates for say men diagnosed in different ages and going on insulin at different times after this. So we consider men diagnosed in ages 40, 50, 60 and 70, and who either never enter insulin treatment or do it 0, 2 or 5 years after diagnosis of DM. To this end we create a prediction data frame where we have observation times from diagnosis and 12 years on (longer would not make sense as this is the extent of the data). But we start by setting up an array to hold the predicted mortality rates, classified by diabetes duration, age at diabetes onset, time of insulin onset, and of course type of model. What we want to do is to plot the age-specific mortality rates for persons not on insulin, and for persons starting insulin at different times after DM. The mortality curves start at the age where the person gets diabetes and continues 12 years; for persons on insulin they start at the age when they initiate insulin. <>= pr.rates <- NArray( list( DMdur = seq(0,12,0.1), DMage = 4:7*10, r.Ins = c(NA,0,2,5), model = c("DM/Ins","All"), what = c("rate","lo","hi") ) ) str( pr.rates ) @ % For convenience the \texttt{Epi} package contains a function that computes predicted (log-)rates with c.i. --- it is merely a wrapper for \texttt{predict.glm}: <<>>= ci.pred @ So set up the prediction data frame and modify it in loops over ages at onset and insulin onset. Note that we set \texttt{lex.dur} to 1000 in the prediction frame, so that we obtain rates in units of events per 1000 PY. <>= nd <- data.frame( DMdur = as.numeric( dimnames(pr.rates)[[1]] ), lex.Cst = factor( 1, levels=1:4, labels=levels(Si$lex.Cst) ), sex = factor( 1, levels=1:2, labels=c("M","F")), lex.dur = 1000 ) for( ia in dimnames(pr.rates)[[2]] ) { dnew <- transform( nd, Age = as.numeric(ia)+DMdur, Per = 1998+DMdur ) pr.rates[,ia,1,"DM/Ins",] <- ci.pred( DM.Dead, newdata = dnew ) pr.rates[,ia,1,"All" ,] <- ci.pred( All.Dead, newdata = dnew ) for( ii in dimnames(pr.rates)[[3]][-1] ) { dnew = transform( dnew, lex.Cst = factor( 2, levels=1:4, labels=levels(Si$lex.Cst) ), t.Ins = ifelse( (DMdur-as.numeric(ii)) >= 0, DMdur-as.numeric(ii), NA ) ) pr.rates[,ia, ii ,"DM/Ins",] <- ci.pred( Ins.Dead, newdata = dnew ) pr.rates[,ia, ii ,"All" ,] <- ci.pred( All.Dead, newdata = dnew ) } } @ % $ So for each age at DM onset we make a plot of the mortality as function of current age both for those with no insulin treatment at those that start 1, 3 and 5 years after, thus 4 curves (with c.i.). These curves are replicated with a different color for the simplified model. <>= par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6, las=1 ) plot( NA, xlim=c(40,82), ylim=c(5,300), bty="n", log="y", xlab="Age", ylab="Mortality rate per 1000 PY" ) abline( v=seq(40,80,5), h=outer(1:9,10^(0:2),"*"), col=gray(0.8) ) for( aa in 4:7*10 ) for( ii in 1:4 ) matlines( aa+as.numeric(dimnames(pr.rates)[[1]]), cbind( pr.rates[,paste(aa),ii,"DM/Ins",], pr.rates[,paste(aa),ii,"All" ,] ), type="l", lty=1, lwd=c(3,1,1), col=rep(c("red","limegreen"),each=3) ) @ % \insfig{mort-int}{0.9}{Estimated mortality rates for male diabetes patients with no insulin (lower sets of curves) and insulin (upper curves), with DM onset in age 40, 50, 60 and 70. The red curves are from the models with separate age effects for persons with and without insulin, and a separate effect of insulin duration. The green curves are from the model with common age-effects and only a time-dependent insulin effect (the classical time-dependent variable approach).} From figure \ref{fig:mort-int} we see that there is a substantial insulin-duration effect which is not accommodated by the simple model with only one time-dependent variable to describe the insulin effect. Note that the simple model (green curves) for those on insulin does not depend in insulin duration, and hence the mortality curves for those on insulin are just parallel to the mortality curves for those not on insulin, regardless of diabetes duration (or age) at the time of insulin initiation. This is the proportional hazards assumption. Thus the effect of insulin initiation is under-estimated for short duration of insulin and over-estimated for long duration of insulin. This is the major discrepancy between the two models, and illustrates the importance of being able to accommodate different time scales, but there is also a declining overall insulin effect by age which is not accommodated by the proportional hazards approach. Finally, this plot illustrates an important feature in reporting models with multiple timescales; all timescales must be represented in the predicted rates, only reporting the effect of one timescale, conditional on a fixed value of other timescales is misleading since all timescales by definition advance at the same pace. For example, the age-effect for a fixed value of insulin duration is a misnomer since it does not correspond to any real person's follow-up. \section{Input to the \texttt{simLexis} function} In order to simulate from the multistate model with the estimated transition rates, and get the follow-up of a hypothetical cohort, we must supply \emph{both} the transition rates and the structure of the model \emph{as well as} the initial cohort status to \texttt{simLexis}. \subsection{The transition object} We first put the models into an object representing the transitions; note this is a list of lists, the latter having \texttt{glm} objects as elements: <>= Tr <- list( "DM" = list( "Ins" = DM.Ins, "Dead" = DM.Dead ), "Ins" = list( "Dead(Ins)" = Ins.Dead ) ) @ % Now we have the description of the rates and of the structure of the model. The \texttt{Tr} object defines the states and models for all transitions between them; the object \verb|Tr$A$B| is the model for the transition intensity from state \texttt{A} to state \texttt{B}. \subsection{The initial cohort} We now define an initial \texttt{Lexis} object of persons with all relevant covariates defined. Note that we use \texttt{subset} to get a \texttt{Lexis} object, this conserves the \texttt{time.scale} and \texttt{time.since} attributes which is needed for the simulation (the usual ``\texttt{[}'' operator does not preserve these attributes when you select columns): <>= str( Si[NULL,1:9] ) ini <- subset(Si,FALSE,select=1:9) str( ini ) ini <- subset(Si,select=1:9)[NULL,] str( ini ) @ % We now have an empty \texttt{Lexis} object with attributes reflecting the timescales in multistate model we want to simulate, so we must now enter some data to represent the persons whose follow-up we want to simulate through the model; we set up an initial dataset with one man and one woman: <>= ini[1:2,"lex.id"] <- 1:2 ini[1:2,"lex.Cst"] <- "DM" ini[1:2,"Per"] <- 1995 ini[1:2,"Age"] <- 60 ini[1:2,"DMdur"] <- 5 ini[1:2,"sex"] <- c("M","F") ini @ % \section{Simulation of the follow-up} Now we simulate 5000 of each of these persons using the estimated model. The \texttt{t.range} argument gives the times range where the integrated intensities (cumulative rates) are evaluated and where linear interpolation is used when simulating transition times. Note that this must be given in the same units as \texttt{lex.dur} in the \texttt{Lexis} object used for fitting the models for the transitions. <>= Nsim <- 500 system.time( simL <- simLexis( Tr, ini, t.range = 12, N = Nsim ) ) @ % The result is a \texttt{Lexis} object --- a data frame representing the simulated follow-up of 10,000 persons (5000 identical men and 5000 identical women) according to the rates we estimated from the original dataset. <>= summary( simL, by="sex" ) @ % \subsection{Using other models for simulation} \subsubsection{Proportional hazards Poisson model} We fitted a proportional mortality model \texttt{All.Dead} (which fitted worse than the other two), this is a model for \emph{both} the transition from ``DM'' to ``Death'' \emph{and} from ``Ins'' to ``Dead(Ins)'', assuming that they are proportional. But it can easily be used in the simulation set-up, because the state is embedded in the model via the term \texttt{lex.Cst}, which is updated during the simulation. Simulation using this instead just requires that we supply a different transition object: <>= Tr.p <- list( "DM" = list( "Ins" = DM.Ins, "Dead" = All.Dead ), "Ins" = list( "Dead(Ins)" = All.Dead ) ) system.time( simP <- simLexis( Tr.p, ini, t.range = 12, N = Nsim ) ) summary( simP, by="sex" ) @ % \subsubsection{Proportional hazards Cox model} A third possibility would be to replace the two-time scale proportional mortality model by a one-time-scale Cox-model, using diabetes duration as time scale, and age at diagnosis of DM as (fixed) covariate: <>= library( survival ) Cox.Dead <- coxph( Surv( DMdur, DMdur+lex.dur, lex.Xst %in% c("Dead(Ins)","Dead")) ~ Ns( Age-DMdur, knots=ad.kn ) + I(lex.Cst=="Ins") + I(Per-2000) + sex, data = Si ) round( ci.exp( Cox.Dead ), 3 ) round( ci.exp( All.Dead ), 3 ) @ % Note that in order for this model to be usable for simulation, it is necessary that we use the components of the \texttt{Lexis} object to specify the survival. Each record in the data frame \texttt{Si} represents follow up from \texttt{DMdur} to \texttt{DMdur+lex.dur}, so the model is a Cox model with diabetes duration as underlying timescale and age at diagnosis, \texttt{Age-DMdur}, as covariate. Also note that we used \texttt{I(lex.Cst=="Ins")} instead of just \texttt{lex.Cst}, because \texttt{coxph} assigns design matrix columns to all levels of \texttt{lex.Cst}, also those not present in data, which would give \texttt{NA}s among the parameter estimates and \texttt{NA}s as mortality outcomes. We see that the effect of insulin and the other covariates are pretty much the same as in the two-time-scale model. We can simulate from this model too; there is no restrictions on what type of model can be used for different transitions <>= Tr.c <- list( "DM" = list( "Ins" = Tr$DM$Ins, "Dead" = Cox.Dead ), "Ins" = list( "Dead(Ins)" = Cox.Dead ) ) system.time( simC <- simLexis( Tr.c, ini, t.range = 12, N = Nsim ) ) summary( simC, by="sex" ) @ \section{Reporting the simulation results} We can now tabulate the number of persons in each state at a predefined set of times on a given time scale. Note that in order for this to be sensible, the \texttt{from} argument would normally be equal to the starting time for the simulated individuals. <>= system.time( nSt <- nState( subset(simL,sex=="M"), at=seq(0,11,0.2), from=1995, time.scale="Per" ) ) nSt[1:10,] @ % We see that as time goes by, the 5000 men slowly move away from the starting state (\texttt{DM}). Based on this table (\texttt{nSt} is a table) we can now compute the fractions in each state, or, rather more relevant, the cumulative fraction across the states in some specified order, so that a plot of the stacked probabilities can be made, using either the default rather colorful layout, or a more minimalistic version (both in figure \ref{fig:pstate0}): <>= pM <- pState( nSt, perm=c(1,2,4,3) ) head( pM ) par( mfrow=c(1,2), mar=c(3,3,1,1), mgp=c(3,1,0)/1.6 ) plot( pM ) plot( pM, border="black", col="transparent", lwd=3 ) text( rep(as.numeric(rownames(pM)[nrow(pM)-1]),ncol(pM)), pM[nrow(pM),]-diff(c(0,pM[nrow(pM),]))/5, colnames( pM ), adj=1 ) box( col="white", lwd=3 ) box() @ % \insfig{pstate0}{1.0}{Default layout of the \textrm{\tt plot.pState} graph (left), and a version with the state probabilites as lines and annotation of states.} A more useful set-up of the graph would include a more through annotation and sensible choice of colors, as seen in figure \ref{fig:pstatex}: <>= clr <- c("limegreen","orange") # expand with a lighter version of the two chosen colors clx <- c( clr, rgb( t( col2rgb( clr[2:1] )*2 + rep(255,3) ) / 3, max=255 ) ) par( mfrow=c(1,2), las=1, mar=c(3,3,4,2), mgp=c(3,1,0)/1.6 ) # Men plot( pM, col=clx ) lines( as.numeric(rownames(pM)), pM[,2], lwd=3 ) mtext( "60 year old male, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:19/20, labels=FALSE ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) # Women pF <- pState( nState( subset(simL,sex=="F"), at=seq(0,11,0.2), from=1995, time.scale="Per" ), perm=c(1,2,4,3) ) plot( pF, col=clx ) lines( as.numeric(rownames(pF)), pF[,2], lwd=3 ) mtext( "60 year old female, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:19/20, labels=FALSE ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) @ % \insfig{pstatex}{1.0}{\textrm{\tt plot.pState} graphs where persons ever on insulin are given in orange and persons never on insulin in green, and the overall survival (dead over the line) as a black line.} If we instead wanted to show the results on the age-scale, we would use age as timescale when constructing the probabilities; otherwise the code is pretty much the same as before (Figure \ref{fig:pstatey}): <>= par( mfrow=c(1,2), las=1, mar=c(3,3,4,2), mgp=c(3,1,0)/1.6 ) # Men pM <- pState( nState( subset(simL,sex=="M"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) plot( pM, col=clx, xlab="Age" ) lines( as.numeric(rownames(pM)), pM[,2], lwd=3 ) mtext( "60 year old male, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:19/20, labels=FALSE ) axis( side=4, at=1:19/20, labels=FALSE, tcl=-0.4 ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) # Women pF <- pState( nState( subset(simL,sex=="F"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) plot( pF, col=clx, xlab="Age" ) lines( as.numeric(rownames(pF)), pF[,2], lwd=3 ) mtext( "60 year old female, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:9/10, labels=FALSE ) axis( side=4, at=1:19/20, labels=FALSE, tcl=-0.4 ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) @ % Note the several statements with \texttt{axis(side=4,...}; they are nesessary to get the fine tick-marks in the right hand side of the plots that you will need in order to read off the probabilities at 2006 (or 71 years). \insfig{pstatey}{1.0}{\textrm{\tt plot.pState} graphs where persons ever on insulin are given in orange and persons never on insulin in green, and the overall survival (dead over the line) as a black line.} \subsection{Comparing predictions from different models} We have actually fitted different models for the transitions, and we have simulated Lexis objects from all three approaches, so we can plot these predictions on top of each other: <>= PrM <- pState( nState( subset(simP,sex=="M"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) PrF <- pState( nState( subset(simP,sex=="F"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) CoxM <- pState( nState( subset(simC,sex=="M"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) CoxF <- pState( nState( subset(simC,sex=="F"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) par( mfrow=c(1,2), mar=c(3,3,1,1), mgp=c(3,1,0)/1.6 ) plot( pM, border="black", col="transparent", lwd=3 ) lines( PrM, border="blue" , col="transparent", lwd=3 ) lines( CoxM, border="red" , col="transparent", lwd=3 ) text( 60.5, 0.05, "M" ) box( lwd=3 ) plot( pF, border="black", col="transparent", lwd=3 ) lines( PrF, border="blue" , col="transparent", lwd=3 ) lines( CoxF, border="red" , col="transparent", lwd=3 ) text( 60.5, 0.05, "F" ) box( lwd=3 ) @ % \insfig{comp-0}{1.0}{Comparison of the simulated state occupancy probabilities using separate Poisson models for the mortality rates with and without insulin (black) and using proportional hazards Poisson models (blue) and Cox-models with diabetes duration as timescale and age at diabetes diagnosis as covariate (red).} From figure \ref{fig:comp-0} it is clear that the two proportional hazards models (blue and red curves) produce pretty much the same estimates of the state occupancy probabilites over time, but also that they relative to the model with separately estimated transition intensities overestimates the probability of being alive without insulin and underestimates the probabilities of being dead without insulin. However both the overall survival, and the fraction of persons on insulin are quite well in agreement with the more elaborate model. Thus the proportional hazards models overestimate the relative mortality of the insulin treated diabetes patients relative to the non-insulin treated. Interestingly, we also see a bump in th estimated probabilities from the Cox-model based model, but this is entirely an artifact that comes from the estimation method for the baseline hazard of the Cox-model that lets the (cumulative) hazard have large jumps at event times where the risk set is small. So also here it shows up that an assumption of continuous underlying hazards leads to more credible estimates. \chapter{Simulation of transitions in multistate models} \section{Theory} Suppose that the rate functions for the transitions out of the current state to, say, 3 different states are $\lambda_1$, $\lambda_2$ and $\lambda_3$, and the corresponding cumulative rates are $\Lambda_1$, $\Lambda_2$ and $\Lambda_3$, and we want to simulate an exit time and an exit state (that is either 1, 2 or 3). This can be done in two slightly different ways: \begin{enumerate} \item First time, then state: \begin{enumerate} \item Compute the survival function, $S(t) = \exp\bigl(-\Lambda_1(t)-\Lambda_2(t)-\Lambda_3(t)\bigr)$ \item Simulate a random U(0,1) variate, $u$, say. \item The simulated exit time is then the solution $t_u$ to the equation $S(t_u) = u \quad \Leftrightarrow \quad \sum_j\Lambda_j(t_u) = -\log(u)$. \item A simulated transition at $t_u$ is then found by simulating a random draw from the multinomial distribution with probabilities $p_i=\lambda_i(t_u) / \sum_j\lambda_j(t_u)$. \end{enumerate} \item Separate cumulative incidences: \begin{enumerate} \item Simulate 3 independent U(0,1) random variates $u_1$, $u_2$ and $u_3$. \item Solve the equations $\Lambda_i(t_i)=-\log(u_i), i=1,2,3$ and get $(t_1,t_2,t_3)$. \item The simulated survival time is then $\min(t_1,t_2,t_3)$, and the simulated transition is to the state corresponding to this, that is $k \in \{1,2,3\}$, where $t_k=\min(t_1,t_2,t_3)$ \end{enumerate} \end{enumerate} The intuitive argument is that with three possible transition there are 3 independent processes running, but only the first transition is observed. The latter approach is used in the implementation in \texttt{simLexis}. The formal argument for the equality of the two approaches goes as follows: \begin{enumerate} \item Equality of the transition times: \begin{enumerate} \item In the first approach we simulate from a distribution with cumulative rate $\Lambda_1(t)+\Lambda_2(t)+\Lambda_3(t)$, hence from a distribution with survival function: \begin{align*} S(t) & = \exp\bigl(-(\Lambda_1(t)+\Lambda_2(t)+\Lambda_3(t))\bigr) \\ & = \exp\bigl(-\Lambda_1(t)\bigr)\times \exp\bigl(-\Lambda_2(t)\bigr)\times \exp\bigl(-\Lambda_3(t)\bigr) \end{align*} \item In the second approach we choose the smallest of three independent survival times, with survival functions $\exp(-\Lambda_i), i=1,2,3$. Now, the survival function for the minimum of three independent survival times is: \begin{align*} S_\text{min}(t) & = \pmat{\min(t_1,t_2,t_3)>t} \\ & = \pmat{t_1>t} \times \pmat{t_2>t} \times \pmat{t_3>t} \\ & = \exp\bigl(-\Lambda_1(t)\bigr)\times \exp\bigl(-\Lambda_2(t)\bigr)\times \exp\bigl(-\Lambda_3(t)\bigr) \end{align*} which is the same survival function as derived above. \end{enumerate} \item Type of transition: \begin{enumerate} \item In the first instance the probability of a transition to state $i$, conditional on the transition time being $t$, is as known from standard probability theory: $\lambda_i(t)/\bigl(\lambda_1(t)+\lambda_2(t)+\lambda_3(t)\bigr)$. \item In the second approach we choose the transition corresponding to the the smallest of the transition times. So when we condition on the event that a transition takes place at time $t$, we have to show that the conditional probability that the smallest of the three simulated transition times was actually the $i$th, is as above. But conditional on \emph{survival} till $t$, the probabilities that events of type $1,2,3$ takes place in the interval $(t,t+\dif t)$ are $\lambda_1(t)\dif t$, $\lambda_2(t)\dif t$ and $\lambda_1(t)\dif t$, respectively (assuming that the probability of more than one event in the interval of length $\dif t$ is 0). Hence the conditional probabilities \emph{given a transition time} in $(t,t+\dif t)$ is: \[ \frac{\lambda_i(t)\dif t}{\lambda_1(t)\dif t+\lambda_2(t)\dif t+\lambda_3(t)\dif t}= \frac{\lambda_i(t)}{\lambda_1(t)+\lambda_2(t)+\lambda_3(t)} \] --- exactly as above. \end{enumerate} \end{enumerate} \section{Components of \texttt{simLexis}} This section explains the actually existing code for \texttt{simLexis}, as it is in the current version of \texttt{Epi}. <>= options( keep.source=TRUE ) @ % The function \texttt{simLexis} takes a \texttt{Lexis} object as input. This defines the initial state(s) and times of the start for a number of persons. Since the purpose is to simulate a history through the estimated multistate model, the input values of the outcome variables \texttt{lex.Xst} and \texttt{lex.dur} are ignored --- the aim is to simulate values for them. Note however that the attribute \texttt{time.since} must be present in the object. This is used for initializing timescales defined as time since entry into a particular state, it is a character vector of the same length as the \texttt{time.scales} attribute, with value equal to a state name if the corresponding time scale is defined as time since entry into that state. In this example the 4th timescale is time since entry into the ``Ins'' state, and hence: <<>>= cbind( attr( ini, "time.scale" ), attr( ini, "time.since" ) ) @ % \texttt{Lexis} objects will have this attribute set for time scales created using \texttt{cutLexis}. The other necessary argument is a transition object \texttt{Tr}, which is a list of lists. The elements of the lists should be \texttt{glm} objects derived by fitting Poisson models to a \texttt{Lexis} object representing follow-up data in a multistate model. It is assumed (but not checked) that timescales enter in the model via the timescales of the \texttt{Lexis} object. Formally, there are no assumptions about how \texttt{lex.dur} enters in the model. Optional arguments are \texttt{t.range}, \texttt{n.int} or \texttt{time.pts}, specifying the times after entry at which the cumulative rates will be computed (the maximum of which will be taken as the censoring time), and \texttt{N} a scalar or numerical vector of the number of persons with a given initial state each record of the \texttt{init} object should represent. The central part of the functions uses a \texttt{do.call} / \texttt{lapply} / \texttt{split} construction to do simulations for different initial states. This is the construction in the middle that calls \texttt{simX}. \texttt{simLexis} also calls \texttt{get.next} which is further detailed below. <<>>= simLexis @ % \subsection{\texttt{simX}} \texttt{simX} is called by \texttt{simLexis} and simulates transition-times and -types for a set of patients assumed to be in the same state. It is called from \texttt{simLexis} with a data frame as argument, uses the state in \texttt{lex.Cst} to select the relevant component of \texttt{Tr} and compute predicted cumulative intensities for all states reachable from this state. Note that it is here the switch between \texttt{glm}, \texttt{coxph} and objects of class \texttt{function} is made. The dataset on which this prediction is done has \texttt{length(time.pts)} rows per person. <<>>= Epi:::simX @ % As we see, \texttt{simX} calls \texttt{sim1} which simulates the transition for one person. \subsection{\texttt{sim1}} The predicted cumulative intensities are fed, person by person, to \texttt{sim1} --- again via a \texttt{do.call} / \texttt{lapply} / \texttt{split} construction --- and the resulting time and state is appended to the \texttt{nd} data frame. This way we have simulated \emph{one} transition (time and state) for each person: <<>>= Epi:::sim1 @ % The \texttt{sim1} function uses \texttt{lint} to do linear interpolation. \subsection{\texttt{lint}} We do not use \texttt{approx} to do the linear interpolation, because this function does not do the right thing if the cumulative incidences (\texttt{ci}) are constant across a number of times. Therefore we have a custom made linear interpolator that does the interpolation exploiting the fact the the \texttt{ci} is non-decreasing and \texttt{tt} is strictly monotonously increasing: <<>>= Epi:::lint @ \subsection{\texttt{get.next}} We must repeat the simulation operation on those that have a simulated entry to a transient state, and also make sure that any time scales defined as time since entry to one of these states be initialized to 0 before a call to \texttt{simX} is made for these persons. This accomplished by the function \texttt{get.next}: <<>>= Epi:::get.next @ \subsection{\texttt{chop.lex}} The operation so far has censored individuals \texttt{max(time.pts)} after \emph{each} new entry to a transient state. In order to groom the output data we use \texttt{chop.lex} to censor all persons at the same designated time after \emph{initial} entry. <<>>= Epi:::chop.lex @ \section{Probabilities from simulated \texttt{Lexis} objects} Once we have simulated a Lexis object we will of course want to use it for estimating probabilities, so basically we will want to enumerate the number of persons in each state at a pre-specified set of time points. \subsection{\texttt{nState}} Since we are dealing with multistate model with potentially multiple time scales, it is necessary to define the timescale (\texttt{time.scale}), the starting point on this timescale (\texttt{from}) and the points after this where we compute the number of occupants in each state, (\texttt{at}). <<>>= nState @ % \subsection{\texttt{pState}, \texttt{plot.pState} and \texttt{lines.pState}} In order to plot probabilities of state-occupancy it is useful to compute cumulative probabilities across states in any given order; this is done by the function \texttt{pState}, which returns a matrix of class \texttt{pState}: <<>>= pState @ % There is also a \texttt{plot} and \texttt{lines} method for the resulting \texttt{pState} objects: <<>>= plot.pState lines.pState @ % \bibliographystyle{plain} \begin{thebibliography}{1} \bibitem{Carstensen.2011a} B.~Carstensen and M.~Plummer. \newblock Using {L}exis objects for multi-state models in {R}. \newblock {\em Journal of Statistical Software}, 38(6):1--18, 1 2011. \bibitem{Iacobelli.2013} S.~Iacobelli and B.~Carstensen. \newblock {{M}ultiple time scales in multi-state models}. \newblock {\em Stat Med}, 32(30):5315--5327, Dec 2013. \bibitem{Plummer.2011} M.~Plummer and B.~Carstensen. \newblock Lexis: An {R} class for epidemiological studies with long-term follow-up. \newblock {\em Journal of Statistical Software}, 38(5):1--12, 1 2011. \end{thebibliography} \addcontentsline{toc}{chapter}{References} \end{document} Epi/inst/doc/sim-Lexis.R0000644000175100001440000004122012531361114014520 0ustar hornikusers### R code from vignette source 'sim-Lexis.rnw' ### Encoding: ISO8859-1 ################################################### ### code chunk number 1: start ################################################### options( width=90 ) library( Epi ) print( sessionInfo(), l=F ) ################################################### ### code chunk number 2: Lexis ################################################### data(DMlate) dml <- Lexis( entry = list(Per=dodm, Age=dodm-dobth, DMdur=0 ), exit = list(Per=dox), exit.status = factor(!is.na(dodth),labels=c("DM","Dead")), data = DMlate ) ################################################### ### code chunk number 3: cut ################################################### dmi <- cutLexis( dml, cut = dml$doins, pre = "DM", new.state = "Ins", new.scale = "t.Ins", split.states = TRUE ) summary( dmi ) str(dmi) ################################################### ### code chunk number 4: boxes ################################################### boxes( dmi, boxpos = list(x=c(20,20,80,80), y=c(80,20,80,20)), scale.R = 1000, show.BE = TRUE ) ################################################### ### code chunk number 5: split ################################################### Si <- splitLexis( dmi, 0:30/2, "DMdur" ) dim( Si ) print( subset( Si, lex.id==97 )[,1:10], digits=6 ) ################################################### ### code chunk number 6: knots ################################################### nk <- 5 ( ai.kn <- with( subset(Si,lex.Xst=="Ins"), quantile( Age+lex.dur , probs=(1:nk-0.5)/nk ) ) ) ( ad.kn <- with( subset(Si,lex.Xst=="Dead"), quantile( Age+lex.dur , probs=(1:nk-0.5)/nk ) ) ) ( di.kn <- with( subset(Si,lex.Xst=="Ins"), c(0,quantile( DMdur+lex.dur, probs=(1:(nk-1))/nk ) )) ) ( dd.kn <- with( subset(Si,lex.Xst=="Dead"), c(0,quantile( DMdur+lex.dur, probs=(1:(nk-1))/nk ) )) ) ( ti.kn <- with( subset(Si,lex.Xst=="Dead(Ins)"), c(0,quantile( t.Ins+lex.dur, probs=(1:(nk-1))/nk ) )) ) ################################################### ### code chunk number 7: Poisson ################################################### library( splines ) DM.Ins <- glm( (lex.Xst=="Ins") ~ Ns( Age , knots=ai.kn ) + Ns( DMdur, knots=di.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="DM") ) DM.Dead <- glm( (lex.Xst=="Dead") ~ Ns( Age , knots=ad.kn ) + Ns( DMdur, knots=dd.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="DM") ) Ins.Dead <- glm( (lex.Xst=="Dead(Ins)") ~ Ns( Age , knots=ad.kn ) + Ns( DMdur, knots=dd.kn ) + Ns( t.Ins, knots=ti.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="Ins") ) ################################################### ### code chunk number 8: prop-haz ################################################### with( Si, table(lex.Cst) ) All.Dead <- glm( (lex.Xst %in% c("Dead(Ins)","Dead")) ~ Ns( Age , knots=ad.kn ) + Ns( DMdur, knots=dd.kn ) + lex.Cst + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = Si ) round( ci.exp( All.Dead ), 3 ) ################################################### ### code chunk number 9: get-dev ################################################### what <- c("null.deviance","df.null","deviance","df.residual") ( rD <- unlist( DM.Dead[what] ) ) ( rI <- unlist( Ins.Dead[what] ) ) ( rA <- unlist( All.Dead[what] ) ) round( c( dd <- rA-(rI+rD), "pVal"=1-pchisq(dd[3],dd[4]+1) ), 3 ) ################################################### ### code chunk number 10: pr-array ################################################### pr.rates <- NArray( list( DMdur = seq(0,12,0.1), DMage = 4:7*10, r.Ins = c(NA,0,2,5), model = c("DM/Ins","All"), what = c("rate","lo","hi") ) ) str( pr.rates ) ################################################### ### code chunk number 11: sim-Lexis.rnw:481-482 ################################################### ci.pred ################################################### ### code chunk number 12: make-pred ################################################### nd <- data.frame( DMdur = as.numeric( dimnames(pr.rates)[[1]] ), lex.Cst = factor( 1, levels=1:4, labels=levels(Si$lex.Cst) ), sex = factor( 1, levels=1:2, labels=c("M","F")), lex.dur = 1000 ) for( ia in dimnames(pr.rates)[[2]] ) { dnew <- transform( nd, Age = as.numeric(ia)+DMdur, Per = 1998+DMdur ) pr.rates[,ia,1,"DM/Ins",] <- ci.pred( DM.Dead, newdata = dnew ) pr.rates[,ia,1,"All" ,] <- ci.pred( All.Dead, newdata = dnew ) for( ii in dimnames(pr.rates)[[3]][-1] ) { dnew = transform( dnew, lex.Cst = factor( 2, levels=1:4, labels=levels(Si$lex.Cst) ), t.Ins = ifelse( (DMdur-as.numeric(ii)) >= 0, DMdur-as.numeric(ii), NA ) ) pr.rates[,ia, ii ,"DM/Ins",] <- ci.pred( Ins.Dead, newdata = dnew ) pr.rates[,ia, ii ,"All" ,] <- ci.pred( All.Dead, newdata = dnew ) } } ################################################### ### code chunk number 13: mort-int ################################################### par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6, las=1 ) plot( NA, xlim=c(40,82), ylim=c(5,300), bty="n", log="y", xlab="Age", ylab="Mortality rate per 1000 PY" ) abline( v=seq(40,80,5), h=outer(1:9,10^(0:2),"*"), col=gray(0.8) ) for( aa in 4:7*10 ) for( ii in 1:4 ) matlines( aa+as.numeric(dimnames(pr.rates)[[1]]), cbind( pr.rates[,paste(aa),ii,"DM/Ins",], pr.rates[,paste(aa),ii,"All" ,] ), type="l", lty=1, lwd=c(3,1,1), col=rep(c("red","limegreen"),each=3) ) ################################################### ### code chunk number 14: Tr ################################################### Tr <- list( "DM" = list( "Ins" = DM.Ins, "Dead" = DM.Dead ), "Ins" = list( "Dead(Ins)" = Ins.Dead ) ) ################################################### ### code chunk number 15: make-ini ################################################### str( Si[NULL,1:9] ) ini <- subset(Si,FALSE,select=1:9) str( ini ) ini <- subset(Si,select=1:9)[NULL,] str( ini ) ################################################### ### code chunk number 16: ini-fill ################################################### ini[1:2,"lex.id"] <- 1:2 ini[1:2,"lex.Cst"] <- "DM" ini[1:2,"Per"] <- 1995 ini[1:2,"Age"] <- 60 ini[1:2,"DMdur"] <- 5 ini[1:2,"sex"] <- c("M","F") ini ################################################### ### code chunk number 17: simL ################################################### Nsim <- 5000 system.time( simL <- simLexis( Tr, ini, t.range = 12, N = Nsim ) ) ################################################### ### code chunk number 18: sum-simL ################################################### summary( simL, by="sex" ) ################################################### ### code chunk number 19: Tr.p-simP ################################################### Tr.p <- list( "DM" = list( "Ins" = DM.Ins, "Dead" = All.Dead ), "Ins" = list( "Dead(Ins)" = All.Dead ) ) system.time( simP <- simLexis( Tr.p, ini, t.range = 12, N = Nsim ) ) summary( simP, by="sex" ) ################################################### ### code chunk number 20: Cox-dur ################################################### library( survival ) Cox.Dead <- coxph( Surv( DMdur, DMdur+lex.dur, lex.Xst %in% c("Dead(Ins)","Dead")) ~ Ns( Age-DMdur, knots=ad.kn ) + I(lex.Cst=="Ins") + I(Per-2000) + sex, data = Si ) round( ci.exp( Cox.Dead ), 3 ) round( ci.exp( All.Dead ), 3 ) ################################################### ### code chunk number 21: TR.c ################################################### Tr.c <- list( "DM" = list( "Ins" = Tr$DM$Ins, "Dead" = Cox.Dead ), "Ins" = list( "Dead(Ins)" = Cox.Dead ) ) system.time( simC <- simLexis( Tr.c, ini, t.range = 12, N = Nsim ) ) summary( simC, by="sex" ) ################################################### ### code chunk number 22: nState ################################################### system.time( nSt <- nState( subset(simL,sex=="M"), at=seq(0,11,0.2), from=1995, time.scale="Per" ) ) nSt[1:10,] ################################################### ### code chunk number 23: pstate0 ################################################### pM <- pState( nSt, perm=c(1,2,4,3) ) head( pM ) par( mfrow=c(1,2), mar=c(3,3,1,1), mgp=c(3,1,0)/1.6 ) plot( pM ) plot( pM, border="black", col="transparent", lwd=3 ) text( rep(as.numeric(rownames(pM)[nrow(pM)-1]),ncol(pM)), pM[nrow(pM),]-diff(c(0,pM[nrow(pM),]))/5, colnames( pM ), adj=1 ) box( col="white", lwd=3 ) box() ################################################### ### code chunk number 24: pstatex ################################################### clr <- c("limegreen","orange") # expand with a lighter version of the two chosen colors clx <- c( clr, rgb( t( col2rgb( clr[2:1] )*2 + rep(255,3) ) / 3, max=255 ) ) par( mfrow=c(1,2), las=1, mar=c(3,3,4,2), mgp=c(3,1,0)/1.6 ) # Men plot( pM, col=clx ) lines( as.numeric(rownames(pM)), pM[,2], lwd=3 ) mtext( "60 year old male, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:19/20, labels=FALSE ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) # Women pF <- pState( nState( subset(simL,sex=="F"), at=seq(0,11,0.2), from=1995, time.scale="Per" ), perm=c(1,2,4,3) ) plot( pF, col=clx ) lines( as.numeric(rownames(pF)), pF[,2], lwd=3 ) mtext( "60 year old female, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:19/20, labels=FALSE ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) ################################################### ### code chunk number 25: pstatey ################################################### par( mfrow=c(1,2), las=1, mar=c(3,3,4,2), mgp=c(3,1,0)/1.6 ) # Men pM <- pState( nState( subset(simL,sex=="M"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) plot( pM, col=clx, xlab="Age" ) lines( as.numeric(rownames(pM)), pM[,2], lwd=3 ) mtext( "60 year old male, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:19/20, labels=FALSE ) axis( side=4, at=1:19/20, labels=FALSE, tcl=-0.4 ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) # Women pF <- pState( nState( subset(simL,sex=="F"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) plot( pF, col=clx, xlab="Age" ) lines( as.numeric(rownames(pF)), pF[,2], lwd=3 ) mtext( "60 year old female, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:9/10, labels=FALSE ) axis( side=4, at=1:19/20, labels=FALSE, tcl=-0.4 ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) ################################################### ### code chunk number 26: comp-0 ################################################### PrM <- pState( nState( subset(simP,sex=="M"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) PrF <- pState( nState( subset(simP,sex=="F"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) CoxM <- pState( nState( subset(simC,sex=="M"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) CoxF <- pState( nState( subset(simC,sex=="F"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) par( mfrow=c(1,2), mar=c(3,3,1,1), mgp=c(3,1,0)/1.6 ) plot( pM, border="black", col="transparent", lwd=3 ) lines( PrM, border="blue" , col="transparent", lwd=3 ) lines( CoxM, border="red" , col="transparent", lwd=3 ) text( 60.5, 0.05, "M" ) box( lwd=3 ) plot( pF, border="black", col="transparent", lwd=3 ) lines( PrF, border="blue" , col="transparent", lwd=3 ) lines( CoxF, border="red" , col="transparent", lwd=3 ) text( 60.5, 0.05, "F" ) box( lwd=3 ) ################################################### ### code chunk number 27: sim-Lexis.rnw:987-988 ################################################### options( keep.source=TRUE ) ################################################### ### code chunk number 28: sim-Lexis.rnw:1004-1007 ################################################### cbind( attr( ini, "time.scale" ), attr( ini, "time.since" ) ) ################################################### ### code chunk number 29: sim-Lexis.rnw:1032-1033 ################################################### simLexis ################################################### ### code chunk number 30: sim-Lexis.rnw:1050-1051 ################################################### Epi:::simX ################################################### ### code chunk number 31: sim-Lexis.rnw:1063-1064 ################################################### Epi:::sim1 ################################################### ### code chunk number 32: sim-Lexis.rnw:1076-1077 ################################################### Epi:::lint ################################################### ### code chunk number 33: sim-Lexis.rnw:1087-1088 ################################################### Epi:::get.next ################################################### ### code chunk number 34: sim-Lexis.rnw:1097-1098 ################################################### Epi:::chop.lex ################################################### ### code chunk number 35: sim-Lexis.rnw:1115-1116 ################################################### nState ################################################### ### code chunk number 36: sim-Lexis.rnw:1125-1126 ################################################### pState ################################################### ### code chunk number 37: sim-Lexis.rnw:1130-1132 ################################################### plot.pState lines.pState Epi/inst/doc/sim-Lexis.pdf0000644000175100001440000126641412576452270015124 0ustar hornikusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 4303 /Filter /FlateDecode /N 97 /First 781 >> stream x\Ysɑ~_Qo&wB]#x#J.Rm=@Sl 88f$ݸD!uuVYGB TE`9fUY J#sCI' P%#AV(8/a`0N *`R Г籉s8f _3`7^wFxUt-LM `kwoORdrgAvӸd-zWQUx|KTy)ӝRe9yHY/" 7^M"&g +b.~8ʩLAf9X $:7iF$|7>Sei8θ8sÏ~RΈ!!Ÿ;*&x֩'Y({1Y 40pjbirb/N.̽LYl4S sg27pe6],;hey}=f{ݛݤer?Q֕9u3 6 L!0<иf-QGPl38Gv33Ay NӫZaWAL8 yw#DWXh$\|n;N8UDECuc"v3l2l4?>4]dZUꭼE؍( \kh,.,^n'>PFXqrUsIL"6C2U;r`Bk#^)3 x`=N8hElkyBlus"RdfMmxÊVV#| :I?">' _\O~aܾ=:ǣ-]FH3y*!g~<_}8}}Ng,2,.sVo//a6ˑ#nizvHIQx\W4]*^rKZ}|ON>}>||v3}O\PgtOkɗo>4ftV&#y󏯮I>Y.up4*:P>L 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Such a dataframe is called a \texttt{Lexis} object. The tools for handling follow-up data then use the structure of this for special plots, tabulations etc. Follow-up data basically consists of a time of entry, a time of exit and an indication of the status at exit (normally either ``alive'' or ``dead''). Implicitly is also assumed a status \emph{during} the follow-up (usually ``alive''). \begin{figure}[htbp] \centering \setlength{\unitlength}{1pt} \begin{picture}(210,70)(0,75) %\scriptsize \thicklines \put( 0,80){\makebox(0,0)[r]{Age-scale}} \put( 50,80){\line(1,0){150}} \put( 50,80){\line(0,1){5}} \put(100,80){\line(0,1){5}} \put(150,80){\line(0,1){5}} \put(200,80){\line(0,1){5}} \put( 50,77){\makebox(0,0)[t]{35}} \put(100,77){\makebox(0,0)[t]{40}} \put(150,77){\makebox(0,0)[t]{45}} \put(200,77){\makebox(0,0)[t]{50}} \put( 0,115){\makebox(0,0)[r]{Follow-up}} \put( 80,105){\makebox(0,0)[r]{\small Two}} \put( 90,105){\line(1,0){87}} \put( 90,100){\line(0,1){10}} \put(100,100){\line(0,1){10}} \put(150,100){\line(0,1){10}} \put(180,105){\circle{6}} \put( 95,110){\makebox(0,0)[b]{1}} \put(125,110){\makebox(0,0)[b]{5}} \put(165,110){\makebox(0,0)[b]{3}} \put( 50,130){\makebox(0,0)[r]{\small One}} \put( 60,130){\line(1,0){70}} \put( 60,125){\line(0,1){10}} \put(100,125){\line(0,1){10}} \put(130,130){\circle*{6}} \put( 80,135){\makebox(0,0)[b]{4}} \put(115,135){\makebox(0,0)[b]{3}} \end{picture} \caption{\it Follow-up of two persons} \label{fig:fu2} \end{figure} \section{Timescales} A timescale is a variable that varies deterministically \emph{within} each person during follow-up, \textit{e.g.}: \begin{itemize} \item Age \item Calendar time \item Time since treatment \item Time since relapse \end{itemize} All timescales advance at the same pace, so the time followed is the same on all timescales. Therefore, it suffices to use only the entry point on each of the time scale, for example: \begin{itemize} \item Age at entry. \item Date of entry. \item Time since treatment (\emph{at} treatment this is 0). \item Time since relapse (\emph{at} relapse this is 0).. \end{itemize} In the \texttt{Epi} package, follow-up in a cohort is represented in a \texttt{Lexis} object. A \texttt{Lexis} object is a dataframe with a bit of extra structure representing the follow-up. For the \texttt{nickel} data we would construct a \texttt{Lexis} object by: <<>>= data( nickel ) nicL <- Lexis( entry = list( per=agein+dob, age=agein, tfh=agein-age1st ), exit = list( age=ageout ), exit.status = ( icd %in% c(162,163) )*1, data = nickel ) @ The \texttt{entry} argument is a \emph{named} list with the entry points on each of the timescales we want to use. It defines the names of the timescales and the entry points. The \texttt{exit} argument gives the exit time on \emph{one} of the timescales, so the name of the element in this list must match one of the neames of the \texttt{entry} list. This is sufficient, because the follow-up time on all time scales is the same, in this case \texttt{ageout - agein}. Now take a look at the result: <<>>= str( nickel ) str( nicL ) head( nicL ) @ The \texttt{Lexis} object \texttt{nicL} has a variable for each timescale which is the entry point on this timescale. The follow-up time is in the variable \texttt{lex.dur} (\textbf{dur}ation). There is a \texttt{summary} function for \texttt{Lexis} objects that list the numer of transitions and records as well as the total follow-up time: <<>>= summary( nicL ) @ We defined the exit status to be death from lung cancer (ICD7 162,163), i.e. this variable is 1 if follow-up ended with a death from this cause. If follow-up ended alive or by death from another cause, the exit status is coded 0, i.e. as a censoring. Note that the exit status is in the variable \texttt{lex.Xst} (e\textbf{X}it \textbf{st}atus. The variable \texttt{lex.Cst} is the state where the follow-up takes place (\textbf{C}urrent \textbf{st}atus), in this case 0 (alive). It is possible to get a visualization of the follow-up along the timescales chosen by using the \texttt{plot} method for \texttt{Lexis} objects. \texttt{nicL} is an object of \emph{class} \texttt{Lexis}, so using the function \texttt{plot()} on it means that \R\ will look for the function \texttt{plot.Lexis} and use this function. <>= plot( nicL ) @ The function allows a lot of control over the output, and a \texttt{points.Lexis} function allows plotting of the endpoints of follow-up: <>= par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6 ) plot( nicL, 1:2, lwd=1, col=c("blue","red")[(nicL$exp>0)+1], grid=TRUE, lty.grid=1, col.grid=gray(0.7), xlim=1900+c(0,90), xaxs="i", ylim= 10+c(0,90), yaxs="i", las=1 ) points( nicL, 1:2, pch=c(NA,3)[nicL$lex.Xst+1], col="lightgray", lwd=3, cex=1.5 ) points( nicL, 1:2, pch=c(NA,3)[nicL$lex.Xst+1], col=c("blue","red")[(nicL$exp>0)+1], lwd=1, cex=1.5 ) @ The results of these two plotting commands are in figure \ref{fig:Lexis-diagram}. \begin{figure}[tb] \centering \label{fig:Lexis-diagram} \includegraphics[width=0.39\textwidth]{Follow-up-nicL1} \includegraphics[width=0.59\textwidth]{Follow-up-nicL2} \caption{\it Lexis diagram of the \texttt{nickel} dataset, left panel the default version, the right one with bells and whistles. The red lines are for persons with exposure$>0$, so it is pretty evident that the oldest ones are the exposed part of the cohort.} \end{figure} \section{Splitting the follow-up time along a timescale} The follow-up time in a cohort can be subdivided by for example current age. This is achieved by the \texttt{splitLexis} (note that it is \emph{not} called \texttt{split.Lexis}). This requires that the timescale and the breakpoints on this timescale are supplied. Try: <<>>= nicS1 <- splitLexis( nicL, "age", breaks=seq(0,100,10) ) summary( nicL ) summary( nicS1 ) @ So we see that the number of events and the amount of follow-up is the same in the two datasets; only the number of records differ. To see how records are split for each individual, it is useful to list the results for a few individuals: <<>>= round( subset( nicS1, id %in% 8:10 ), 2 ) @ The resulting object, \texttt{nicS1}, is again a \texttt{Lexis} object, and so follow-up may be split further along another timescale. Try this and list the results for individuals 8, 9 and 10 again: <<>>= nicS2 <- splitLexis( nicS1, "tfh", breaks=c(0,1,5,10,20,30,100) ) round( subset( nicS2, id %in% 8:10 ), 2 ) @ If we want to model the effect of these timescales we will for each interval use either the value of the left endpoint in each interval or the middle. There is a function \texttt{timeBand} which returns these. Try: <<>>= timeBand( nicS2, "age", "middle" )[1:20] # For nice printing and column labelling use the data.frame() function: data.frame( nicS2[,c("id","lex.id","per","age","tfh","lex.dur")], mid.age=timeBand( nicS2, "age", "middle" ), mid.tfh=timeBand( nicS2, "tfh", "middle" ) )[1:20,] @ Note that these are the midpoints of the intervals defined by \texttt{breaks=}, \emph{not} the midpoints of the actual follow-up intervals. This is because the variable to be used in modelling must be independent of the consoring and mortality pattern --- it should only depend on the chosen grouping of the timescale. \section{Splitting time at a specific date} If we have a recording of the date of a specific event as for example recovery or relapse, we may classify follow-up time as being before of after this intermediate event. This is achieved with the function \texttt{cutLexis}, which takes three arguments: the time point, the timescale, and the value of the (new) state following the date. Now we define the age for the nickel vorkers where the cumulative exposure exceeds 50 exposure years: <<>>= subset( nicL, id %in% 8:10 ) agehi <- nicL$age1st + 50 / nicL$exposure nicC <- cutLexis( data=nicL, cut=agehi, timescale="age", new.state=2, precursor.states=0 ) subset( nicC, id %in% 8:10 ) @ (The \texttt{precursor.states=} argument is explained below). Note that individual 6 has had his follow-up split at age 25 where 50 exposure-years were attained. This could also have been achieved in the split dataset \texttt{nicS2} instead of \texttt{nicL}, try: <<>>= subset( nicS2, id %in% 8:10 ) agehi <- nicS2$age1st + 50 / nicS2$exposure nicS2C <- cutLexis( data=nicS2, cut=agehi, timescale="age", new.state=2, precursor.states=0 ) subset( nicS2C, id %in% 8:10 ) @ Note that follow-up subsequent to the event is classified as being in state 2, but that the final transition to state 1 (death from lung cancer) is preserved. This is the point of the \texttt{precursor.states=} argument. It names the states (in this case 0, ``Alive'') that will be over-witten by \texttt{new.state} (in this case state 2, ``High exposure''). Clearly, state 1 (``Dead'') should not be updated even if it is after the time where the persons moves to state 2. In other words, only state 0 is a precursor to state 2, state 1 is always subsequent to state 2. Note if the intermediate event is to be used as a time-dependent variable in a Cox-model, then \texttt{lex.Cst} should be used as the time-dependent variable, and \texttt{lex.Xst==1} as the event. \section{Competing risks --- multiple types of events} If we want to consider death from lung cancer and death from other causes as separate events we can code these as for example 1 and 2. <<>>= data( nickel ) nicL <- Lexis( entry = list( per=agein+dob, age=agein, tfh=agein-age1st ), exit = list( age=ageout ), exit.status = ( icd > 0 ) + ( icd %in% c(162,163) ), data = nickel ) summary( nicL ) subset( nicL, id %in% 8:10 ) @ If we want to label the states, we can enter the names of these in the \texttt{states} parameter, try for example: <<>>= nicL <- Lexis( entry = list( per=agein+dob, age=agein, tfh=agein-age1st ), exit = list( age=ageout ), exit.status = ( icd > 0 ) + ( icd %in% c(162,163) ), data = nickel, states = c("Alive","D.oth","D.lung") ) summary( nicL ) @ Note that the \texttt{Lexis} function automatically assumes that all persons enter in the first level (given in the \texttt{states=} argument) When we cut at a date as in this case, the date where cumulative exposure exceeds 50 exposure-years, we get the follow-up \emph{after} the date classified as being in the new state if the exit (\texttt{lex.Xst}) was to a state we defined as one of the \texttt{precursor.states}: <<>>= nicL$agehi <- nicL$age1st + 50 / nicL$exposure nicC <- cutLexis( data = nicL, cut = nicL$agehi, timescale = "age", new.state = "HiExp", precursor.states = "Alive" ) subset( nicC, id %in% 8:10 ) summary( nicC, scale=1000 ) @ Note that the persons-years is the same, but that the number of events has changed. This is because events are now defined as any transition from alive, including the transitions to \texttt{HiExp}. Also note that (so far) it is necessary to specify the variable with the cutpoints in full, using only \texttt{cut=agehi} would give an error. \subsection{Subdivision of existing states} It may be of interest to subdivide the states following the intermediate event according to wheter the event has occurred or not. That is done by the argument \texttt{split.states=TRUE}. Moreover, it will also often be of interest to introduce a new timescale indicating the time since intermediate event. This can be done by the argument \texttt{new.scale=TRUE}, alternatively \texttt{new.scale="tfevent"}, as illustrated here: <<>>= nicC <- cutLexis( data = nicL, cut = nicL$agehi, timescale = "age", new.state = "Hi", split.states=TRUE, new.scale=TRUE, precursor.states = "Alive" ) subset( nicC, id %in% 8:10 ) summary( nicC, scale=1000 ) @ \section{Multiple events of the same type (recurrent events)} Sometimes more events of the same type are recorded for each person and one would then like to count these and put follow-up time in states accordingly. Essentially, each set of cutpoints represents progressions from one state to the next. Therefore the states should be numbered, and the numbering of states subsequently occupied be increased accordingly. This is a behaviour different from the one outlined above, and it is achieved by the argument \texttt{count=TRUE} to \texttt{cutLexis}. When \texttt{count} is set to \texttt{TRUE}, the value of the arguments \texttt{new.state} and \texttt{precursor.states} are ignored. Actually, when using the argument \texttt{count=TRUE}, the function \texttt{countLexis} is called, so an alternative is to use this directly. \end{document} Epi/inst/doc/Follow-up.R0000644000175100001440000001276012531361114014541 0ustar hornikusers### R code from vignette source 'Follow-up.rnw' ################################################### ### code chunk number 1: Follow-up.rnw:65-67 ################################################### library(Epi) print( sessionInfo(), l=F ) ################################################### ### code chunk number 2: Follow-up.rnw:146-153 ################################################### data( nickel ) nicL <- Lexis( entry = list( per=agein+dob, age=agein, tfh=agein-age1st ), exit = list( age=ageout ), exit.status = ( icd %in% c(162,163) )*1, data = nickel ) ################################################### ### code chunk number 3: Follow-up.rnw:163-166 ################################################### str( nickel ) str( nicL ) head( nicL ) ################################################### ### code chunk number 4: Follow-up.rnw:175-176 ################################################### summary( nicL ) ################################################### ### code chunk number 5: nicL1 ################################################### plot( nicL ) ################################################### ### code chunk number 6: nicL2 ################################################### par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6 ) plot( nicL, 1:2, lwd=1, col=c("blue","red")[(nicL$exp>0)+1], grid=TRUE, lty.grid=1, col.grid=gray(0.7), xlim=1900+c(0,90), xaxs="i", ylim= 10+c(0,90), yaxs="i", las=1 ) points( nicL, 1:2, pch=c(NA,3)[nicL$lex.Xst+1], col="lightgray", lwd=3, cex=1.5 ) points( nicL, 1:2, pch=c(NA,3)[nicL$lex.Xst+1], col=c("blue","red")[(nicL$exp>0)+1], lwd=1, cex=1.5 ) ################################################### ### code chunk number 7: Follow-up.rnw:229-232 ################################################### nicS1 <- splitLexis( nicL, "age", breaks=seq(0,100,10) ) summary( nicL ) summary( nicS1 ) ################################################### ### code chunk number 8: Follow-up.rnw:239-240 ################################################### round( subset( nicS1, id %in% 8:10 ), 2 ) ################################################### ### code chunk number 9: Follow-up.rnw:245-247 ################################################### nicS2 <- splitLexis( nicS1, "tfh", breaks=c(0,1,5,10,20,30,100) ) round( subset( nicS2, id %in% 8:10 ), 2 ) ################################################### ### code chunk number 10: Follow-up.rnw:253-258 ################################################### timeBand( nicS2, "age", "middle" )[1:20] # For nice printing and column labelling use the data.frame() function: data.frame( nicS2[,c("id","lex.id","per","age","tfh","lex.dur")], mid.age=timeBand( nicS2, "age", "middle" ), mid.tfh=timeBand( nicS2, "tfh", "middle" ) )[1:20,] ################################################### ### code chunk number 11: Follow-up.rnw:276-281 ################################################### subset( nicL, id %in% 8:10 ) agehi <- nicL$age1st + 50 / nicL$exposure nicC <- cutLexis( data=nicL, cut=agehi, timescale="age", new.state=2, precursor.states=0 ) subset( nicC, id %in% 8:10 ) ################################################### ### code chunk number 12: Follow-up.rnw:287-292 ################################################### subset( nicS2, id %in% 8:10 ) agehi <- nicS2$age1st + 50 / nicS2$exposure nicS2C <- cutLexis( data=nicS2, cut=agehi, timescale="age", new.state=2, precursor.states=0 ) subset( nicS2C, id %in% 8:10 ) ################################################### ### code chunk number 13: Follow-up.rnw:312-321 ################################################### data( nickel ) nicL <- Lexis( entry = list( per=agein+dob, age=agein, tfh=agein-age1st ), exit = list( age=ageout ), exit.status = ( icd > 0 ) + ( icd %in% c(162,163) ), data = nickel ) summary( nicL ) subset( nicL, id %in% 8:10 ) ################################################### ### code chunk number 14: Follow-up.rnw:325-333 ################################################### nicL <- Lexis( entry = list( per=agein+dob, age=agein, tfh=agein-age1st ), exit = list( age=ageout ), exit.status = ( icd > 0 ) + ( icd %in% c(162,163) ), data = nickel, states = c("Alive","D.oth","D.lung") ) summary( nicL ) ################################################### ### code chunk number 15: Follow-up.rnw:345-353 ################################################### nicL$agehi <- nicL$age1st + 50 / nicL$exposure nicC <- cutLexis( data = nicL, cut = nicL$agehi, timescale = "age", new.state = "HiExp", precursor.states = "Alive" ) subset( nicC, id %in% 8:10 ) summary( nicC, scale=1000 ) ################################################### ### code chunk number 16: Follow-up.rnw:371-379 ################################################### nicC <- cutLexis( data = nicL, cut = nicL$agehi, timescale = "age", new.state = "Hi", split.states=TRUE, new.scale=TRUE, precursor.states = "Alive" ) subset( nicC, id %in% 8:10 ) summary( nicC, scale=1000 ) Epi/src/0000755000175100001440000000000012576333004011577 5ustar hornikusersEpi/src/chinv2.c0000644000175100001440000000273712576333004013145 0ustar hornikusers/* $Id: chinv2.c 11357 2009-09-04 15:22:46Z therneau $ ** ** matrix inversion, given the FDF' cholesky decomposition ** ** input **matrix, which contains the chol decomp of an n by n ** matrix in its lower triangle. ** ** returned: the upper triangle + diagonal contain (FDF')^{-1} ** below the diagonal will be F inverse ** ** Terry Therneau ** ** Copied from the survival package by Terry Therneau, version 2.35-7 */ void chinv2(double **matrix , int n) { register double temp; register int i,j,k; /* ** invert the cholesky in the lower triangle ** take full advantage of the cholesky's diagonal of 1's */ for (i=0; i0) { matrix[i][i] = 1/matrix[i][i]; /*this line inverts D */ for (j= (i+1); j=0; i--) { if (matrix[i][i]==0) y[i] =0; else { temp = y[i]/matrix[i][i]; for (j= i+1; j eps) eps = matrix[i][i]; for (j=(i+1); j #include #include /* Utility functions by Terry Therneau from the survival package These are specially adapted to work with information matrices that are not of full rank. */ int cholesky2(double **matrix, int m, double toler); void chsolve2(double **matrix, int m, double *y); void chinv2(double **matrix, int m); /* Efficient calculation of the conditional log likelihood, along with the score and information matrix, for a single stratum. Input parameters: X T x m matrix of covariate values y T-vector that indicates if an individual is a case (y[t]==1) or control (y[t]==0) T Number of individuals in the stratum m Number of covariates offset Vector of offsets for the linear predictor beta m-vector of log odds ratio parameters Output parameters: loglik The conditional log-likelihood (scalar) score The score function (m-vector) info The information matrix (m x m matrix) The contribution from this stratum will be *added* to the output parameters, so they must be correctly initialized before calling cloglik. */ static void cloglik_stratum(double const *X, int const *y, double const *offset, int T, int m, double const *beta, double *loglik, double *score, double *info) { double *f, *g, *h, *xt, *xmean; int i,j,k,t; int K = 0, Kp; int iscase = 1; double sign = 1; double lpmax; /* Calculate number of cases */ for (t = 0; t < T; ++t) { if (y[t] != 0 && y[t] != 1) { error("Invalid outcome in conditional log likelihood"); } K += y[t]; } if (K==0 || K==T) { return; /* Non-informative stratum */ } /* If there are more cases than controls then define cases to be those with y[t] == 0, and reverse the sign of the covariate values. */ if (2 * K > T ) { K = T - K; iscase = 0; sign = -1; } /* Calculate the maximum value of the linear predictor (lpmax) within the stratum. This is subtracted from the linear predictor when taking exponentials for numerical stability. Note that we must correct the log-likelihood for this, but not the score or information matrix. */ lpmax = sign * offset[0]; for (i = 0; i < m; ++i) { lpmax += sign * beta[i] * X[T*i]; } for (t = 1; t < T; ++t) { double lp = sign * offset[t]; for (i = 0; i < m; ++i) { lp += sign * beta[i] * X[t + T*i]; } if (lp > lpmax) { lpmax = lp; } } /* Calculate the mean value of the covariates within the stratum. This is used to improve the numerical stability of the score and information matrix. */ xmean = Calloc(m, double); for (i = 0; i < m; ++i) { xmean[i] = 0; for (t = 0; t < T; ++t) { xmean[i] += sign * X[t + T*i]; } xmean[i] /= T; } /* Contribution from cases */ for (t = 0; t < T; ++t) { if (y[t] == iscase) { loglik[0] += sign * offset[t]; for (i = 0; i < m; ++i) { loglik[0] += sign * X[t + i*T] * beta[i]; score[i] += sign * X[t + i*T] - xmean[i]; } loglik[0] -= lpmax; } } /* Allocate and initialize workspace for recursive calculations */ Kp = K + 1; f = Calloc(Kp, double); g = Calloc(m * Kp, double); h = Calloc(m * m * Kp, double); xt = Calloc(m, double); for (k = 0; k < Kp; ++k) { f[k] = 0; for (i = 0; i < m; ++i) { g[k+Kp*i] = 0; for (j = 0; j < m; ++j) { h[k + Kp*(i + m*j)] = 0; } } } f[0] = 1; /* Recursively calculate contributions over all possible case sets of size K. */ for (t = 0; t < T; ++t) { double Ct = offset[t]; for (i = 0; i < m; ++i) { xt[i] = sign * X[t + T*i] - xmean[i]; Ct += sign * beta[i] * X[t + T*i]; } Ct = exp(Ct - lpmax); for (k = imin2(K,t+1); k > 0; --k) { for (i = 0; i < m; ++i) { double const *gpi = g + Kp*i; for (j = 0; j < m; ++j) { double const *gpj = g + Kp*j; double *hp = h + Kp*(i + m*j); hp[k] += Ct * (hp[k-1] + xt[i] * gpj[k-1] + xt[j] * gpi[k-1] + xt[i] * xt[j] * f[k-1]); } } for (i = 0; i < m; ++i) { double *gp = g + Kp*i; gp[k] += Ct * (gp[k-1] + xt[i] * f[k-1]); } f[k] += Ct * f[k-1]; } } /* Add contributions from this stratum to the output parameters */ loglik[0] -= log(f[K]); for (i = 0; i < m; ++i) { double const *gpi = g + Kp*i; score[i] -= gpi[K] / f[K]; for (j = 0; j < m; ++j) { double const *gpj = g + Kp*j; double const *hp = h + Kp*(i + m*j); info[i + m*j] += hp[K]/f[K] - (gpi[K]/f[K]) * (gpj[K]/f[K]); } } Free(f); Free(g); Free(h); Free(xt); Free(xmean); } /* * Calculate the conditional log likelihood summed over all strata, * along with the score and information matrix. * * Input parameters: * * X - list of matrices of covariate values. One element of the list * corresponds to a single stratum * Y - list of vectors of outcomes, corresponding to X, * beta - vector of log odds ratio parameters * m - number of parameters * * Output parameters * * loglik - contains the conditional log-likelihood on exit (scalar) * score - contains the score function on exit (m - vector) * info - contains the information matrix on exit (m*m - vector) */ static void cloglik(SEXP X, SEXP y, SEXP offset, int m, double *beta, double *loglik, double *score, double *info) { int i; int M = m*m; /* Output parameters of cloglik_stratum must be initialized to zero */ loglik[0] = 0; for (i = 0; i < m; ++i) { score[i] = 0; } for (i = 0; i < M; ++i) { info[i] = 0; } for (i = 0; i < length(X); ++i) { SEXP Xi = VECTOR_ELT(X,i); SEXP yi = VECTOR_ELT(y,i); SEXP oi = VECTOR_ELT(offset, i); cloglik_stratum(REAL(Xi), INTEGER(yi), REAL(oi), nrows(Xi), m, beta, loglik, score, info); } } /* The chinv2 function only works on the lower triangle of the matrix. This wrapper function copies the lower to the upper triangle */ static void invert_info(double **imat, int m) { int i,j; chinv2(imat, m); for (i = 1; i < m; i++) { for (j = 0; j < i; j++) { imat[i][j] = imat[j][i]; } } } /* Find maximum likelihood estimate of conditional logistic regression model by Newton-Raphson. The algorithm is copied from coxph.fit from the survival package by Terry Therneau. The variable u is used to store both the score function and the step for the next iteration. Likewise info contains both the information matrix and its Cholesky decomposition. If flag > 0 then the arrays hold the score and variance-covariance matrix respectively. */ static void clogit_fit(SEXP X, SEXP y, SEXP offset, int m, double *beta, double *loglik, double *u, double *info, int *flag, int *maxiter, double const *eps, double const * tol_chol) { int i, iter = 0; Rboolean halving = FALSE; double *oldbeta = Calloc(m, double); double **imat = Calloc(m, double*); /* Set up ragged array representation of information matrix for use by cholesky2, chsolve2, and invert_info functions */ for (i = 0; i < m; ++i) { imat[i] = info + m*i; } /* Initial iteration */ cloglik(X, y, offset, m, beta, loglik, u, info); if (*maxiter > 0) { *flag = cholesky2(imat, m, *tol_chol); if (*flag > 0) { chsolve2(imat, m, u); for (i = 0; i < m; i++) { oldbeta[i] = beta[i]; beta[i] += u[i]; } } else { /* Bad information matrix. Don't go into the main loop */ *maxiter = 0; } } /* Main loop */ for (iter = 1; iter <= *maxiter; iter++) { double oldlik = *loglik; cloglik(X, y, offset, m, beta, loglik, u, info); if (fabs(1 - (oldlik / *loglik)) <= *eps && !halving) { /* Done */ break; } else if (iter == *maxiter) { /* Out of time */ *flag = 1000; break; } else if (*loglik < oldlik) { /* Not converging: halve step size */ halving = TRUE; for (i = 0; i < m; i++) { beta[i] = (beta[i] + oldbeta[i]) /2; } } else { /* Normal update */ halving = FALSE; oldlik = *loglik; *flag = cholesky2(imat, m, *tol_chol); if (*flag > 0) { chsolve2(imat, m, u); for (i = 0; i < m; i++) { oldbeta[i] = beta[i]; beta[i] += u[i]; } } else { break; /* Bad information matrix */ } } } *maxiter = iter; if (*flag > 0) { cholesky2(imat, m, *tol_chol); invert_info(imat, m); } Free(oldbeta); Free(imat); } /* R interface */ SEXP clogit(SEXP X, SEXP y, SEXP offset, SEXP init, SEXP maxiter, SEXP eps, SEXP tol_chol) { int i; int n = length(X); int m = length(init); int M = m*m; int flag = 0; int niter = INTEGER(maxiter)[0]; double loglik[2], *score, *info, *beta; SEXP ans, a, names, dims; if (!isNewList(X)) error("'X' must be a list"); if (!isNewList(y)) error("'y' must be a list"); if (!isNewList(offset)) error("'offset' must be a list"); if (length(X) != length(y)) error("length mismatch between X and y"); if (length(X) != length(offset)) error("length mismatch between X and offset"); for (i = 0; i < n; ++i) { int T = nrows(VECTOR_ELT(X,i)); int xcols = ncols(VECTOR_ELT(X,i)); int ylen = length(VECTOR_ELT(y,i)); int olen = length(VECTOR_ELT(offset, i)); if (xcols != m) { error("Element %d of X has %d columns; expected %d", i, xcols, m); } if (ylen != T) { error("Element %d of y has length %d; expected %d", i, ylen, T); } if (olen != T) { error("Element %d of offset has length %d; expected %d", i, ylen, T); } } beta = (double *) R_alloc(m, sizeof(double)); for (i = 0; i < m; ++i) { beta[i] = REAL(init)[i]; } score = (double *) R_alloc(m, sizeof(double)); info = (double *) R_alloc(M, sizeof(double)); /* Calculate initial loglikelihood */ cloglik(X, y, offset, m, beta, &loglik[0], score, info); /* Maximize the likelihood */ clogit_fit(X, y, offset, m, beta, &loglik[1], score, info, &flag, &niter, REAL(eps), REAL(tol_chol)); /* Construct return list */ PROTECT(ans = allocVector(VECSXP, 5)); PROTECT(names = allocVector(STRSXP, 5)); /* Estimates */ PROTECT(a = allocVector(REALSXP, m)); for (i = 0; i < m; ++i) { REAL(a)[i] = beta[i]; } SET_VECTOR_ELT(ans, 0, a); SET_STRING_ELT(names, 0, mkChar("coefficients")); UNPROTECT(1); /* Log likelihood */ PROTECT(a = allocVector(REALSXP, 2)); REAL(a)[0] = loglik[0]; REAL(a)[1] = loglik[1]; SET_VECTOR_ELT(ans, 1, a); SET_STRING_ELT(names, 1, mkChar("loglik")); UNPROTECT(1); /* Information matrix */ PROTECT(a = allocVector(REALSXP, M)); PROTECT(dims = allocVector(INTSXP, 2)); for (i = 0; i < M; ++i) { REAL(a)[i] = info[i]; } INTEGER(dims)[0] = m; INTEGER(dims)[1] = m; setAttrib(a, R_DimSymbol, dims); SET_VECTOR_ELT(ans, 2, a); SET_STRING_ELT(names, 2, mkChar("var")); UNPROTECT(2); /* Flag */ PROTECT(a = ScalarInteger(flag)); SET_VECTOR_ELT(ans, 3, a); SET_STRING_ELT(names, 3, mkChar("flag")); UNPROTECT(1); /* Number of iterations */ PROTECT(a = ScalarInteger(niter)); SET_VECTOR_ELT(ans, 4, a); SET_STRING_ELT(names, 4, mkChar("iter")); UNPROTECT(1); setAttrib(ans, R_NamesSymbol, names); UNPROTECT(2); return(ans); } Epi/NAMESPACE0000644000175100001440000000747612576332467012261 0ustar hornikusersexport( as.Date.cal.yr, apc.frame, apc.fit, apc.plot, apc.lines, plot.apc, lines.apc, pc.points, pc.lines, pc.matpoints, pc.matlines, cal.yr, as.Date.cal.yr, ccwc, ci.pd, ci.lin, ci.exp, ci.cum, ci.pred, ci.mat, lls, clear, contr.orth, contr.2nd, contr.cum, contr.diff, detrend, dur, effx, effx.match, float, print.floated, gen.exp, Icens, print.Icens, plotevent, fit.add, fit.mult, ftrend, Lexis.diagram, Lexis.lines, Life.lines, Lexis, merge.Lexis, plot.Lexis, points.Lexis, lines.Lexis, PY.ann.Lexis, subset.Lexis, summary.Lexis, print.summary.Lexis, splitLexis, transform.Lexis, levels.Lexis, Relevel.Lexis, factorize.Lexis, cutLexis, countLexis, stack.Lexis, tmat.Lexis, boxes.Lexis, boxes.matrix, boxes.MS, msdata.Lexis, etm.Lexis, crr.Lexis, simLexis, subset.stacked.Lexis, transform.stacked.Lexis, plot.pState, lines.pState, entry, exit, status, timeBand, timeScales, breaks, merge.data.frame, tbox, dbox, fillarr, boxarr, boxes, factorize, PY.ann, N2Y, tmat, nState, pState, msdata, etm, mh, ncut, nice, NArray, ZArray, Ns, Termplot, pctab, plotEst, pointsEst, projection.ip, linesEst, rateplot, Aplot, Pplot, Cplot, Relevel, ROC, twoby2, Wald, stat.table, clogistic) # Import generic methods importFrom( utils, stack ) importFrom( splines, ns, bs ) importFrom( cmprsk, crr) importFrom( etm, etm ) importFrom( MASS, mvrnorm ) importFrom( survival, clogit) importFrom("grDevices", "gray", "rainbow") importFrom("graphics", "abline", "arrows", "axis", "box", "layout", "lines", "locator", "matlines", "matplot", "matpoints", "mtext", "par", "plot", "plot.new", "plot.window", "points", "polygon", "rect", "rug", "segments", "strheight", "strwidth", "text") importFrom("stats", ".getXlevels", "addmargins", "anova", "approx", "ave", "binomial", "coef", "complete.cases", "contr.sum", "fisher.test", "fitted", "gaussian", "glm", "is.empty.model", "median", "model.extract", "model.matrix", "model.offset", "model.response", "nlm", "pchisq", "pnorm", "poisson", "predict", "qnorm", "qt", "quantile", "runif", "termplot", "update", "vcov", "weighted.mean", "xtabs") importFrom("utils", "flush.console", "str") ## importFrom( mstate, mstate ) # register S3 methods S3method( print, Icens) S3method( print, floated) S3method( plot, apc) S3method( lines, apc) S3method( plot, Lexis) S3method( plot, pState) S3method( lines, pState) S3method( points, Lexis) S3method( lines, Lexis) S3method( PY.ann, Lexis) S3method( merge, Lexis) S3method( subset, Lexis) S3method( subset, stacked.Lexis) S3method( summary, Lexis) S3method( print, summary.Lexis) S3method(transform, Lexis) S3method(transform, stacked.Lexis) S3method( levels, Lexis) S3method( Relevel, Lexis) S3method( Relevel, factor) S3method( Relevel, default) S3method(factorize, Lexis) S3method( stack, Lexis) S3method( tmat, Lexis) S3method( boxes, Lexis) S3method( boxes, matrix) S3method( boxes, MS) S3method( msdata, Lexis) S3method( etm, Lexis) S3method( merge, data.frame) S3method( print, stat.table) S3method( print, clogistic) S3method( coef, clogistic) S3method( vcov, clogistic) S3method( as.Date, cal.yr) Epi/data/0000755000175100001440000000000012531361106011714 5ustar hornikusersEpi/data/lungDK.rda0000644000175100001440000000535612531361106013601 0ustar hornikusers{tMgOA"rriVGMŌsTuC$9JKD"R-J0fDGi'S,mZjd. .ѢfTgRӺE1&3|߷g3g|˷aJϕsG#76xt^:^:a/4쥓YkJsnԓZtEk-E-zʢ-z?X,:0XCyCyCyCyCyCy@^ C=aѿYNuhE<~Q_h﫲}%|}dѧwPPPPPPs&h@(ϣ:vGbx}BS{ODߎ%俎mAދ[G~_Wf8>}b { v}II0OϹ KLc8*GQ'~:n7d{SD}||9Ջ3pTM_9> f`Lgb<#8@?2ϓ8~%/5<.~KN.y#_l~N3tq|#S^ Z9VƼ⿷ĜkƁ==\j+t#o|_""oU ΃w0= A7c͘fh5λH|q1yg7u&t#k90O*:s C?y1 ؾKp|,k} } u{SvP)ʨPǢ)דio>Ǚ<~_`&'q+_''_'Y ^_F9 0k@-Ӆ&7t˝#IФ/IsIl-z?(.gDpv6aa m y^u,=kn!\h/+yϊ{]c=: GxCV@|8^ӍĎl\o _z1ڞ{|W& _g wH<-F{W{\Gc-;q/ ;woqAG6d/)B>q19!yo׶6 WEb_yL_]8K,Ek0׷&u^=7a]:-=~:lq;o7^nN:啄KWD9#ǭeg38o|!Mh5f̖&OYo T3jwq]h?7{+p|46C/^l}#΁k`Iz6;ȝX ߰Nu &ڸD?pe<}akX9ӸUֶd~̩ ]r#uIJ4̭|7zRZ{u,Tai;_&i!,/9cԨ!V> ]"+0c"4B֡BKEF4BVe1G4!b*&R~Ũ<aX ю?K$gĘ /mG]G /ȅbB5WѤ~!tP.Q=UPS~DmgTx=+]NSNHRaj4URhnfNa'q4CjtML=`׏q_Q.^82Å.5 NnUB=T^ W .e:&7f6R#Jҟ];dxР"lGvk̅KǠꣻy6dMsZ߀Q| зJ3NI]#'=2輗9 8+[(NRX^މo2Mbl<3},;zBÃ-p tԱGij2 ޟ9:}GiL{7iRŇ>7>:EYC"]t_lRsjyK49ܯO4x4-IV@osN$3_.PݧVL1VB(QjM/fn/ϕX>ÿr`Ԯl|Lsn*B#}1#`]3X'H)Jh5YL}dEΦ{ծ8(Q\ 5+ c+˞NR~Vk @% \!4N{4}* >JM(mF&L | N?ςdMaD':f+bCtVH b$qς>M(y}>/^~ $l+ Ѧ &DKxY )& ue9Q 5>kv:U>M xa'A08 'r? 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    ʎ"mWp Cf,6ff@2O$ݴvM}pjz-[a+!#IkqD7YEE`GBZ>(pQpgX|1Ua4# $ވ+Z1+ EBӶt"KN Tg˳˝>xAY h$Oz)K۫:vZp<WYz@S0 ( ˺" 8WPN.W& I\8]J0 YZEpi/R/0000755000175100001440000000000012576333004011211 5ustar hornikusersEpi/R/expand.data.r0000644000175100001440000000436212531361077013572 0ustar hornikusersexpand.data <- function(fu, formula, breaks, data) { Nsubject <- nrow(fu) ## The model matrix to be merged up to the expanded data if (!missing(formula)) { covariate.frame <- data.frame(model.matrix(formula,data)[,-1]) cov.names <- names(covariate.frame) covariate.frame$id <- 1:Nsubject } else { covariate.frame <- NULL cov.names <- character(0) } ## 1-responses for the survival intervals Nbreak <- length(breaks) ILENGTH <- function(x) { pmax(pmin(x[2], breaks[-1]) - pmax(x[1], breaks[-Nbreak]), 0) } well.mat <- -matrix(apply(fu[,c(1,2), drop=FALSE], 1, ILENGTH), nrow=Nbreak-1) ## For the sake of the stability of the fitting procedure, each record ## with a 1-response is further split into separate records for each ## follow-up interval: id.vec <- c(diag(Nbreak-1)) well.mat <- matrix(apply(well.mat, 2, "*", id.vec), nrow=Nbreak-1) well.id <- rep(1:Nsubject, each=Nbreak-1) valid.cols <- apply(well.mat!=0, 2, any) well.mat <- well.mat[,valid.cols, drop=FALSE] #Remove cols that are all zero well.id <- well.id[valid.cols] ## 0-responses for the event intervals is.case <- !is.na(fu[,3]) # Observed to become ill fu.cases <- subset(fu, is.case) ill.mat <- -matrix(apply(fu.cases[,c(2,3), drop=FALSE], 1, ILENGTH), nrow=Nbreak-1) ill.id <- which(is.case) ## The dataframe for analysis is one observation per survival interval ## (well.mat) and one per event interval (ill.mat): rates.frame <- as.data.frame(t(cbind(well.mat, ill.mat))) rates.names <- paste("(", breaks[-Nbreak],",",breaks[-1],")", sep="") names(rates.frame) <- rates.names rates.frame$y <- rep(c(1,0), c(ncol(well.mat), ncol(ill.mat))) rates.frame$id <- c(well.id, ill.id) ## Merge the covariates on to the model matrix for the baseline if (!is.null(covariate.frame)) { model.frame <- merge(rates.frame, covariate.frame, by="id") } else { model.frame <- rates.frame } model.frame[["id"]] <- NULL #Remove id variable return( list( rates.frame = model.frame[,rates.names, drop=FALSE], cov.frame = model.frame[, cov.names, drop=FALSE], y = model.frame[, "y", drop=TRUE ] ) ) } Epi/R/clear.R0000644000175100001440000000056112531361077012426 0ustar hornikusers# Clear the workspace of all objects whose names don't start with a full stop clear <- function() { env <- as.environment(1) to.go <- ls(env, all.names=FALSE) continue <- TRUE while (continue) { nxt <- search()[[2]] # bit of a botch if (substr(nxt, 1, 8)!="package:") detach() else continue <- FALSE } remove(list=to.go, envir=env) } Epi/R/clogistic.R0000644000175100001440000001500112531361077013313 0ustar hornikuserssplitMatrix <- function (x, f, drop=FALSE) { lapply(split(seq_len(nrow(x)), f, drop = drop), function(ind) x[ind, , drop = FALSE]) } fixEvent <- function(event) { ### Convert outcome in clogit model to 0/1 binary coding if (any(is.na(event))) stop("Event contains missing values") if (is.logical(event)) { status <- is.numeric(event) } else if (is.numeric(event)) { status <- if (max(event) == 2) event - 1 else event temp <- (status == 0 | status == 1) if (!all(temp)) { warning("If outcome is numeric then it must be coded 0/1 or 1/2") } } else if (is.factor(event)) { if (nlevels(event) != 2) stop("If outcome is a factor then it must have 2 levels") status <- event == levels(event)[2] } return(as.integer(status)) } isInformative <- function(Xsplit, ysplit, strata) { ## Identify which observations are informative in a conditional ## logistic regression. is.homogeneous <- function(x) { all(x==x[1]) } y.bad <- sapply(ysplit, is.homogeneous) X.bad <- sapply(Xsplit, function(x) { all(apply(x, 2, is.homogeneous)) }) is.informative <- vector("list", length(ysplit)) for (i in seq(along=is.informative)) { canuse <- (!y.bad[i]) && (!X.bad[i]) is.informative[[i]] <- rep(canuse, length(ysplit[[i]])) } return(unsplit(is.informative, strata)) } fitClogit <- function(X, y, offset, strata, init, iter.max, eps, toler.chol) { ## Safe wrapper around the C function "clogit" that ensures all ## arguments have the correct type and storage mode. y <- fixEvent(y) if (!is.matrix(X)) { X <- as.matrix(X) } if (!is.double(X)) { X <- matrix(as.double(X), nrow(X), ncol(X)) } if (is.null(offset)) { offset <- rep(0, nrow(X)) } offset <- as.double(offset); ## Split into strata Xsplit <- splitMatrix(X, strata, drop=TRUE) ysplit <- split(y, strata, drop=TRUE) osplit <- split(offset, strata, drop=TRUE) info <- isInformative(Xsplit, ysplit, strata) if (!any(info)) { stop("There are no informative observations") } ans <- .Call("clogit", Xsplit, ysplit, osplit, as.double(init), as.integer(iter.max), as.double(eps), as.double(toler.chol), PACKAGE="Epi") ans$informative <- info return(ans) } clogistic <- function (formula, strata, data, subset, na.action, init, model = TRUE, x = FALSE, y = TRUE, contrasts = NULL, iter.max=20, eps=1e-6, toler.chol = sqrt(.Machine$double.eps)) { ## User interface, edited version of glm call <- match.call() if (missing(data)) data <- environment(formula) mf <- match.call(expand.dots = FALSE) m <- match(c("formula", "data", "subset", "na.action", "offset", "strata"), names(mf), 0L) mf <- mf[c(1, m)] mf$drop.unused.levels <- TRUE mf[[1L]] <- as.name("model.frame") mf <- eval(mf, parent.frame()) mt <- attr(mf, "terms") Y <- model.response(mf, "any") if (is.null(Y)) stop("missing outcome") if (length(dim(Y)) == 1L) { nm <- rownames(Y) dim(Y) <- NULL if (!is.null(nm)) names(Y) <- nm } X <- if (!is.empty.model(mt)) model.matrix(mt, mf, contrasts) else stop("Invalid model matrix") offset <- as.vector(model.offset(mf)) if (!is.null(offset)) { if (length(offset) != NROW(Y)) stop(gettextf("number of offsets is %d should equal %d (number of observations)", length(offset), NROW(Y)), domain = NA) } strata <- model.extract(mf, "strata") if (is.null(strata)) stop("argument 'strata' missing") contrasts <- attr(X, "contrasts") if (attr(mt, "intercept") > 0) { X <- X[,-1, drop=FALSE] } if (missing(init)) init <- rep(0, ncol(X)) if (iter.max < 0) stop("'iter.max' must be non-negative") if (eps <= 0) stop("'eps' must be positive") if (toler.chol <= 0) stop("'toler.chol' must be positive") if (eps < toler.chol) stop("'toler.chol' must be smaller than 'eps'") fit <- fitClogit(X = X, y = Y, offset = offset, strata=strata, init=init, toler.chol=toler.chol, eps=eps, iter.max=iter.max) if (fit$flag <= 0) { stop("Information matrix is not positive definite") } else if (fit$flag == 1000) { warning("Iteration limit exceeded") } nvar <- length(init) which.sing <- if (fit$flag < nvar) { diag(fit$var)==0 } else { rep(FALSE, nvar) } fit$coefficients[which.sing] <- NA fit$flag <- NULL ## Add back in parameter names cfnames <- colnames(X) names(fit$coefficients) <- cfnames dimnames(fit$var) <- list(cfnames, cfnames) fit$n <- sum(fit$informative) if (model) { fit$model <- mf } else { ## Without model frame this cannot be interpreted fit$informative <- NULL } fit$na.action <- attr(mf, "na.action") if (x) fit$x <- X if (!y) fit$y <- NULL fit <- c(fit, list(call = call, formula = formula, terms = mt, contrasts = contrasts, xlevels = .getXlevels(mt, mf))) class(fit) <- c("clogistic") fit } coef.clogistic <- function(object,...) { object$coefficients } vcov.clogistic <- function(object, ...) { object$var } print.clogistic <- function (x, digits = max(options()$digits - 4, 3), ...) { ## Print method for clogistic objects, edited from print.coxph cat("\nCall: ", deparse(x$call), "\n\n", sep="\n") savedig <- options(digits = digits) on.exit(options(savedig)) coef <- coef.clogistic(x) se <- sqrt(diag(vcov.clogistic(x))) if (is.null(coef) | is.null(se)) stop("Input is not valid") coefmat <- cbind(coef, exp(coef), se, coef/se, signif(1 - pchisq((coef/se)^2, 1), digits - 1)) dimnames(coefmat) <- list(names(coef), c("coef", "exp(coef)", "se(coef)", "z", "p")) cat("\n") prmatrix(coefmat) logtest <- -2 * (x$loglik[1] - x$loglik[2]) if (is.null(x$df)) df <- sum(!is.na(coef)) else df <- round(sum(x$df), 2) cat("\n") cat("Likelihood ratio test=", format(round(logtest, 2)), " on ", df, " df,", " p=", format(1 - pchisq(logtest, df)), ", n=", x$n, sep = "") cat("\n") invisible() } Epi/R/detrend.R0000644000175100001440000000057712531361077012774 0ustar hornikusersdetrend <- function( M, t, weight=rep(1,nrow(M)) ) { Thin.col <- function ( X, tol = 1e-06) # Function to remove lin. dep. columns from a matrix { QR <- qr(X, tol = tol, LAPACK = FALSE) X[, QR$pivot[seq(length = QR$rank)], drop = FALSE] } # Now detrend the matrix using the weighted inner product. Thin.col( projection.ip( cbind( 1, t ), M , orth = TRUE, weight = weight ) ) } Epi/R/lexis.R0000644000175100001440000005231512531361077012470 0ustar hornikusersLexis <- function(entry, exit, duration, entry.status=0, exit.status=0, id, data, merge=TRUE, states, tol=.Machine$double.eps^0.5, keep.dropped=FALSE ) { nmissing <- missing(entry) + missing(exit) + missing(duration) if (nmissing > 2) stop("At least one of the arguments exit and duration must be supplied") only.exit <- missing( entry.status ) && !missing( exit.status ) ## If data argument is supplied, use it to evaluate arguments if (!missing(data)) { if (!missing(entry)) { entry <- eval(substitute(entry), data, parent.frame()) } if (!missing(exit)) { exit <- eval(substitute(exit), data, parent.frame()) } if (!missing(duration)) { duration <- eval(substitute(duration), data, parent.frame()) } entry.status <- eval(substitute(entry.status), data, parent.frame()) exit.status <- eval(substitute(exit.status), data, parent.frame()) if (!missing(id)) { id <- eval(substitute(id), data, parent.frame()) } if (merge) { data <- as.data.frame(data) } } ## Check for missing values in status variables wh.miss <- any(is.na(entry.status)) + 2*any(is.na(exit.status)) if ( wh.miss > 0 ) stop("Missing values in ", switch( wh.miss, "entry status", "exit status", "entry AND exit status" ) ) ## Adjust entry status mode according to exit status if( only.exit ) { if( is.logical( exit.status ) ) entry.status <- FALSE if( is.character( exit.status ) ) exit.status <- factor( exit.status ) if( is.factor( exit.status ) ) { entry.status <- factor( rep( levels(exit.status)[1], length(exit.status)), levels=levels(exit.status), labels=levels(exit.status) ) cat("NOTE: entry.status has been set to", paste( '"', levels(exit.status)[1], '"', sep='' ), "for all.\n" ) } if( is.numeric( exit.status ) ) entry.status <- rep( 0, length( exit.status ) ) } ## Convert character states to factors if( is.character(entry.status) ) entry.status <- factor(entry.status) if( is.character( exit.status) ) exit.status <- factor( exit.status) ## Check compatibility of entry and exit status if (is.factor(entry.status) || is.factor(exit.status)) { if (is.factor(entry.status) && is.factor(exit.status)) { if (!identical(levels(entry.status),levels(exit.status))) { all.levels = union(levels(entry.status),levels(exit.status)) entry.status <- factor( entry.status, levels=all.levels ) exit.status <- factor( exit.status, levels=all.levels ) cat("Incompatible factor levels in entry.status and exit.status:\n", "both lex.Cst and lex.Xst now have levels:\n", all.levels, "\n") } } else { stop("Incompatible classes for entry and exit status") } } else { if (mode(entry.status) != mode(exit.status)) { stop("Incompatible mode for entry and exit status") } } ## If entry is missing and one of the others is given as a list of length ## one, entry is assumed to be 0 on this only timescale. if( nmissing==2 ) { if( !missing(exit) ) { if( length(exit)>1 ) stop("If 'entry' is omitted, only one timescale can be specified.") else { entry <- exit entry[[1]] <- 0*entry[[1]] cat( "NOTE: entry is assumed to be 0 on the",names(exit),"timescale.\n") } } else if( !missing(duration) ) { if( length(duration)>1 ) stop("If 'entry' is omitted, only one timescale can be specified") else { entry <- duration entry[[1]] <- 0*entry[[1]] cat( "NOTE: entry is assumed to be 0 on the",names(duration),"timescale.\n") } } else stop("Either exit or duration must be supplied.") } ## Coerce entry and exit lists to data frames if(!missing(entry)) { entry <- as.data.frame(entry) if (is.null(names(entry))) stop("entry times have no names") if (any(substr(names(entry),1,4) == "lex.")) stop("names starting with \"lex.\" cannot be used for time scales") } if(!missing(exit)) { exit <- as.data.frame(exit) if (is.null(names(exit))) stop("exit times have no names") if (any(substr(names(exit),1,4) == "lex.")) stop("names starting with \"lex.\" cannot be used for time scales") } if(!missing(duration)) { duration <- as.data.frame(duration) if (is.null(names(duration))) stop("duration have no names") if (any(substr(names(duration),1,4) == "lex.")) stop("names starting with \"lex.\" cannot be used for time scales") } if (missing(entry)) { ## Impute entry entry <- exit - duration } if (missing(duration)) { ## Impute duration full.time.scales <- intersect(names(entry), names(exit)) if (length(full.time.scales) == 0) { stop("Cannot calculate duration from entry and exit times") } duration <- exit[,full.time.scales[1]] - entry[,full.time.scales[1]] } if (missing(exit)) { all.time.scales <- names(entry) } else { ## We dont need the exit times but, if they are supplied, we must ## make sure they are consistent with the entry and duration. all.time.scales <- unique(c(names(entry), names(exit))) ## Fill in any missing entry times entry.missing <- setdiff(all.time.scales, names(entry)) if (length(entry.missing) > 0) { entry <- cbind(entry, exit[,entry.missing, drop=FALSE] - duration) } ## Check that duration is the same on all time scales dura <- exit - entry[,names(exit),drop=FALSE] if (missing(duration)) { duration <- dura[,1] #BxC# apply( dura, 1, mean, na.rm=TRUE ) # Allows for timescales with missing values } ok <- sapply(lapply(dura, all.equal, duration), isTRUE) # ok <- sapply(lapply(dura, all.equal, duration), # function(x) identical(FALSE,x) ) if (!all(ok)) { stop("Duration is not the same on all time scales") } } # Taken care of by the code that detects whether lex.du <= tol # ## Check that duration is positive # if (any(duration<0)) { # stop("Duration must be non-negative") # } ## Make sure id value - if supplied - is valid. Otherwise supply default id if (missing(id)) { id <- 1:nrow(entry) } else if (any(duplicated(id))) { ##Fixme: check for overlapping intervals ##stop("Duplicate values in id") } ## Return a data frame with the entry times, duration, and status ## variables Use the prefix "lex." for the names of reserved ## variables. if( is.data.frame( duration ) ) duration <- duration[,1] lex <- data.frame(entry, "lex.dur" = duration, "lex.Cst" = entry.status, "lex.Xst" = exit.status, "lex.id" = id ) #### Addition by BxC --- support for states as factors # Convert states to factors if states are given if( !missing( states ) ) #is.character( states ) ) { # This as.character-business is necessary because we cannot assume # that the values of states are 1,2, etc. st.lev <- sort( unique( as.character( c(lex$lex.Cst,lex$lex.Xst) ) ) ) lex$lex.Cst <- factor( as.character(lex$lex.Cst), levels=st.lev, labels=states ) lex$lex.Xst <- factor( as.character(lex$lex.Xst), levels=st.lev, labels=states ) } if (!missing(data) && merge) { duplicate.names <- intersect(names(lex), names(data)) if (length(duplicate.names) > 0) { stop("Cannot merge data with duplicate names") } lex <- cbind(lex, data) } ## Drop rows with short or negantive duration for consistency with splitLexis short.dur <- lex$lex.dur <= tol if ( any(short.dur) ) { warning("Dropping ", sum(short.dur), " rows with duration of follow up < tol\n", if( keep.dropped ) " The dropped rows are in the attribute 'dropped'\n", if( keep.dropped ) " To see them type attr(Obj,'dropped'),\n", if( keep.dropped ) " to get rid of them type attr(Obj,'dropped') <- NULL\n", if( keep.dropped ) " - where 'Obj' is the name of your Lexis object" ) lex <- subset(lex, !short.dur) if( keep.dropped ) attr(lex,"dropped") <- subset(data, short.dur) } ## Return Lexis object attr(lex,"time.scales") <- all.time.scales attr(lex,"time.since") <- rep( "", length(all.time.scales) ) breaks <- vector("list", length(all.time.scales)) names(breaks) <- all.time.scales attr(lex,"breaks") <- breaks class(lex) <- c("Lexis", class(lex)) return(lex) } is.Lexis <- function(x) { inherits(x, "Lexis") } check.time.scale <- function(lex, time.scale=NULL) { ## Utility function, returns the names of the time scales in a Lexis object ## lex - a Lexis object ## time.scale - a numeric or character vector. The function checks that ## these are valid time scales for the Lexis object. ## Return value is a character vector containing the names of the requested ## time scales all.names <- timeScales(lex) if (is.null(time.scale)) return(all.names) nscale <- length(time.scale) scale.names <- character(nscale) if (is.character(time.scale)) { for (i in 1:nscale) { if (is.null(lex[[time.scale[i]]])) stop(time.scale[i], " is not a valid time scale name") } } else if (is.numeric(time.scale)) { if (any(time.scale > length(all.names)) || any(time.scale < 1)) stop(time.scale, " not valid time scale column number(s)") time.scale <- all.names[time.scale] } else { stop("invalid type for time scale") } return(time.scale) } valid.times <- function(x, time.scale=1) { # A utility function that returns a data.frame / Lexis object with # rows with missing timescales removed x[complete.cases(x[,check.time.scale(x,time.scale)]),] } plot.Lexis.1D <- function(x, time.scale=1, breaks="lightgray", type="l", col="darkgray", xlim, ylim, xlab, ylab, ...) { ## x Lexis object ## time.scale name of time scale to plot if (length(time.scale) != 1) stop("Only one time scale allowed") x <- valid.times(x,time.scale) time.entry <- x[,time.scale] time.exit <- x[,time.scale] + x$lex.dur id <- x$lex.id if (missing(xlim)) xlim <- c(min(time.entry), max(time.exit)) if (missing(ylim)) ylim <- range(id) if (missing(xlab)) xlab <- time.scale if (missing(ylab)) ylab <- "id number" plot(time.entry, id, type="n", xlab=xlab, ylab=ylab, xlim=xlim, ylim=ylim, ...) if (type=="b" || type=="l") { segments(time.entry, id, time.exit, id, col=col, ...) } if (type=="b" || type=="p") { points(time.exit, id, col=col, ...) } ## Plot break points brk <- attr(x,"breaks")[[time.scale]] abline(v=brk, col=breaks, ...) } points.Lexis.1D <- function(x, time.scale, ...) { x <- valid.times(x,time.scale) time.exit <- x[,time.scale] + x$lex.dur points(time.exit, x$lex.id, ...) } lines.Lexis.1D <- function(x, time.scale, type="l", col="darkgray", breaks="lightgray", ...) { x <- valid.times(x,time.scale) time.entry <- x[,time.scale] time.exit <- x[,time.scale] + x$lex.dur id <- x$lex.id segments(time.entry, id, time.exit, id, col=col, ...) ## Plot break points brk <- attr(x,"breaks")[[time.scale]] abline(v=brk, col=breaks, ...) } plot.Lexis.2D <- function(x, time.scale, breaks="lightgray", type="l", col="darkgray", xlim, ylim, xlab, ylab, grid=FALSE, col.grid="lightgray", lty.grid=2, coh.grid=FALSE, ...) { if (length(time.scale) != 2) stop("Two time scales are required") x <- valid.times(x,time.scale) time.entry <- time.exit <- vector("list",2) for (i in 1:2) { time.entry[[i]] <- x[,time.scale[i]] time.exit[[i]] <- x[,time.scale[i]] + x$lex.dur } if (missing(xlim) && missing(ylim)) { ## If no axis limits are given, set the plotting region to be ## square, and adjust the axis limits to cover the same time interval. ## All life lines will then be at 45 degrees. opar <- par(pty="s") on.exit(par(opar)) min.times <- sapply(time.entry, min) max.times <- sapply(time.exit, max) xywidth <- max(max.times - min.times) xlim <- min.times[1] + c(0, xywidth) ylim <- min.times[2] + c(0, xywidth) } else if (missing(xlim)) { xlim <- c(min(time.entry[[1]]), max(time.exit[[1]])) } else if (missing(ylim)) { ylim <- c(min(time.entry[[2]]), max(time.exit[[2]])) } if (missing(xlab)) xlab <- time.scale[1] if (missing(ylab)) ylab <- time.scale[2] plot(time.entry[[1]], time.entry[[2]], type="n", xlab=xlab, ylab=ylab, xlim=xlim, ylim=ylim, ...) # Set up the background grid(s): if (!missing(grid)) { if (is.logical(grid)) { if (grid) { vgrid <- pretty(xlim) hgrid <- pretty(ylim) } } else if (is.list(grid)) { vgrid <- grid[[1]] hgrid <- grid[[length(grid)]] } else if (is.numeric(grid)) { vgrid <- grid - min( grid ) + min( pretty( xlim )[pretty(xlim)>=par("usr")[1]] ) hgrid <- grid - min( grid ) + min( pretty( ylim )[pretty(ylim)>=par("usr")[3]] ) } else stop( "'grid' must be either logical, list or a numeric vector" ) # and plot the grid: abline( v=vgrid, h=hgrid, col=col.grid, lty=lty.grid ) box() } if (!missing(grid) & coh.grid) { # Make the 45-degree grids as fine as the finest grid on the axes for (yy in c(hgrid-diff(range(hgrid)),hgrid)) abline( yy-min(vgrid), 1, col=col.grid, lty=lty.grid ) for (yy in c(vgrid-diff(range(vgrid)),vgrid)) abline( min(hgrid)-yy, 1, col=col.grid, lty=lty.grid ) } # End of explicitly requested background grid(s) (PHEW!) if (type=="b" || type=="l") { segments(time.entry[[1]], time.entry[[2]], time.exit[[1]], time.exit[[2]], col=col, ...) } if (type=="b" || type=="p") { points(time.exit[[1]], time.exit[[2]], col = col, ...) } if (type != "n") { ## Plot break points brk <- attr(x,"breaks")[time.scale] abline(v=brk[[1]], h=brk[[2]], col=breaks, ...) } } points.Lexis.2D <- function(x, time.scale, ...) { x <- valid.times(x,time.scale) time.exit <- vector("list",2) for (i in 1:2) { time.exit[[i]] <- x[,time.scale[i]] + x$lex.dur } points( time.exit[[1]], time.exit[[2]], ...) } lines.Lexis.2D <- function(x, time.scale, col="darkgray", ...) { x <- valid.times(x,time.scale) time.entry <- time.exit <- vector("list",2) for (i in 1:2) { time.entry[[i]] <- x[,time.scale[i]] time.exit[[i]] <- x[,time.scale[i]] + x$lex.dur } segments(time.entry[[1]], time.entry[[2]], time.exit[[1]], time.exit[[2]], col=col, ...) } ### Plotting generic functions plot.Lexis <- function( x = Lexis( entry=list(Date=1900,Age=0), exit=list(Age=0) ), time.scale=NULL, type="l", breaks="lightgray", ...) { time.scale <- check.time.scale(x, time.scale) if (length(time.scale) > 2) time.scale <- time.scale[1:2] # Save the timescale(s) for use in subsequent calls options( Lexis.time.scale = time.scale ) if (length(time.scale) == 1) plot.Lexis.1D(x, time.scale=time.scale, type=type, breaks=breaks, ...) else if (length(time.scale) == 2) plot.Lexis.2D(x, time.scale=time.scale, type=type, breaks=breaks, ...) } lines.Lexis <- function(x, time.scale=options()[["Lexis.time.scale"]], ...) { time.scale <- check.time.scale(x, time.scale) if (length(time.scale) > 2) time.scale <- time.scale[1:2] if (length(time.scale) == 1) lines.Lexis.1D(x, time.scale=time.scale, ...) else if (length(time.scale) == 2) lines.Lexis.2D(x, time.scale=time.scale, ...) } points.Lexis <- function(x, time.scale=options()[["Lexis.time.scale"]], ...) { time.scale <- check.time.scale(x, time.scale) if (length(time.scale) > 2) time.scale <- time.scale[1:2] if (length(time.scale) == 1) points.Lexis.1D(x, time.scale=time.scale, ...) else if (length(time.scale) == 2) points.Lexis.2D(x, time.scale=time.scale, ...) } PY.ann <- function (x, ...) UseMethod("PY.ann") PY.ann.Lexis <- function( x, time.scale=options()[["Lexis.time.scale"]], digits=1, ... ) { if( !inherits(x,"Lexis") ) stop( "Only meaningful for Lexis objects not for objects of class ", class(x) ) wh.x <- x[,time.scale[1]] + x[,"lex.dur"]/2 if( two.scales <- length(time.scale)==2 ) wh.y <- x[,time.scale[2]] + x[,"lex.dur"]/2 else wh.y <- x[,"lex.id"] text( wh.x, wh.y, formatC(x$lex.dur,format="f",digits=digits), adj=c(0.5,-0.5), srt=if(two.scales) 45 else 0, ... ) } ### Generic functions ### Methods for data.frame drop Lexis attributes, so we need a Lexis ### method that adds them again subset.Lexis <- function(x, ...) { y <- subset.data.frame(x, ...) attr(y,"breaks") <- attr(x, "breaks") attr(y,"time.scales") <- attr(x, "time.scales") attr(y,"time.since") <- attr(x, "time.since") return(y) } merge.data.frame <- function(x, y, ...) { if (is.Lexis(x)) merge.Lexis(x, y, ...) else if (is.Lexis(y)) merge.Lexis(y, x, ...) else base::merge.data.frame(x, y, ...) } merge.Lexis <- function(x, y, id, by, ...) { if (!missing(id)) { if (!is.character(id) || length(id) != 1 || !(id %in% names(y))) { stop("id must be the name of a single variable in y") } if (any(duplicated(y[[id]]))) { stop("values of the id variable must be unique in y") } y$lex.id <- y[[id]] } else if (missing(by)) { by <- intersect(names(x), names(y)) if (length(by)==0) { stop("x and y have no variable names in common") } } z <- base::merge.data.frame(x, y, ...) attr(z,"breaks") <- attr(x, "breaks") attr(z,"time.scales") <- attr(x, "time.scales") attr(z,"time.since") <- attr(x, "time.since") class(z) <- c("Lexis", "data.frame") return(z) } ## Extractor functions entry <- function(x, time.scale = NULL, by.id = FALSE ) { time.scale <- check.time.scale(x, time.scale) wh <- x[,time.scale[1]] == ave( x[,time.scale[1]], x$lex.id, FUN=if( by.id ) min else I ) if (length(time.scale) > 1) { res <- as.matrix(x[wh, time.scale]) if( by.id ) rownames( res ) <- x$lex.id[wh] return( res ) } else { res <- x[wh, time.scale] if( by.id ) names( res ) <- x$lex.id[wh] return( res ) } } exit <- function(x, time.scale = NULL, by.id = FALSE ) { time.scale <- check.time.scale(x, time.scale) wh <- x[,time.scale[1]] == ave( x[,time.scale[1]], x$lex.id, FUN=if( by.id ) max else I ) if (length(time.scale) > 1) { res <- as.matrix(x[wh, time.scale]) + x$lex.dur[wh] if( by.id ) rownames( res ) <- x$lex.id[wh] return( res ) } else { res <- x[wh, time.scale] + x$lex.dur[wh] if( by.id ) names( res ) <- x$lex.id[wh] return( res ) } } dur <- function(x, by.id=FALSE) { if( by.id ) return( tapply(x$lex.dur,x$lex.id,sum) ) else return( x$lex.dur ) } status <- function(x, at="exit", by.id = FALSE) { at <- match.arg(at, c("entry","exit")) wh <- x[,timeScales(x)[1]] == ave( x[,timeScales(x)[1]], x$lex.id, FUN=if(by.id) switch(at, "entry"=min, "exit"=max) else I ) res <- switch(at, "entry"=x$lex.Cst, "exit"=x$lex.Xst)[wh] if( by.id ) names( res ) <- x$lex.id[wh] res } timeScales <- function(x) { return (attr(x,"time.scales")) } timeBand <- function(lex, time.scale, type="integer") { time.scale <- check.time.scale(lex, time.scale)[1] breaks <- attr(lex, "breaks")[[time.scale]] time1 <- lex[[time.scale]] band <- findInterval(time1, breaks) ##Check that right hand side of interval falls in the same band abrk <- c(breaks, Inf) tol <- sqrt(.Machine$double.eps) if (any(time1 + lex$lex.dur > abrk[band+1] + tol)) { stop("Intervals spanning multiple time bands in Lexis object") } type <- match.arg(type, choices = c("integer","factor","left","middle", "right")) if (type=="integer") { return(band) } I1 <- c(-Inf, breaks) I2 <- c(breaks, Inf) labels <- switch(type, "factor" = paste("(", I1, ",", I2, "]", sep=""), "left" = I1, "right" = I2, "middle" = (I1 + I2)/2) if(type=="factor") { return(factor(band, levels=0:length(breaks), labels=labels)) } else { return(labels[band+1]) } } breaks <- function(lex, time.scale) { time.scale <- check.time.scale(lex, time.scale)[1] return(attr(lex, "breaks")[[time.scale]]) } transform.Lexis <- function(`_data`, ... ) { save.at <- attributes(`_data`) ## We can't use NextMethod here because of the special scoping rules ## used by transform.data.frame y <- base::transform.data.frame(`_data`, ...) save.at[["names"]] <- attr(y, "names") attributes(y) <- save.at y } Epi/R/ncut.r0000644000175100001440000000074012531361077012350 0ustar hornikusersncut <- function( x, breaks, type="left" ) { # Sorting to get the opportunity to call the function recursively. breaks <- sort( breaks ) # Get the indices, but fix the 0 indices to produce NAs: fi <- findInterval( x, breaks ) fi[fi==0] <- length( breaks ) + 1 switch( toupper( substr( type, 1, 1 ) ), "L" = breaks[fi], "R" = -ncut( -x, -breaks ), "M" = ( breaks[fi] - ncut( -x, -breaks ) ) / 2 ) } Epi/R/Life.lines.R0000644000175100001440000000555212531361077013335 0ustar hornikusersLife.lines <- function( entry.date = NA, exit.date = NA, birth.date = NA, entry.age = NA, exit.age = NA, risk.time = NA ) { # A function allowing any three of the arguments to be specified # and yet returns enty age and -time and exit age and -time. # Check if any variable is supplied with class if( conv <- any( inherits( entry.date, "Date" ), inherits( exit.date, "Date" ), inherits( birth.date, "Date" ), inherits( entry.age , "difftime" ), inherits( exit.age , "difftime" ), inherits( risk.time, "difftime" ) ) ) { # Convert "Date" and "difftime" to years if( inherits( entry.date, "Date" ) ) entry.date <- as.numeric( entry.date ) / 365.35 + 1970 if( inherits( exit.date, "Date" ) ) exit.date <- as.numeric( exit.date ) / 365.35 + 1970 if( inherits( birth.date, "Date" ) ) birth.date <- as.numeric( birth.date ) / 365.35 + 1970 if( inherits( entry.age , "difftime" ) ) entry.age <- as.numeric( entry.age ) / 365.35 if( inherits( exit.age , "difftime" ) ) exit.age <- as.numeric( exit.age ) / 365.35 if( inherits( risk.time, "difftime" ) ) risk.time <- as.numeric( risk.time ) / 365.35 # Convert to numeric class( entry.date ) <- "numeric" class( exit.date ) <- "numeric" class( birth.date ) <- "numeric" class( entry.age ) <- "numeric" class( exit.age ) <- "numeric" class( risk.time ) <- "numeric" } # Find out which three items are supplied. # wh <- (1:6)[!is.na( list( entry.date, entry.age, exit.date, exit.age, birth.date, risk.time ) )] # Matrix of relevant quantities. # LL <- rbind( entry.date, entry.age, exit.date, exit.age, birth.date, risk.time ) # Matrix giving the three constraints among the six quantities: # M <- rbind( c( -1, 1, 0, 0, 1, 0 ), c( 0, 0, -1, 1, 1, 0 ), c( 0, 1, 0, -1, 0, 1 ) ) # Now in principle we have that M %*% LL = 0. # Partitioning M=(A1|A2), t(LL)=(t(x1),t(x2)) # this gives A1 %*% x1 = -A2 %*% x2 # Check if there is sufficient information # if( qr( M[,-wh[1:3]] )$rank < 3 ) cat( "Insufficient information to display life lines" ) # Then do the calculation # A1 <- M[, wh[1:3]] A2 <- M[,-wh[1:3]] x1 <- LL[wh[1:3],] x2 <- -solve( A2 ) %*% A1 %*% x1 LL[-wh[1:3],] <- x2 LL <- data.frame( t(LL) ) attr( LL, "Date" ) <- conv # Convert to dates and difftimes if( conv ) { LL[,c(1,3,5)] <- ( LL[,c(1,3,5)] - 1970 ) * 365.25 LL[,c(2,4,6)] <- LL[,c(2,4,6)] * 365.25 class( LL[,1] ) <- class( LL[,3] ) <- class( LL[,5] ) <- "Date" class( LL[,2] ) <- class( LL[,4] ) <- class( LL[,6] ) <- "difftime" } LL } Epi/R/twoby2.R0000644000175100001440000000700512531361077012566 0ustar hornikuserstwoby2 <- function( exposure, outcome, alpha = 0.05, print = TRUE, dec = 4, conf.level = 1-alpha, F.lim = 10000 ) # What is the limit for trying the # Fisher.test { if( !missing( conf.level ) ) alpha <- 1 - conf.level if( inherits( exposure, c( "table", "matrix" ) ) ) tab <- exposure else tab <- table( exposure, outcome ) tab <- tab[1:2,1:2] a <- tab[1, 1] b <- tab[1, 2] c <- tab[2, 1] d <- tab[2, 2] bin.ci <- function( x, n ) { # Confidence interval for proportion based on Taylor-expansion of # the log-odds --- surprisingly good coverage. ef <- exp( qnorm(1-alpha/2)/sqrt(x*(n-x)/n) ) p <- x / n c( x/n, p/(p+(1-p)*ef), p/(p+(1-p)/ef) ) } rr <- (a/(a + b))/(c/(c + d)) se.log.rr <- sqrt((b/a)/(a + b) + (d/c)/(c + d)) lci.rr <- exp(log(rr) - qnorm( 1-alpha/2 ) * se.log.rr) uci.rr <- exp(log(rr) + qnorm( 1-alpha/2 ) * se.log.rr) or <- (a/b)/(c/d) se.log.or <- sqrt(1/a + 1/b + 1/c + 1/d) lci.or <- exp(log(or) - qnorm( 1-alpha/2 ) * se.log.or) uci.or <- exp(log(or) + qnorm( 1-alpha/2 ) * se.log.or) # Computing the c.i. for the probability difference as method # 10 from Newcombe, Stat.Med. 1998, 17, pp.873 ff. pr.dif <- ci.pd( a, c, b, d, alpha=alpha, print=FALSE )[5:7] pd <- pr.dif[1] lci.pd <- pr.dif[2] uci.pd <- pr.dif[3] as.pval <- 1 - pchisq( log( or )^2 / sum( 1/tab[1:2,1:2] ), 1 ) # If the numers are too large we don't bother about computing Fisher's test Fisher <- ( sum( tab ) < F.lim ) ft <- if( !Fisher ) NA else fisher.test( tab, conf.level=1-alpha ) # We need row and colum names for annotating the output if( is.null( rownames( tab ) ) ) rownames( tab ) <- paste( "Row", 1:2 ) if( is.null( colnames( tab ) ) ) colnames( tab ) <- paste( "Col", 1:2 ) tbl <- cbind( tab[1:2,1:2], rbind( bin.ci( a, a+b ), bin.ci( c, c+d ) ) ) colnames( tbl )[3:5] <- c(paste( " P(", colnames( tab )[1], ")", sep=""), paste( 100*(1-alpha),"% conf.", sep="" ), "interval") if( print ) cat("2 by 2 table analysis:", "\n------------------------------------------------------", "\nOutcome :", colnames( tab )[1], "\nComparing :", rownames( tab )[1], "vs.", rownames( tab )[2], "\n\n" ) if( print ) print( round( tbl, dec ) ) if( print ) cat( "\n" ) rmat <- rbind( c( rr, lci.rr, uci.rr ), c( or, lci.or, uci.or ), if( Fisher) c( ft$estimate, ft$conf.int ), c( pd, lci.pd, uci.pd ) ) rownames( rmat ) <- c(" Relative Risk:", " Sample Odds Ratio:", if( Fisher) "Conditional MLE Odds Ratio:", " Probability difference:") colnames( rmat ) <- c(" ", paste( 100*(1-alpha),"% conf.", sep="" ), "interval" ) if( print ) print( round( rmat, dec ) ) if( print ) cat( if( Fisher ) "\n Exact P-value:", if( Fisher ) round( ft$p.value, dec ), "\n Asymptotic P-value:", round( as.pval, dec ), "\n------------------------------------------------------\n") invisible( list( table = tbl, measures = rmat, p.value = c(as.pval,if( Fisher )ft$p.value) ) ) } Epi/R/rateplot.R0000644000175100001440000001551012531361077013172 0ustar hornikusersrateplot <- function( rates, which = c("ap","ac","pa","ca"), age = as.numeric( dimnames( rates )[[1]] ), per = as.numeric( dimnames( rates )[[2]] ), grid = FALSE, a.grid = grid, p.grid = grid, c.grid = grid, ygrid = grid, col.grid = gray( 0.9 ), a.lim = range( age, na.rm=TRUE ) + c(0,diff( range( age ) )/30), p.lim = range( per, na.rm=TRUE ) + c(0,diff( range( age ) )/30), c.lim = NULL, ylim = range( rates[rates>0], na.rm=TRUE ), at = NULL, labels = paste( at ), a.lab = "Age at diagnosis", p.lab = "Date of diagnosis", c.lab = "Date of birth", ylab = "Rates", type = "l", lwd = 2, lty = 1, log.ax = "y", las = 1, ann = FALSE, a.ann = ann, p.ann = ann, c.ann = ann, xannx = 1/20, cex.ann = 0.8, a.thin = seq( 1, length( age ), 2 ), p.thin = seq( 1, length( per ), 2 ), c.thin = seq( 2, length( age ) + length( per ) - 1, 2 ), col = par( "fg" ), a.col = col, p.col = col, c.col = col, ... ) { # Remove 0 rates, in order to avoid warnings rates[rates==0] <- NA # then do the plots for( i in 1:length( which ) ) { if( toupper( which[i] ) == "AP" ) Aplot( rates, age = age, per = per, a.grid = a.grid, ygrid = ygrid, col.grid = col.grid, a.lim = a.lim, ylim = ylim, a.lab = a.lab, ylab = ylab, at = at, labels = labels, type = type, lwd = lwd, lty = lty, col = col, log.ax = log.ax, las = las, p.ann = p.ann, xannx = xannx, p.col = p.col, cex.ann = cex.ann, p.thin = p.thin, p.lines = TRUE, c.lines = FALSE, ... ) if( toupper( which[i] ) == "AC" ) Aplot( rates, age = age, per = per, a.grid = a.grid, ygrid = ygrid, col.grid = col.grid, a.lim = a.lim, ylim = ylim, a.lab = a.lab, ylab = ylab, at = at, labels = labels, type = type, lwd = lwd, lty = lty, col = col, log.ax = log.ax, las = las, c.ann = c.ann, p.ann = p.ann, xannx = xannx, c.col = c.col, p.col = p.col, cex.ann = cex.ann, c.thin = c.thin, p.lines = FALSE, c.lines = TRUE, ... ) if( toupper( which[i] ) %in% c("APC","ACP") ) Aplot( rates, age = age, per = per, a.grid = a.grid, ygrid = ygrid, col.grid = col.grid, a.lim = a.lim, ylim = ylim, a.lab = a.lab, ylab = ylab, at = at, labels = labels, type = type, lwd = lwd, lty = lty, col = col, log.ax = log.ax, las = las, c.ann = c.ann, p.ann = p.ann, xannx = xannx, c.col = c.col, p.col = p.col, cex.ann = cex.ann, c.thin = c.thin, p.thin = p.thin, p.lines = TRUE, c.lines = TRUE, ... ) if( toupper( which[i] ) == "PA" ) Pplot( rates, age = age, per = per, grid = grid, p.grid = p.grid, ygrid = ygrid, col.grid = col.grid, p.lim = p.lim, ylim = ylim, p.lab = p.lab, ylab = ylab, at = at, labels = labels, type = type, lwd = lwd, lty = lty, col = col, log.ax = log.ax, las = las, ann = a.ann, xannx = xannx, cex.ann = cex.ann, a.thin = a.thin, ... ) if( toupper( which[i] ) == "CA" ) Cplot( rates, age = age, per = per, grid = grid, c.grid = c.grid, ygrid = ygrid, col.grid = col.grid, c.lim = c.lim, ylim = ylim, c.lab = c.lab, ylab = ylab, at = at, labels = labels, type = type, lwd = lwd, lty = lty, col = col, log.ax = log.ax, las = las, ann = a.ann, xannx = xannx, cex.ann = cex.ann, a.thin = a.thin, ... ) } } Epi/R/cal.yr.R0000644000175100001440000000312312531361077012525 0ustar hornikuserscal.yr <- function( x, format = "%Y-%m-%d", wh = NULL ) { cl.typ <- c("Date","POSIXct","POSIXlt","date","dates","chron") # Check if the input is a data frame and convert if( inherits( x, "data.frame" ) & is.null(wh) & missing(format) ) { # Indicator of where a date-type variable is wh <- sapply( x, inherits, cl.typ ) # The positions wh <- (1:length(wh))[wh] } if( inherits( x, "data.frame" ) & is.null(wh) & !missing(format) ) { # Indicator of where the character variables are wh <- sapply( x, is.character ) # The positions wh <- (1:length(wh))[wh] } if( inherits( x, "data.frame" ) & is.vector(wh) ) { if( is.character(wh) ) wh <- match( wh, names(x) ) # Convert the dates or the character variables for( i in wh ) { if( is.character(x[,i]) ) x[,i] <- cal.yr( x[,i], format=format ) else x[,i] <- cal.yr( x[,i] ) } return( x ) } # Finally, down to business --- converting a vector to decimal years: # Check if the input is some kind of date or time object if( any( inherits( x, cl.typ ) ) ) x <- as.Date( as.POSIXct( x ) ) else if( is.character( x ) ) x <- as.Date( x, format = format ) else if( is.factor( x ) ) x <- as.Date( as.character( x ), format = format ) else stop( "\nInput should be a data frame, a character vector, a factor or ", "some kind of date or time object:\n", "Date, POSIXct, POSIXlt, date, dates or chron" ) res <- as.numeric( x ) / 365.25 + 1970 class( res ) <- c("cal.yr","numeric") return( res ) } Epi/R/splitLexis.R0000644000175100001440000000775612531361077013515 0ustar hornikuserssplit.lexis.1D <- function(lex, breaks, time.scale, tol) { time.scale <- check.time.scale(lex, time.scale) ## Entry and exit times on the time scale that we are splitting time1 <- lex[,time.scale, drop=FALSE] time2 <- time1 + lex$lex.dur ## Augment break points with +/- infinity breaks <- sort( unique( breaks ) ) I1 <- c(-Inf, breaks) I2 <- c(breaks,Inf) ## Arrays containing data on each interval (rows) for each subject (cols) en <- apply(time1, 1, pmax, I1) # Entry time ex <- apply(time2, 1, pmin, I2) # Exit time NR <- nrow(en) NC <- ncol(en) ## Does subject contribute follow-up time to this interval? ## (intervals shorter than tol are ignored) valid <- en < ex - tol dur <- ex - en; dur[!valid] <- 0 # Time spent in interval ## Cumulative time since entry at the start of each interval time.since.entry <- rbind(0, apply(dur,2,cumsum)[-NR,,drop=FALSE]) cal.new.entry <- function(entry.time) { sweep(time.since.entry, 2, entry.time, "+")[valid] } old.entry <- lex[, timeScales(lex), drop=FALSE] new.entry <- lapply(old.entry, cal.new.entry) ## Status calculation aug.valid <- rbind(valid, rep(FALSE, NC)) last.valid <- valid & !aug.valid[-1,] any.valid <- apply(valid,2,any) new.Xst <- matrix( lex$lex.Cst, NR, NC, byrow=TRUE) new.Xst[last.valid] <- lex$lex.Xst[any.valid] n.interval <- apply(valid, 2, sum) new.lex <- Lexis("entry" = new.entry, "duration" = dur[valid], "id" = rep(lex$lex.id, n.interval), "entry.status" = rep(lex$lex.Cst, n.interval), "exit.status" = new.Xst[valid]) ## Update breaks attribute and tranfer time.since attribute breaks.attr <- attr(lex, "breaks") breaks.attr[[time.scale]] <- sort(c(breaks.attr[[time.scale]], breaks)) attr(new.lex, "breaks") <- breaks.attr attr(new.lex, "time.since") <- attr(lex, "time.since") return(new.lex) } splitLexis <- function(lex, breaks, time.scale=1, tol= .Machine$double.eps^0.5) { ## Advise the uninformed user... if( inherits(lex,"stacked.Lexis") ) stop( "It makes no sense to time-split after stacking ---\n", "split your original Lexis object and stack that to get what you want.\n") ## Set temporary, unique, id variable lex$lex.tempid <- lex$lex.id lex$lex.id <- 1:nrow(lex) ## Save auxiliary data aux.data.names <- setdiff(names(lex), timeScales(lex)) aux.data.names <- aux.data.names[substr(aux.data.names,1,4) != "lex."] aux.data <- lex[, c("lex.id","lex.tempid", aux.data.names), drop=FALSE] ## Check for NAs in the timescale ts <- check.time.scale(lex, time.scale) ts.miss <- any(is.na(lex[,ts])) if( ts.miss ) { na.lex <- lex[ is.na(lex[,ts]),] lex <- lex[!is.na(lex[,ts]),] cat( "Note: NAs in the time-scale \"", ts, "\", you split on\n") } ## If states are factors convert to numeric while splitting factor.states <- is.factor( lex$lex.Cst ) if( factor.states ) { state.levels <- levels( lex$lex.Cst ) nstates <- nlevels( lex$lex.Cst ) lex$lex.Cst <- as.integer( lex$lex.Cst ) lex$lex.Xst <- as.integer( lex$lex.Xst ) } ## Split the data lex <- split.lexis.1D(lex, breaks, time.scale, tol) ## Reinstitute the factor levels if( factor.states ) { lex$lex.Cst <- factor( lex$lex.Cst, levels=1:nstates, labels=state.levels ) lex$lex.Xst <- factor( lex$lex.Xst, levels=1:nstates, labels=state.levels ) } ## Put the NA-rows back if( ts.miss ) lex <- rbind( lex, na.lex[,colnames(lex)] ) ## Save attributes lex.attr <- attributes(lex) ## Merge lex <- merge.data.frame(lex, aux.data, by="lex.id") ## Restore attributes attr(lex,"breaks") <- lex.attr$breaks attr(lex,"time.scales") <- lex.attr$time.scales attr(lex,"time.since") <- lex.attr$time.since class(lex) <- c("Lexis", "data.frame") ## Restore id variable lex$lex.id <- lex$lex.tempid lex$lex.tempid <- NULL return(lex) } Epi/R/effx.r0000644000175100001440000002541112531361077012331 0ustar hornikusers## Program to calculate effects ## Michael Hills ## Improved by Bendix Carstensen and Martyn Plummer ## Post Tartu 2007 version June 2007 ## Addition allowing a TRUE/FALSE as binary outcome ## and possibility or relative risk for binary outcomes and rate ## difference for failure outcomes, BxC, Ocitober 2012 effx<-function(response, type="metric", fup=NULL, exposure, strata=NULL, control=NULL, weights=NULL, eff=NULL, alpha=0.05, base=1, digits=3, data=NULL) { ## stores the variable names for response, etc. rname<-deparse(substitute(response)) ename<-deparse(substitute(exposure)) if (!missing(strata)) { sname<-deparse(substitute(strata)) } ## The control argument is more complex, as it may be a name or ## list of names if(!missing(control)) { control.arg <- substitute(control) if (length(control.arg) > 1) { control.names <- sapply(control.arg, deparse)[-1] } else { control.names <- deparse(control.arg) } } ## Match the type argument type <- match.arg(type, c("metric", "failure", "count", "binary")) ## Check for missing arguments if (missing(response)) stop("Must specify the response","\n") if (missing(exposure)) stop("Must specify the exposure","\n") if (type == "failure" && missing(fup)) { stop("Must specify a follow-up variable when type is failure") } ## performs a few other checks if(rname==ename)stop("Same variable specified as response and exposure") if (!missing(strata)) { if(rname==sname)stop("Same variable specified as response and strata") if(sname==ename)stop("Same variable specified as strata and exposure") } ## If data argument is supplied, evaluate the arguments in that ## data frame. if (!missing(data)) { exposure <- eval(substitute(exposure), data) response <- eval(substitute(response), data) if (!missing(strata)) strata <- eval(substitute(strata), data) if (!missing(control)) control <- eval(substitute(control), data) if (!missing(fup)) fup <- eval(substitute(fup), data) if (!missing(weights)) { weights <- eval(substitute(weights), data) } } ## Now check validity of evaluated arguments if(is.logical(response)) response <- as.numeric(response) if(!is.numeric(response)) stop("Response must be numeric, not a factor") if (!missing(weights) && type != "binary") { stop("weights only allowed for a binary response") } if (!missing(strata) && !is.factor(strata)) stop("Stratifying variable must be a factor") if(type=="binary") { if( is.null(eff) ) eff<-"OR" if( !(eff %in% c("OR","RR") ) ) stop( "Only RR and OR allowed for binary response" ) response <- as.numeric(response) tmp<-(response==0 | response==1) if(all(tmp,na.rm=TRUE)==FALSE) stop("Binary response must be logical or coded 0,1 or NA") } # If a count is given we are actually using the fup if(type=="count") fup<-is.na(response)*1 if(type=="failure") { if( is.null(eff) ) eff<-"RR" response <- as.numeric(response) tmp<-(response==0 | response==1) if(all(tmp,na.rm=TRUE)==FALSE) stop("Failure response must be logical or coded 0,1 or NA") } ## If exposure is an ordered factor, drops the order. if(class(exposure)[1]=="ordered") { exposure<-factor(exposure, ordered=F) } ## Fix up the control argument as a named list if (!missing(control)) { if (is.list(control)) { names(control) <- control.names } else { control <- list(control) names(control) <- control.names } } ## prints out some information about variables cat("---------------------------------------------------------------------------","\n") cat("response : ", rname, "\n") cat("type : ", type, "\n") cat("exposure : ", ename, "\n") if(!missing(control))cat("control vars : ",names(control),"\n") if(!missing(strata)) { cat("stratified by : ",sname,"\n") } cat("\n") if(is.factor(exposure)) { cat(ename,"is a factor with levels: ") cat(paste(levels(exposure),collapse=" / "),"\n") exposure <- Relevel( exposure, base ) cat( "baseline is ", levels( exposure )[1] ,"\n") } else { cat(ename,"is numeric","\n") } if(!missing(strata)) { cat(sname,"is a factor with levels: ") cat(paste(levels(strata),collapse="/"),"\n") } if(type=="metric")cat("effects are measured as differences in means","\n") if(type=="binary") { if( eff=="OR" | is.null(eff))cat("effects are measured as odds ratios","\n") if( eff=="RR" )cat("effects are measured as relative risk","\n") } if(type=="failure") { if( eff=="RR" | is.null(eff))cat("effects are measured as rate ratios","\n") if( eff=="RD" )cat("effects are measured as rate differences","\n") } cat("---------------------------------------------------------------------------","\n") cat("\n") ## translates type of response and choice of eff into family if ( type=="metric") family<-gaussian if ( type=="binary") family<-binomial(link=logit) if ( type=="failure" | type=="count") family<-poisson(link=log) if ( !is.null(eff) ) { if (type=="binary" & eff=="RR") family<-binomial(link=log) if (type=="failure" & eff=="RD") family<-poisson(link=identity) } ## gets number of levels for exposure if a factor if(is.factor(exposure)) { nlevE<-length(levels(exposure)) } ## labels the output if(is.factor(exposure)) { cat("effect of",ename,"on",rname,"\n") } else { cat("effect of an increase of 1 unit in",ename,"on",rname,"\n") } if(!missing(control)) { cat("controlled for",names(control),"\n\n") } if(!missing(strata)) { cat("stratified by",sname,"\n\n") } ## no stratifying variable if(missing(strata)) { if(type=="metric") { if(missing(control)) { m<-glm(response~exposure,family=family) cat("number of observations ",m$df.null+1,"\n\n") mm<-glm(response~1,family=family,subset=!is.na(exposure)) } else { m<-glm(response~.+exposure,family=family, subset=!is.na(exposure),data=control) cat("number of observations ",m$df.null+1,"\n\n") mm<-glm(response~.,family=family, subset=!is.na(exposure),data=control) } res<-ci.lin(m,subset=c("Intercept","exposure"),alpha=alpha) res<-res[,c(1,5,6)] } if(type=="binary") { if(missing(control)) { m<-glm(response~exposure,family=family,weights=weights) cat("number of observations ",m$df.null+1,"\n\n") mm<-glm(response~1,family=family,subset=!is.na(exposure),weights=weights) } else { m<-glm(response~.+exposure,family=family, subset=!is.na(exposure),data=control,weights=weights) cat("number of observations ",m$df.null+1,"\n\n") mm<-glm(response~.,family=family, subset=!is.na(exposure),data=control,weights=weights) } res<-ci.lin(m,subset=c("Intercept","exposure"),Exp=TRUE,alpha=alpha) res<-res[,c(5,6,7)] } if (type=="failure" | type=="count") { if (missing(control)) { m<-glm(response/fup~exposure,weights=fup,family=family) cat("number of observations ",m$df.null+1,"\n\n") mm<-glm(response/fup~1,weights=fup,family=family, subset=!is.na(exposure)) } else { m<-glm(response/fup~.+exposure,weights=fup,family=family, data=control) cat("number of observations ",m$df.null+1,"\n\n") mm<-glm(response/fup~.,weights=fup,family=family, subset=!is.na(exposure),data=control) } res<-ci.exp(m,subset=c("Intercept","exposure"),alpha=alpha,Exp=(eff=="RR")) } res<-signif(res,digits) colnames(res)[1]<-c("Effect") if(is.factor(exposure)) { ln <- levels(exposure) rownames(res)[2:nlevE]<-paste(ln[2:nlevE],"vs",ln[1]) } aov <- anova(mm,m,test="Chisq") print( res[-1,] ) cat("\nTest for no effects of exposure on", aov[2,3],"df:", "p-value=",format.pval(aov[2,5],digits=3),"\n") invisible(list(res,paste("Test for no effects of exposure on", aov[2,3],"df:","p-value=",format.pval(aov[2,5],digits=3)))) } ## stratifying variable if(!missing(strata)) { sn <- levels(strata) nlevS<-length(levels(strata)) if(type=="metric") { if(missing(control)) { m<-glm(response~strata/exposure,family=family) cat("number of observations ",m$df.null+1,"\n\n") mm<-glm(response~strata+exposure,family=family) } else { m <-glm(response~strata/exposure + .,family=family, data=control) cat("number of observations ",m$df.null+1,"\n\n") mm <-glm(response~strata+exposure + .,family=family, data=control) } res<-ci.lin(m,subset=c("strata"),alpha=alpha)[c(-1:-(nlevS-1)),c(1,5,6)] } if(type=="binary") { if(missing(control)) { m<-glm(response~strata/exposure,family=family,weights=weights) cat("number of observations ",m$df.null+1,"\n\n") mm<-glm(response~strata+exposure,family=family,weights=weights) } else { m <-glm(response~strata/exposure + .,family=family, data=control,weights=weights) cat("number of observations ",m$df.null+1,"\n\n") mm <-glm(response~strata+exposure + .,family=family, data=control,weights=weights) } res<-ci.exp(m,subset=c("strata"),alpha=alpha)[c(-1:-(nlevS-1)),] } if (type=="failure" | type=="count") { if(missing(control)) { m<-glm(response/fup~strata/exposure,weights=fup,family=family) cat("number of observations ",m$df.null+1,"\n\n") mm<-glm(response~strata+exposure,weights=fup,family=family) } else { m <-glm(response/fup~.+strata/exposure,weights=fup,family=family,data=control) cat("number of observations ",m$df.null+1,"\n\n") mm<-glm(response/fup~.+strata+exposure,weights=fup,family=family,data=control) } res<-ci.exp(m,subset=c("strata"),alpha=alpha)[c(-1:-(nlevS-1)),] } res<-signif(res,digits) colnames(res)[1]<-c("Effect") if(is.factor(exposure)) { ln<-levels(exposure) newrownames<-NULL for(i in c(1:(nlevE-1))) { newrownames<-c(newrownames, paste("strata",sn[1:nlevS],"level",ln[i+1],"vs",ln[1])) } } else { newrownames<-paste("strata",sn[1:nlevS]) } rownames(res)<-newrownames aov<-anova(mm,m,test="Chisq") print( res ) cat("\nTest for effect modification on", aov[2,3],"df:","p-value=",format.pval(aov[2,5],digits=3),"\n") invisible(list(res,paste("Test for effect modification on", aov[2,3],"df:","p-value=",format.pval(aov[2,5],digits=3)))) } } Epi/R/N2Y.r0000644000175100001440000000423712531361077012014 0ustar hornikusersN2Y <- function( A, P, N, data=NULL, return.dfr=TRUE ) { # Make local versions of variables if a dataframe is supplied if( !is.null(data) ) { A <- if( !missing(A) ) eval( substitute(A), data, parent.frame() ) else data$A P <- if( !missing(P) ) eval( substitute(P), data, parent.frame() ) else data$P N <- if( !missing(N) ) eval( substitute(N), data, parent.frame() ) else data$N } # Derive the interval lengths from supplied data A.int <- unique( diff(sort(unique(A))) ) P.int <- unique( diff(sort(unique(P))) ) # Check if something is fishy if( length(A.int)!=1 ) stop( "Non-uniform age interval lengths:\n", A.int ) if( length(P.int)!=1 ) stop( "Non-uniform period interval lengths:\n", P.int ) if( A.int!=P.int ) stop( "Unequal age and period interval lengths:\n", "age: ", A.int, ", period: ", P.int ) # Put population prevalence data in a table Ntab <- xtabs( N ~ A + P ) # Devise a table for the risk times Ydim <- c(dimnames(Ntab),list(wh=c("lo","up"))) # note one less age and period category Ytab <- array( NA, dim=sapply(Ydim,length), dimnames = Ydim )[-dim(Ntab)[1],-dim(Ntab)[2],] # How manu age and period classes na <- nrow(Ytab) np <- ncol(Ytab) for(a in 1:na) for(p in 1:np) { Ytab[a,p,"up"] <- Ntab[a ,p]/3 + Ntab[a+1,p+1]/6 if( a > 1) Ytab[a,p,"lo"] <- Ntab[a-1,p]/6 + Ntab[a ,p+1]/3 else Ytab[a,p,"lo"] <- Ntab[a ,p]/2 + Ntab[a ,p+1]/2 - Ytab[a,p,"up"] } # Remember to check the follow-up time Ytab <- Ytab * A.int # Convert to a data frame if required (the default) if( return.dfr ) { ## If a dataframe is required as return value Ytab <- data.frame(expand.grid(dimnames(Ytab)), Y=c(Ytab)) ## Retrieve the numerical values of left endpoints of intervals Ytab$A <- as.numeric(as.character(Ytab$A)) Ytab$P <- as.numeric(as.character(Ytab$P)) ## Compute the correct midpoints from the supplied data Ytab$A <- Ytab$A + A.int * (1 + (Ytab$wh == "up"))/3 Ytab$P <- Ytab$P + P.int * (1 + (Ytab$wh == "lo"))/3 Ytab <- Ytab[, c("A","P","Y")] } Ytab } Epi/R/contr.orth.R0000644000175100001440000000040712531361077013437 0ustar hornikuserscontr.orth <- function( n ) { if( is.numeric( n ) && length( n )==1 ) levs <- 1:n else { levs <- n n <- length( n ) } Z <- contr.sum( n ) L <- 1:n - mean(1:n) contr <- Z - L%*%( ( t(L) %*% L )^(-1) ) %*% ( t(L) %*% Z ) contr[,1:(n-2)] } Epi/R/stattable.R0000644000175100001440000003440412531361077013326 0ustar hornikusersstat.table <- function(index, contents=count(), data, margins=FALSE) { ## Get names of indices, using either user-supplied names, or by ## deparsing. Some of this code is stolen from the list.names() ## function in table() index.sub <- substitute(index) index <- if (missing(data)) eval(index) else eval(index.sub, data) deparse.name <- function(x) if(is.symbol(x)) as.character(x) else "" if (is.list(index)) { if (is.call(index.sub)) { ## List constructed in the call to stat.table index.names <- names(index.sub) fixup <- if (is.null(index.names)) seq(along = index.sub) else index.names == "" dep <- sapply(index.sub[fixup], deparse.name) if (is.null(index.names)) index.labels <- dep else { index.labels <- index.names index.labels[fixup] <- dep } index.labels <- index.labels[-1] } else { ## List constructed outside the call to stat.table index.labels <- if (!is.null(names(index))) { names(index) } else { rep("", length(index)) } } } else { ## A single vector index.labels <- deparse.name(index.sub) } ## Coerce index to list of factors if (!is.list(index)) index <- list(index) index <- lapply(index, as.factor) ## Coerce contents to an expression representing a list of function calls contents <- substitute(contents) if (!identical(deparse(contents[[1]]), "list")) { contents <- call("list", contents) } ## Check that functions in the contents list are valid valid.functions <- c("count","mean","weighted.mean","sum","quantile", "median","IQR","max","min","ratio","percent","sd") table.fun <- character(length(contents) - 1) for (i in 2:length(contents)) { if (!is.call(contents[[i]])) stop("contents must be a list of function calls") FUN <- deparse(contents[[i]][[1]]) if (!FUN %in% valid.functions) stop(paste("Function",FUN,"not permitted in stat.table")) else table.fun[i-1] <- FUN } ## Label the contents by default with the function call ## But if user has supplied a label use that instead. stat.labels <- sapply(contents, deparse)[-1] content.names <- names(contents) if (!is.null(content.names)) { for (i in 2:length(content.names)) { if (nchar(content.names[i]) > 0) stat.labels[i-1] <- content.names[i] } } ##Define the allowed tabulation functions count <- function(id){ if (missing(id)) { id <- seq(along=index[[1]]) } y <- tapply(id, INDEX=subindex, FUN=function(x) length(unique(x))) y[is.na(y)] <- 0 return(y) } mean <- function(x, trim=0, na.rm=TRUE) { tapply(x, INDEX=subindex, FUN = base::mean, trim=trim, na.rm=na.rm) } weighted.mean <- function(x,w,na.rm=TRUE) { tapply(x, INDEX=subindex, FUN=stats::weighted.mean, w=w, na.rm=na.rm) } sum <- function(...,na.rm=TRUE) { tapply(..., INDEX=subindex, FUN = base::sum, na.rm=na.rm) } quantile <- function(x, probs, na.rm=TRUE,names=TRUE,type=7,...) { if (length(probs) > 1) stop("The quantile function only accepts scalar prob values within stat.table") tapply(x, INDEX=subindex, FUN = stats::quantile, probs=probs, na.rm=na.rm,names=names,type=type,...) } median <- function(x, na.rm=TRUE) { tapply(x, INDEX=subindex, FUN = stats::median, na.rm=na.rm) } IQR <- function(x, na.rm=TRUE) { tapply(x, INDEX=subindex, FUN= stats::IQR, na.rm=na.rm) } max <- function(..., na.rm=TRUE) { tapply(..., INDEX=subindex, FUN = base::max, na.rm=na.rm) } min <- function(..., na.rm=TRUE) { tapply(..., INDEX=subindex, FUN = base::min, na.rm=na.rm) } ratio <- function(d,y,scale=1, na.rm=TRUE) { if (length(scale) != 1) stop("Scale parameter must be a scalar") if (na.rm) { w <- (!is.na(d) & !is.na(y)) tab1 <- tapply(d*w, INDEX=subindex, FUN=base::sum, na.rm=TRUE) tab2 <- tapply(y*w, INDEX=subindex, FUN=base::sum, na.rm=TRUE) } else { tab1 <- tapply(d, INDEX=subindex, FUN=base::sum, na.rm=FALSE) tab2 <- tapply(y, INDEX=subindex, FUN=base::sum, na.rm=FALSE) } return(scale*tab1/tab2) } percent <- function(...) { x <- list(...) if (length(x) == 0) stop("No variables to calculate percent") x <- lapply(x, as.factor) n <- count() ## Work out which indices to sweep out sweep.index <- logical(length(subindex)) for (i in seq(along=subindex)) { sweep.index[i] <- !any(sapply(x,identical,subindex[[i]])) } if (!any(sweep.index)) { return(100*n/base::sum(n, na.rm=TRUE)) } else { margin <- apply(n,which(sweep.index),base::sum, na.rm=TRUE) margin[margin==0] <- NA return(100*sweep(n, which(sweep.index), margin,"/")) } } sd <- function (..., na.rm = TRUE) { tapply(..., INDEX=subindex, FUN = stats::sd, na.rm=na.rm) } ##Calculate dimension of the main table, excluding margins n.dim <- length(index) tab.dim <- sapply(index, nlevels) ##Sort out margins if (length(margins) == 1) margins <- rep(margins, n.dim) else if(length(margins) != n.dim) stop("Incorrect length for margins argument") ##Create grid of all possible subtables. fac.list <- vector("list", n.dim) for (i in 1:n.dim) { fac.list[[i]] <- if (margins[i]) c(0,1) else 1 } subtable.grid <- as.matrix(expand.grid(fac.list)) ##Fill in the subtables ans.dim <- c(length(contents)-1, tab.dim + margins) ans <- numeric(prod(ans.dim)) for (i in 1:nrow(subtable.grid)) { ##in.subtable is a logical vector indicating which dimensions are ##in the subtable (i.e. which have not been marginalized out) in.subtable <- as.logical(subtable.grid[i,]) llim <- rep(1,n.dim) + ifelse(in.subtable,rep(0,n.dim),tab.dim) ulim <- tab.dim + ifelse(in.subtable,rep(0,n.dim),rep(1, n.dim)) subindex <- index[in.subtable] if (length(subindex) == 0) { ## Marginalizing out all dimensions subindex <- list(rep(1, length(index[[1]]))) } subtable.list <- if(missing(data)) ##eval(contents, parent.frame()) eval(contents) else eval(as.expression(contents), data) for (j in 1:length(subtable.list)) { ans[array.subset(ans.dim,c(j,llim),c(j,ulim))] <- subtable.list[[j]] } } ans <- array(ans, dim=ans.dim) ans.dimnames <- lapply(index, levels) names(ans.dimnames) <- index.labels for (i in 1:length(index)) { if (margins[i]) ans.dimnames[[i]] <- c(ans.dimnames[[i]], "Total") } dimnames(ans) <- c(list("contents"=stat.labels), ans.dimnames) attr(ans, "table.fun") <- table.fun class(ans) <- c("stat.table", class(ans)) return(ans) } array.subset <- function(dim,lower,upper) { ##Returns a logical array of dimension dim for which elements in the range ##[lower[1]:upper[1], lower[2]:upper[2],...] are TRUE and others FALSE ##Check validity of arguments (but assume everything is an integer) ndim <- length(dim) if (length(lower) != ndim || length(upper) != ndim) { stop("Length of lower and upper limits must match dimension") } if (any(lower > upper) || any(lower < 1) || any(upper > dim)) { stop("Invalid limits") } ##The math is easier if we index arrays from 0 rather than 1 lower <- lower - 1 upper <- upper - 1 N <- prod(dim) ans <- rep(TRUE, N) for (i in 1:N) { l <- i - 1 for (d in 1:ndim) { k <- l %% dim[d] #k is the index of the ith element in dimension d if (k < lower[d] || k > upper[d]) { ans[i] <- FALSE break } l <- l %/% dim[d] } } return(array(ans, dim)) } split.to.width <- function(x,width) { ## Splits a string into a vector so that each element has at most width ## characters. If width is smaller than the length of the shortest word ## then the latter is used instead x.split <- strsplit(x,split=" ")[[1]] width <- max(c(width,nchar(x.split))) y <- character(0) imin <- 1 n <- length(x.split) for (i in 1:n) { cum.width <- if(i==n) { Inf } else { sum(nchar(x.split[imin:(i+1)])) + (i - imin + 1) } if (cum.width > width) { y <- c(y,paste(x.split[imin:i], collapse=" ")) imin <- i + 1 } } return(y) } pretty.print.stattable.1d <- function(x, width, digits) { ##Pretty printing of 1-D stat.table if (length(dim(x)) != 2) stop("Cannot print stat.table") ncol <- nrow(x) col.width <- numeric(ncol+1) col.header <- vector("list",ncol+1) n.header <- integer(ncol+1) print.list <- vector("list",ncol+1) ##First column col.header[[1]] <- split.to.width(names(dimnames(x))[2], width) n.header[1] <- length(col.header[[1]]) col1 <- format(c(col.header[[1]],dimnames(x)[[2]]), justify="left") col.header[[1]] <- col1[1:n.header[1]] print.list[[1]] <- col1[-(1:n.header[1])] col.width[1] <- nchar(col.header[[1]][1]) ##Other columns for (i in 2:(ncol+1)) { col.header[[i]] <- split.to.width(dimnames(x)[[1]][i-1], width) n.header[i] <- length(col.header[[i]]) this.col <- formatC(x[i-1,],width=width, digits=digits[attr(x,"table.fun")[i-1]], "f") this.col <- format(c(col.header[[i]],this.col),justify="right") col.width[i] <- nchar(this.col[1]) col.header[[i]] <- this.col[1:n.header[i]] print.list[[i]] <- this.col[-(1:n.header[i])] } ## table.width <- sum(col.width) + ncol + 3 max.n.header <- max(n.header) cat(" ",rep("-",table.width)," \n",sep="") for(i in 1:max.n.header) { cat(" ") for(j in 1:length(print.list)) { if (i <= n.header[j]) { cat(col.header[[j]][i]) } else { cat(rep(" ", col.width[[j]]),sep="") } if (j==1) cat(" ") else cat(" ") } cat(" \n") } cat(" ",rep("-",table.width)," \n",sep="") for (i in 1:length(print.list[[1]])) { cat(" ") if (pmatch("Total",print.list[[1]][i],nomatch=0)) { ##Add a blank line before the total cat(rep(" ",col.width[1]+1)," ",rep(" ",sum(col.width[-1])+ncol), " \n ",sep="") } for (j in 1:length(print.list)) { cat(print.list[[j]][i]) if (j == 1) { cat(" " ) } else { cat(" ") } } cat(" \n") } cat(" ",rep("-",table.width)," \n",sep="") return(invisible(x)) } pretty.print.stattable.2d <- function(x, width, digits) { ##Pretty printing of 2-Dimensional stat.table if (length(dim(x)) != 3) stop("Cannot print stat.table") nstat <- dim(x)[1] ncol <- dim(x)[3] nrow <- dim(x)[2] col.width <- numeric(ncol+1) col.header <- vector("list",ncol+1) n.header <- integer(ncol+1) print.list <- vector("list",ncol+1) ##First column col.header[[1]] <- split.to.width(names(dimnames(x))[2], width) n.header[1] <- length(col.header[[1]]) col1 <- format(c(col.header[[1]],dimnames(x)[[2]]), justify="left") col.header[[1]] <- col1[1:n.header[1]] print.list[[1]] <- col1[-(1:n.header[1])] col.width[1] <- nchar(col.header[[1]][1]) ##Other columns for (i in 2:(ncol+1)) { col.header[[i]] <- split.to.width(dimnames(x)[[3]][i-1], width) n.header[i] <- length(col.header[[i]]) this.col <- matrix("", nrow=nstat,ncol=nrow) for (j in 1:nstat) { z <- x[j,,i-1] this.col[j,] <- formatC(z, width=width, format="f", digits=digits[attr(x,"table.fun")[j]]) ## this.col[j,] <- formatC(z, width=width, digits=digits, ## format=ifelse(identical(round(z),z),"d","f")) } this.col <- format(c(col.header[[i]],this.col),justify="right") col.width[i] <- nchar(this.col[1]) col.header[[i]] <- this.col[1:n.header[i]] print.list[[i]] <- this.col[-(1:n.header[i])] } ##Correct first column for multiple stats if (nstat > 1) { pl1 <- print.list[[1]] print.list[[1]] <- rep(paste(rep(" ",col.width[1]),collapse=""),nstat*nrow) print.list[[1]][1 + nstat*((1:nrow)-1)] <- pl1 } table.width <- sum(col.width) + ncol + 3 max.n.header <- max(n.header) cat(" ",rep("-",table.width)," \n",sep="") ## Supercolumn header super.header <- names(dimnames(x))[3] npad <- sum(col.width[-1]) + ncol + 1 - nchar(super.header) if (npad >= 0) { cat(" ",rep(" ",col.width[1])," ",sep="") cat(rep("-",floor(npad/2)),sep="") cat(super.header) cat(rep("-",ceiling(npad/2))," \n",sep="") } ## Headers for(i in 1:max.n.header) { cat(" ") for(j in 1:length(print.list)) { if (i <= n.header[j]) { cat(col.header[[j]][i]) } else { cat(rep(" ", col.width[[j]]),sep="") } if (j==1) cat(" ") else cat(" ") } cat(" \n") } cat(" ",rep("-",table.width)," \n",sep="") ## Body of table blank.line <- function() { cat(" ",rep(" ",col.width[1]+1)," ",rep(" ",sum(col.width[-1])+ncol), " \n",sep="") } for (i in 1:length(print.list[[1]])) { if (pmatch("Total",print.list[[1]][i],nomatch=0)) { ##Add a blank line before the total blank.line() } cat(" ") for (j in 1:length(print.list)) { cat(print.list[[j]][i]) if (j == 1) { cat(" " ) } else { cat(" ") } } cat(" \n") if (nstat > 1 && i %% nstat == 0 && i != length(print.list[[1]])) { ##Separate interleaved stats blank.line() } } cat(" ",rep("-",table.width)," \n",sep="") return(invisible(x)) } print.stat.table <- function(x, width=7,digits,...) { fun.digits <- c("count"=0,"mean"=2,"weighted.mean"=2,"sum"=2,"quantile"=2, "median"=2,"IQR"=2,"max"=2,"min"=2,"ratio"=2,"percent"=1, "sd"=2) if (!missing(digits)) { if (is.null(names(digits))) { if (length(digits) > 1) stop("digits must be a scalar or named vector") else fun.digits[1:length(fun.digits)] <- digits } else { fun.digits[names(digits)] <- digits } } if (length(dim(x)) == 2) pretty.print.stattable.1d(x, width, fun.digits) else if (length(dim(x)) == 3) pretty.print.stattable.2d(x, width, fun.digits) else NextMethod("print",...) } ## Satisfy QA checks by defining these functions. But if we never ## export them they can't be used directly. count <- function(id) { } ratio <- function(d, y, scale=1, na.rm=TRUE) { } percent <- function(...) { } Epi/R/factorize.R0000644000175100001440000000547112531361077013333 0ustar hornikusers# The factorize method factorize <- function (x, ...) UseMethod("factorize") # Default method is just the Relevel method factorize.default <- Relevel.default # The Lexis version of this Relevel.Lexis <- factorize.Lexis <- function (x, states=NULL, print=TRUE, ... ) { # Is this really a Lexis object if( !inherits(x,"Lexis") ) stop( "First argument must be a Lexis object" ) # If lex.Cst and lex.Xst are not factors, make them: if( !is.factor(x$lex.Cst) | !is.factor(x$lex.Xst) ) { Cst <- factor(x$lex.Cst) Xst <- factor(x$lex.Xst) } else # just use the factors as they are { Cst <- x$lex.Cst Xst <- x$lex.Xst } # - but amend them to have the same sety of levels all.levels = union(levels(Cst), levels(Xst)) Cst <- factor(Cst, levels = all.levels) Xst <- factor(Xst, levels = all.levels) # A table of actually occurring levels and their names tCX <- table(Cst) + table(Xst) all.levels <- names( tCX[tCX>0] ) # If states are not given, just return factors with reduced levels if( is.null(states) ) { x$lex.Cst <- factor( Cst, levels = all.levels ) x$lex.Xst <- factor( Xst, levels = all.levels ) } # If new state names are given as a list it implies merging of them if( !is.null( states ) & is.list( states ) ) { x$lex.Cst <- Relevel( Cst, states, ... ) x$lex.Xst <- Relevel( Xst, states, ... ) if( print ) { # Construct translation table between old and grouped states to print tC <- table( Cst, x$lex.Cst ) tX <- table( Xst, x$lex.Xst ) cC <- matrix( colnames(tC), nrow(tC), ncol(tC), byrow=T ) cX <- matrix( colnames(tX), nrow(tX), ncol(tX), byrow=T ) cC[tC==0] <- "" cX[tX==0] <- "" print( data.frame( type=rep( c("lex.Cst","lex.Xst"), c(nrow(tC),nrow(tX)) ), old=c(rownames(tC),rownames(tX)), new=c( apply( cC, 1, paste, collapse="" ), apply( cX, 1, paste, collapse="" ) ) ) ) } } # If states is a character vector we assume that it's just new names if( !is.null( states ) & is.character( states ) ) { if( length( states ) != nlevels(Cst) ) stop( "Second argument is a vector of length ", length(states), ", but it should be the joint no. of states, ", length(all.levels), "\ncorresponding to ", all.levels ) levels( Cst ) <- levels( Xst ) <- states x$lex.Cst <- Cst x$lex.Xst <- Xst if( print ) { cat( "New levels for lex.Xst and lex.Cst generated:\n" ) print( data.frame( old=all.levels, new=levels(x$lex.Cst) ) ) } } # If states is a numeric vector we assume that it's just reordering if( !is.null( states ) & is.numeric( states ) ) { x$lex.Cst <- Relevel( Cst, states ) x$lex.Xst <- Relevel( Xst, states ) } return( x ) } Epi/R/apc.fit.R0000644000175100001440000003122012531546026012657 0ustar hornikusersapc.fit <- function( data, A, P, D, Y, ref.c, ref.p, dist = c("poisson","binomial"), model = c("ns","bs","ls","factor"), dr.extr = c("weighted","Holford"), parm = c("ACP","APC","AdCP","AdPC","Ad-P-C","Ad-C-P","AC-P","AP-C"), npar = c( A=5, P=5, C=5 ), scale = 1, alpha = 0.05, print.AOV = TRUE ) { dist <- match.arg(dist) model <- match.arg(model) drtyp <- match.arg(dr.extr) parm <- toupper(match.arg(parm)) if(!missing(data)) { if (length(match(c("A", "P", "D", "Y"), names(data))) != 4) stop("Data frame ", deparse(substitute(data)), " has columns:\n", names(data), "\nmust have variables:\n", "A (age), P (period), D (cases) and Y (person-time)") data <- data[,c("A","P","D","Y")] data <- data[complete.cases(data),] A <- data$A P <- data$P D <- data$D Y <- data$Y } else { nm <- logical(4) nm[1] <- missing(A) nm[2] <- missing(P) nm[3] <- missing(D) nm[4] <- missing(Y) if (any(nm)) stop("Variable", if (sum(nm) > 1) "s", paste(c(" A", " P", " D", " Y")[nm], collapse = ","), " missing from input") if( diff(range( lv <- sapply( list(A = A, P = P, D = D, Y = Y), length) ) ) != 0 ) stop( "\nLengths of variables (", paste(paste(names(lv), lv, sep = ":"), collapse = ", "), ") are not the same." ) } med <- function(x, y) { # Computes where the median number of ys is on the x scale o <- order(x) a <- y[o] names(a) <- x[o] return( as.numeric(names(a[cumsum(a)/sum(a) > 0.5][1])) ) } p0 <- ifelse(missing(ref.p), med(P, D), ref.p) c0 <- ifelse(missing(ref.c), med(P - A, D), ref.c) ref.p <- !missing(ref.p) ref.c <- !missing(ref.c) if( is.list(npar) & length(npar)<3 ) stop("npar as a list should have length 3! \n") if( !is.list(npar) & length(npar)!=3 ) { npar <- rep(npar, 3)[1:3] names(npar) = c("A","P","C") cat("NOTE: npar is specified as:") print( npar ) } if( is.null(names(npar)) ) names(npar) <- c("A", "P", "C") lu <- paste(formatC(c(alpha/2, 1 - alpha/2) * 100, format = "f", digits = 1), "%", sep = "") proj.ip <- function(X, M, orth = FALSE, weight = rep(1, nrow(X))) { if (nrow(X) != length(weight)) stop("Dimension of space and length of weights differ!") if (nrow(X) != nrow(M)) stop("Dimension of space and rownumber of model matrix differ!") Pp <- solve(crossprod(X * sqrt(weight)), t(X * weight)) %*% M PM <- X %*% Pp if (orth) PM <- M - PM else PM } Thin.col <- function(X, tol = 1e-06) { QR <- qr(X, tol = tol, LAPACK = FALSE) X[, QR$pivot[seq(length = QR$rank)], drop = FALSE] } detrend <- function(M, t, weight = rep(1, nrow(M))) { Thin.col(proj.ip(cbind(1, t), M, orth = TRUE, weight = weight)) } if (is.list(model)) { if (!all(sapply(model, is.function))) stop("'model' is a list, but not all elements are functions as they should be.") if ((lmod <- length(model)) < 3) stop("'model' is a list, with", lmod, "elements, it should have three.") if (is.null(names(model))) names(model) <- c("A", "P", "C") MA <- model[["A"]](A) MP <- model[["P"]](P) MC <- model[["C"]](P - A) Rp <- model[["P"]](p0) Rc <- model[["C"]](c0) } else { if (model == "factor") { MA <- model.matrix(~factor(A) - 1) MP <- model.matrix(~factor(P) - 1) MC <- model.matrix(~factor(P - A) - 1) Rp <- MP[abs(P - p0) == min(abs(P - p0)), , drop = FALSE][1, ] Rc <- MC[abs(P - A - c0) == min(abs(P - A - c0)), , drop = FALSE][1, ] } if (model == "ns") { knl <- is.list(npar) MA <- if (knl) Ns(A, knots = npar[["A"]] ) else Ns(A, knots = quantile( rep(A,D), probs=(0:npar["A"]+0.1)/(npar["A"]+0.2) ) ) MP <- if (knl) Ns(P, knots = npar[["P"]] ) else Ns(P, knots = quantile( rep(P,D), probs=(0:npar["P"]+0.1)/(npar["P"]+0.2) ) ) MC <- if (knl) Ns(P - A, knots = npar[["C"]] ) else Ns(P-A, knots = quantile( rep(P-A,D), probs=(0:npar["C"]+0.1)/(npar["C"]+0.2) ) ) Rp <- ns(p0, knots = attr(MP,"knots"), Boundary.knots = attr(MP,"Boundary.knots")) Rc <- ns(c0, knots = attr(MC,"knots"), Boundary.knots = attr(MC,"Boundary.knots")) Knots <- list( Age = sort(c(attr(MA,"knots"), attr(MA,"Boundary.knots"))), Per = sort(c(attr(MP,"knots"), attr(MP,"Boundary.knots"))), Coh = sort(c(attr(MC,"knots"), attr(MC,"Boundary.knots")))) } if (model %in% c("bs", "ls")) { deg <- switch(model, ls = 1, bs = 3) knl <- is.list(npar) if (knl) nk <- sapply(npar, length) MA <- if (knl) bs(A, knots = npar[["A"]][-c(1,nk[1])], Boundary.knots = npar[["A"]][ c(1,nk[1])], degree = deg) else bs(A, df = npar[["A"]], degree = deg) MP <- if (knl) bs(P, knots = npar[["P"]][-c(1,nk[2])], Boundary.knots = npar[["P"]][ c(1,nk[2])], degree = deg) else bs(P, df = npar[["P"]], degree = deg) MC <- if (knl) bs(P - A, knots = npar[["C"]][-c(1, nk[3])], Boundary.knots = npar[["C"]][c(1, nk[3])], degree = deg) else bs(P - A, df = npar[["C"]], degree = deg) Rp <- bs(p0, knots = attr(MP,"knots"), Boundary.knots = attr(MP,"Boundary.knots"), degree = attr(MP,"degree")) Rc <- bs(c0, knots = attr(MC,"knots"), Boundary.knots = attr(MC,"Boundary.knots"), degree = attr(MC,"degree")) Knots <- list(Age = sort(c(attr(MA,"knots"),attr(MA,"Boundary.knots"))), Per = sort(c(attr(MP,"knots"),attr(MP,"Boundary.knots"))), Coh = sort(c(attr(MC,"knots"),attr(MC,"Boundary.knots")))) } } if (tolower(substr(dist, 1, 2)) == "po") { m.APC <- glm(D ~ MA + I(P - p0) + MP + MC, offset = log(Y), family = poisson) Dist <- "Poisson with log(Y) offset" } if (tolower(substr(dist, 1, 3)) %in% c("bin")) { m.APC <- glm(cbind(D, Y - D) ~ MA + I(P - p0) + MP + MC, family = binomial) Dist <- "Binomial regression (logistic) of D/Y" } m.AP <- update(m.APC, . ~ . - MC) m.AC <- update(m.APC, . ~ . - MP) m.Ad <- update(m.AP, . ~ . - MP) m.A <- update(m.Ad, . ~ . - I(P - p0)) m.0 <- update(m.A, . ~ . - MA) AOV <- anova(m.A, m.Ad, m.AC, m.APC, m.AP, m.Ad, test = "Chisq") attr(AOV, "heading") <- "\nAnalysis of deviance for Age-Period-Cohort model\n" attr(AOV, "row.names") <- c("Age", "Age-drift", "Age-Cohort", "Age-Period-Cohort", "Age-Period", "Age-drift") A.pt <- unique(A) A.pos <- match(A.pt, A) P.pt <- unique(P) P.pos <- match(P.pt, P) C.pt <- unique(P - A) C.pos <- match(C.pt, P - A) MA <- cbind(1, MA) if (!mode(drtyp) %in% c("character", "numeric")) stop("\"dr.extr\" must be of mode \"character\" or \"numeric\".\n") if (is.character(drtyp)) wt <- if (toupper(substr(drtyp, 1, 1)) == "W") D else rep(1, length(D)) if (is.numeric(drtyp)) wt <- drtyp Rp <- matrix(Rp, nrow = 1) Rc <- matrix(Rc, nrow = 1) xP <- detrend(rbind(Rp, MP), c(p0, P), weight = c(0, wt)) xC <- detrend(rbind(Rc, MC), c(c0, P - A), weight = c(0, wt)) MPr <- xP[-1,,drop=FALSE] - ref.p * xP[rep(1, nrow(MP)),,drop=FALSE] MCr <- xC[-1,,drop=FALSE] - ref.c * xC[rep(1, nrow(MC)),,drop=FALSE] if (length(grep("-", parm)) == 0) { if (parm %in% c("ADPC", "ADCP", "APC", "ACP")) m.APC <- update(m.0, . ~ . - 1 + MA + I(P - p0) + MPr + MCr) drift <- rbind( ci.exp(m.APC, subset = "I\\(", alpha = alpha), ci.exp(m.Ad , subset = "I\\(", alpha = alpha) ) rownames(drift) <- c("APC", "A-d") if (parm == "ADCP") m.APC <- update(m.0, . ~ . - 1 + MA + I(P - A - c0) + MPr + MCr) if (parm == "APC") { MPr <- cbind(P - p0, MPr) m.APC <- update(m.0, . ~ . - 1 + MA + MPr + MCr) } if (parm == "ACP") { MCr <- cbind(P - A - c0, MCr) m.APC <- update(m.0, . ~ . - 1 + MA + MPr + MCr) } Age <- cbind(Age = A.pt, ci.exp(m.APC, subset = "MA", ctr.mat = MA[A.pos,,drop=FALSE], alpha = alpha))[order(A.pt),] Per <- cbind(Per = P.pt, ci.exp(m.APC, subset = "MPr", ctr.mat = MPr[P.pos,,drop=FALSE], alpha = alpha))[order(P.pt),] Coh <- cbind(Coh = C.pt, ci.exp(m.APC, subset = "MCr", ctr.mat = MCr[C.pos,,drop=FALSE], alpha = alpha))[order(C.pt),] colnames(Age)[-1] <- c("Rate", lu) colnames(Per)[-1] <- c("P-RR", lu) colnames(Coh)[-1] <- c("C-RR", lu) Type <- paste("ML of APC-model", Dist, ": (", parm, "):\n") Model <- m.APC } else { adc <- update(m.0, . ~ . - 1 + MA + I(P - A - c0)) adp <- update(m.0, . ~ . - 1 + MA + I(P - p0)) drift <- ci.exp(adc, subset = "I\\(") rownames(drift) <- "A-d" xP <- cbind(1, P - p0, MPr) xC <- cbind(1, P - A - c0, MCr) lP <- cbind(P - p0, MPr) lC <- cbind(P - A - c0, MCr) if (parm == "AD-C-P") { rc <- update(m.0, . ~ . - 1 + xC, offset = predict(adc, type = "link")) rp <- update(m.0, . ~ . - 1 + xP, offset = predict(adc, type = "link")) A.eff <- ci.exp(adc, subset = "MA", ctr.mat = MA[A.pos,], alpha = alpha) C.eff <- ci.exp( rc, subset = "xC", ctr.mat = xC[C.pos,], alpha = alpha) P.eff <- ci.exp( rp, subset = "xP", ctr.mat = xP[P.pos,], alpha = alpha) Model <- list( adc, rc, rp ) } else if (parm == "AD-P-C") { rp <- update(m.0, . ~ . - 1 + xP, offset = predict(adp,type = "link")) rc <- update(m.0, . ~ . - 1 + xC, offset = predict(rp,type = "link")) A.eff <- ci.exp(adp, subset = "MA", ctr.mat = MA[A.pos,], alpha = alpha) P.eff <- ci.exp(rp, subset = "xP", ctr.mat = xP[P.pos,], alpha = alpha) C.eff <- ci.exp(rc, subset = "xC", ctr.mat = xC[C.pos,], alpha = alpha) Model <- list( adp, rp, rc ) } else if (parm == "AC-P") { ac <- update(m.0, . ~ . - 1 + MA + lC) rp <- update(m.0, . ~ . - 1 + xP, offset = predict(ac,type = "link")) A.eff <- ci.exp(ac, subset = "MA", ctr.mat = MA[A.pos,], alpha = alpha) C.eff <- ci.exp(ac, subset = "lC", ctr.mat = lC[C.pos,], alpha = alpha) P.eff <- ci.exp(rp, subset = "xP", ctr.mat = xP[P.pos,], alpha = alpha) Model <- list( ac, rp ) } else if (parm == "AP-C") { ap <- update(m.0, . ~ . - 1 + MA + lP) rc <- update(m.0, . ~ . - 1 + xC, offset = predict(ap,type = "link")) A.eff <- ci.exp(ap, subset = "MA", ctr.mat = MA[A.pos,], alpha = alpha) P.eff <- ci.exp(ap, subset = "lP", ctr.mat = lP[P.pos,], alpha = alpha) C.eff <- ci.exp(rc, subset = "xC", ctr.mat = xC[C.pos,], alpha = alpha) Model <- list( ap, rc ) } Age <- cbind(Age = A.pt, A.eff)[order(A.pt),] Per <- cbind(Per = P.pt, P.eff)[order(P.pt),] Coh <- cbind(Cph = C.pt, C.eff)[order(C.pt),] colnames(Age)[-1] <- c("A.eff", lu) colnames(Per)[-1] <- c("P.eff", lu) colnames(Coh)[-1] <- c("C.eff", lu) Type <- paste("Sequential modelling", Dist, ": (", parm, "):\n") } res <- list(Type = Type, Model = Model, Age = Age, Per = Per, Coh = Coh, Drift = drift, Ref = c(Per = if ( parm %in% c("APC","ADPC","Ad-P-C","AP-C") ) p0 else NA, Coh = if ( parm %in% c("ACP","ADCP","Ad-C-P","AC-P") ) c0 else NA ), Anova = AOV) # If a spline model is used, add a "Knots" component to the apc-object if (model %in% c("ns", "bs")) res <- c(res, list(Knots = Knots)) res$Age[, -1] <- res$Age[, -1] * scale if (print.AOV) { print(res$Type) print(res$Anova) } # Print warnings about reference points: if( !ref.p & parm %in% c("APC","ADPC") ) cat( "No reference period given:\n", "Reference period for age-effects is chosen as\n", "the median date of event: ", p0 ) if( !ref.c & parm %in% c("ACP","ADCP") ) cat( "No reference period given:\n", "Reference period for age-effects is chosen as\n", "the median date of birth for persons with event: ", c0 ) class(res) <- "apc" invisible(res) } Epi/R/ROC.R0000644000175100001440000002211312531361077011760 0ustar hornikuserssteplines <- function( x, y, left = TRUE, right = !left, order = TRUE, ... ) { # A function to plot step-functions # # Get the logic right if right is supplied... left <- !right # ... right! n <- length( x ) if( any( order ) ) ord <- order(x) else ord <- 1:n dbl <- rep( 1:n, rep( 2, n) ) xv <- c( !left, rep( T, 2*(n-1) ), left) yv <- c( left, rep( T, 2*(n-1) ), !left) lines( x[ord[dbl[xv]]], y[ord[dbl[yv]]], ... ) } interp <- function ( target, fv, res ) { # Linear interpolaton of the values in the N by 2 matrix res, # to the target value target on the N-vector fv. # Used for placing tickmarks on the ROC-curves. # where <- which( fv>target )[1] - 1:0 int <- fv[where] wt <- ( int[2] - target ) / diff( int ) wt[2] <- 1-wt t( res[where,] ) %*% wt } ROC.tic <- function ( tt, txt = formatC(tt,digits=2,format="f"), dist = 0.02, angle = +135, col = "black", cex = 1.0, fv, res ) { # Function for drawing tickmarks on a ROC-curve # for (i in 1:length(tt)) { pnt <- interp ( tt[i], fv, res ) x <- 1-pnt[2] y <- pnt[1] lines( c( x, x+dist*cos(pi*angle/180) ), c( y, y+dist*sin(pi*angle/180) ), col=col ) text( x+dist*cos(pi*angle/180), y+dist*sin(pi*angle/180), txt[i], col=col, adj=c( as.numeric(abs(angle)>=90), as.numeric( angle <= 0)), cex=cex ) } } ROC <- function ( test = NULL, stat = NULL, form = NULL, plot = c( "sp", "ROC" ), PS = is.null(test), # Curves on probability scale PV = TRUE, # sn, sp, PV printed at "optimality" point MX = TRUE, # tick at "optimality" point MI = TRUE, # Model fit printed AUC = TRUE, # Area under the curve printed grid = seq(0,100,10), # Background grid (%) col.grid = gray( 0.9 ), cuts = NULL, lwd = 2, data = parent.frame(), ... ) { # First all the computations # # Name of the response rnam <- if ( !missing( test ) ) deparse( substitute( test ) ) else "lr.eta" # Fit the model and get the info for the two possible types of input if( is.null( form ) ) { if( is.null( stat ) | is.null( test ) ) stop( "Either 'test' AND 'stat' OR 'formula' must be supplied!" ) lr <- glm( stat ~ test, family=binomial )#, data=data ) resp <- stat Model.inf <- paste("Model: ", deparse( substitute( stat ) ), "~", deparse( substitute( test ) ) ) } else { lr <- glm(form, family = binomial, data = data) resp <- eval( parse(text = deparse(form[[2]])), envir=lr$model ) Model.inf <- paste("Model: ",paste(paste(form)[c(2,1,3)], collapse=" ")) } # Form the empirical distribution function for test for each of # the two categories of resp. # First a table of the test (continuous variable) vs. the response and # adding a row of 0s so that we have all points fro the ROC curve m <- as.matrix( base::table( switch( PS+1, test, lr$fit ), resp ) ) m <- addmargins( rbind( 0, m ), 2 ) # What values of test/eta do the rows refer to fv <- c( -Inf, sort( unique( switch( PS+1, test, lr$fit ) ) ) ) # How many rows in this matrix nr <- nrow(m) # Calculate the empirical distribution functions (well, cumulative numbers): m <- apply( m, 2, cumsum ) # Then the relevant measures are computed. sns <- (m[nr,2]-m[,2]) / m[nr,2] spc <- m[,1] / m[nr,1] pvp <- m[,2] / m[,3] pvn <- (m[nr,1]-m[,1]) / ( m[nr,3]-m[,3] ) res <- data.frame( cbind( sns, spc, pvp, pvn, fv ) ) names( res ) <- c( "sens", "spec", "pvp", "pvn", rnam ) # AUC by triangulation auc <- sum( (res[-1,"sens"]+res[-nr,"sens"])/2 * abs(diff(1-res[,"spec"])) ) # Plot of sens, spec, PV+, PV-: if ( any( !is.na( match( c( "SP", "SNSP", "SPV" ), toupper( plot ) ) ) ) ) { # First for probability scale if ( PS ) { plot( 0:1, 0:1, xlim=0:1, xlab="Cutpoint for predicted probability", ylim=0:1, ylab=" ", type="n" ) if( is.numeric( grid ) ) abline( h=grid/100, v=grid/100, col=col.grid ) box() for ( j in 4:1 ){ steplines( fv, res[,j], lty=1, lwd=lwd, col=gray((j+1)/7)) } text( 0, 1.01, "Sensitivity", cex=0.7, adj=c(0,0), font=2 ) text( 1, 1.01, "Specificity", cex=0.7, adj=c(1,0), font=2 ) text( 0, m[nr,2]/m[nr,3]-0.01, "PV+", cex=0.7, adj=c(0,1), font=2 ) text( 0 + strwidth( "PV+", cex=0.7 ), m[nr,2]/m[nr,3]-0.01, paste( " (= ", m[nr,2],"/", m[nr,3], " =", formatC( 100*m[nr,2]/m[nr,3], digits=3 ), "%)", sep=""), adj=c(0,1), cex=0.7 ) text( 1, 1-m[nr,2]/m[nr,3]-0.01, "PV-", cex=0.7, adj=c(1,1), font=2 ) } # then for test-variable scale else { xl <- range( test ) plot( xl, 0:1, xlim=xl, xlab=paste( deparse( substitute( test ) ), "(quantiles)" ), ylim=0:1, ylab=" ", type="n" ) if( is.numeric( grid ) ) abline( h=grid/100, v=quantile( test, grid/100 ), col=col.grid ) box() for ( j in 4:1 ){ steplines( fv, res[,j], lty=1, lwd=lwd, col=gray((j+1)/7))} text( xl[1], 1.01, "Sensitivity", cex=0.7, adj=c(0,0), font=2 ) text( xl[2], 1.01, "Specificity", cex=0.7, adj=c(1,0), font=2 ) text( xl[1], m[nr,2]/m[nr,3]-0.01, "PV+", cex=0.7, adj=c(0,1), font=2 ) text( xl[1] + strwidth( "PV+", cex=0.7 ), m[nr,2]/m[nr,3]-0.01, paste( " (= ", m[nr,2],"/", m[nr,3], " =", formatC( 100*m[nr,2]/m[nr,3], digits=3 ), "%)", sep=""), adj=c(0,1), cex=0.7 ) text( xl[2], 1-m[nr,2]/m[nr,3]-0.01, "PV-", cex=0.7, adj=c(1,1), font=2 ) } } # Plot of ROC-curve: if ( any( !is.na( match( "ROC", toupper( plot ) ) ) ) ) { plot( 1-res[,2], res[,1], xlim=0:1, xlab="1-Specificity", ylim=0:1, ylab= "Sensitivity", type="n", ...) if( is.numeric( grid ) ) abline( h=grid/100, v=grid/100, col=gray( 0.9 ) ) abline( 0, 1, col=gray( 0.4 ) ) box() lines( 1-res[,"spec"], res[,"sens"], lwd=lwd ) # Tickmarks on the ROC-curve if ( !is.null(cuts) ) { ROC.tic( cuts, txt=formatC( cuts, digits=2, format="f" ), fv=fv, res=res, dist=0.03, cex=0.7) } # Plot of optimality point if (MX) { mx <- max( res[,1]+res[,2] ) mhv <- which( (res[,1]+res[,2])==mx )[1] mxf <- fv[mhv] abline( mx-1, 1, col=gray(0.4) ) ROC.tic( mxf, txt=paste( rnam, "=", formatC( mxf, format="f", digits=3 ) ), fv=fv, res=res, dist=0.03, cex=0.7, angle=135 ) } # Model information if (MI) { crn <- par()$usr text(0.95*crn[2]+0.05*crn[1], 0.07, Model.inf, adj=c(1,0.5),cex=0.7) cf <- summary(lr)$coef[,1:2] nf <- dimnames(cf)[[1]] text(0.95*crn[2]+0.05*crn[1], 0.10, paste("Variable\ \ \ \ \ \ est.\ \ \ \ \ (s.e.) \ \ \n", paste(rbind(nf, rep("\ \ \ \ ",length(nf)), formatC(cf[,1],digits=3,format="f"), rep("\ \ \ (",length(nf)), formatC(cf[,2],digits=3,format="f"), rep(")",length(nf)), rep("\n",length(nf))), collapse=""), collapse=""), adj=c(1,0), cex=0.7 ) } # Print the area under the curve if (AUC) { crn <- par()$usr text( 0.95*crn[2]+0.05*crn[1], 0.00, paste( "Area under the curve:", formatC( auc, format="f", digits=3, width=5 ) ), adj=c(1,0), cex=0.7 ) } # Predictive values at maximum if (PV) { if(!MX) { mx <- max(res[,1]+res[,2]) mhv <- which((res[,1]+res[,2])==mx) mxf <- fv[mhv] } ROC.tic(mxf, fv=fv, res=res, txt= paste( "Sens: ", formatC(100*res[mhv,1],digits=1,format="f"), "%\n", "Spec: ", formatC(100*res[mhv,2],digits=1,format="f"), "%\n", "PV+: ", formatC(100*res[mhv,3],digits=1,format="f"), "%\n", "PV-: ", formatC(100*res[mhv,4],digits=1,format="f"), "%", sep="" ), dist=0.1, cex=0.7, angle=-45 ) } } invisible( list( res=res, AUC=auc, lr=lr ) ) } Epi/R/ci.mat.R0000644000175100001440000000045012531361077012510 0ustar hornikusersci.mat <- function( alpha=0.05, df=Inf ) { ciM <- rbind( c(1,1,1), qt(1-alpha/2,df)*c(0,-1,1) ) colnames( ciM ) <- c("Estimate", paste( formatC( 100* alpha/2 , format="f", digits=1 ), "%", sep="" ), paste( formatC( 100*(1-alpha/2), format="f", digits=1 ), "%", sep="" ) ) ciM } Epi/R/effx.match.r0000644000175100001440000001531612531361077013427 0ustar hornikusers## Program to calculate effects for matched case-control studies ## Michael Hills ## Improved by BxC and MP ## Post Tartu 2007 version June 2007 effx.match<-function(response, exposure, match, strata=NULL, control=NULL, base=1, digits=3, alpha=0.05, data=NULL) { ## stores the variable names for response, etc. rname<-deparse(substitute(response)) ename<-deparse(substitute(exposure)) if (!missing(strata))sname<-deparse(substitute(strata)) ## The control argument is more complex, as it may be a name or ## list of names if(!missing(control)) { control.arg <- substitute(control) if (length(control.arg) > 1) { control.names <- sapply(control.arg, deparse)[-1] } else { control.names <- deparse(control.arg) } } ## If data argument is supplied, evaluate the arguments in that ## data frame. if (!missing(data)) { exposure <- eval(substitute(exposure), data) response <- eval(substitute(response), data) match <- eval(substitute(match),data) if (!missing(strata)) { strata <- eval(substitute(strata), data) } if (!missing(control)) control <- eval(substitute(control), data) } ## performs a few other checks if(rname==ename)stop("Same variable specified as response and exposure") if (!missing(strata)) { if(rname==sname)stop("Same variable specified as response and strata") if(sname==ename)stop("Same variable specified as strata and exposure") } if(!is.numeric(response))stop("Response must be numeric, not a factor") if(!missing(strata)&!is.factor(strata))stop("Stratifying variable must be a factor") tmp<-(response==0 | response==1) if(all(tmp,na.rm=TRUE)==FALSE) stop("Binary response must be coded 0,1 or NA") if(class(exposure)[1]=="ordered") { exposure<-factor(exposure, ordered=F) } ## Fix up the control argument as a named list if (!missing(control)) { if (is.list(control)) { names(control) <- control.names } else { control <- list(control) names(control) <- control.names } } ## prints out some information about variables cat("---------------------------------------------------------------------------","\n") cat("response : ", rname, "\n") cat("exposure : ", ename, "\n") if(!missing(control))cat("control vars : ",names(control),"\n") if(!missing(strata)) cat("stratified by : ",sname,"\n") cat("\n") if(is.factor(exposure)) { cat(ename,"is a factor with levels: ") cat(paste(levels(exposure),collapse=" / "),"\n") cat( "baseline is ", levels( exposure )[base] ,"\n") exposure <- Relevel( exposure, base ) } else { cat(ename,"is numeric","\n") } if(!missing(strata)) { cat(sname,"is a factor with levels: ") cat(paste(levels(strata),collapse="/"),"\n") } cat("effects are measured as odds ratios","\n") cat("---------------------------------------------------------------------------","\n") cat("\n") ## gets number of levels for exposure if a factor if(is.factor(exposure)) { nlevE<-length(levels(exposure)) } else { nlevE<-1 } ## labels the output if(is.factor(exposure)) { cat("effect of",ename,"on",rname,"\n") } else { cat("effect of an increase of 1 unit in",ename,"on",rname,"\n") } if(!missing(control)) { cat("controlled for",names(control),"\n\n") } if(!missing(strata)) { cat("stratified by",sname,"\n\n") } ## no stratifying variable if(missing(strata)) { if(missing(control)) { m<-clogit(response~exposure+strata(match)) cat("number of observations ",m$n,"\n\n") } else { m<-clogit(response~.+exposure+strata(match), subset=!is.na(exposure),data=control) cat("number of observations ",m$n,"\n\n") mm<-clogit(response~.+strata(match), subset=!is.na(exposure),data=control) } res<-ci.lin(m,subset=c("exposure"),Exp=TRUE,alpha=alpha)[,c(5,6,7)] res<-signif(res,digits) if(nlevE<3) { names(res)[1]<-c("Effect") } else { colnames(res)[1]<-c("Effect") if(is.factor(exposure)) { ln <- levels(exposure) rownames(res)[1:nlevE-1]<-paste(ln[2:nlevE],"vs",ln[1]) } } print(res) if(missing(control)) { chisq<-round(summary(m)$logtest[1],2) df<-round(summary(m)$logtest[2]) p<-round(summary(m)$logtest[3],3) cat("\n") cat("Test for no effects of exposure: ","\n") cat("chisq=",chisq, " df=",df, " p-value=",format.pval(p,digits=3),"\n") invisible(list(res,paste("Test for no effects of exposure on", df,"df:","p=",format.pval(p,digits=3)))) } else { aov <- anova(mm,m,test="Chisq") cat("\nTest for no effects of exposure on", aov[2,3],"df:", "p-value=",format.pval(aov[2,5],digits=3),"\n") invisible(list(res,paste("Test for no effects of exposure on", aov[2,3],"df:","p=",format.pval(aov[2,5],digits=3)))) } } ## stratifying variable if(!missing(strata)) { sn <- levels(strata) nlevS<-length(levels(strata)) if(missing(control)) { m<-clogit(response~strata/exposure+strata(match)) cat("number of observations ",m$n,"\n\n") mm<-clogit(response~strata+exposure+strata(match)) } else { m <-clogit(response~strata/exposure + . +strata(match), data=control) cat("number of observations ",m$n,"\n\n") mm <-clogit(response~strata+exposure + . +strata(match), data=control) } res<-ci.lin(m,Exp=TRUE,alpha=alpha,subset="strata")[c(-1:-(nlevS-1)),c(5,6,7)] res<-signif(res,digits) colnames(res)[1]<-c("Effect") if(is.factor(exposure)) { ln<-levels(exposure) newrownames<-NULL for(i in c(1:(nlevE-1))) { newrownames<-c(newrownames, paste("strata",sn[1:nlevS],"level",ln[i+1],"vs",ln[1])) } } else { newrownames<-paste("strata",sn[1:nlevS]) } rownames(res)<-newrownames aov<-anova(mm,m,test="Chisq") print( res ) cat("\nTest for effect modification on", aov[2,3],"df:","p-value=",format.pval(aov[2,5],digits=3),"\n") invisible(list(res,paste("Test for effect modification on", aov[2,3],"df:","p-value=",format.pval(aov[2,5],digits=3)))) } } Epi/R/apc.frame.R0000644000175100001440000000506712531361077013202 0ustar hornikusersapc.frame <- function( a.lab, cp.lab, r.lab, rr.lab = r.lab / rr.ref, rr.ref = r.lab[length(r.lab)/2], a.tic = a.lab, cp.tic = cp.lab, r.tic = r.lab, rr.tic = r.tic / rr.ref, tic.fac = 1.3, a.txt = "Age", cp.txt = "Calendar time", r.txt = "Rate per 100,000 person-years", rr.txt = "Rate ratio", ref.line = TRUE, gap = diff(range(c(a.lab,a.tic)))/10, col.grid = gray( 0.85 ), sides = c(1,2,4) ) { cp <- min( c(cp.tic,cp.lab) ) - max( c(a.lab,a.tic) ) - gap xl <- c(min( c(a.lab,a.tic) ), max( c(a.lab,a.tic) ) + gap + diff( range( c(cp.lab,cp.tic) ) ) ) yl <- range( c(r.lab,r.tic) ) rrtck <- outer( c(0.5,1,1.5,2:9), 10^(-5:5), "*" ) # Empty plot frame plot( NA, xlab="", xlim=xl, xaxt="n", xaxs="i", ylab="", ylim=yl, yaxt="n", yaxs="i", log="y" ) # Grid lines abline( h=c(r.tic,outer( c(0.5,1,1.5,2:9), 10^(-5:5), "*" )), v=c(a.tic,cp.tic - cp), col=col.grid ) # Reference line for the RR=1 if ( ref.line ) segments( min(c(cp.lab,cp.tic))-cp, rr.ref, max(c(cp.lab,cp.tic))-cp, rr.ref ) # Close it nicely off: box() # Axis construction (tickmarks, labels and annotation) if ( 1 %in% sides ) { axis( side=1, at=a.lab ) axis( side=1, at=a.tic, labels=NA, tcl=par("tcl")/tic.fac ) axis( side=1, at=cp.lab - cp, labels=cp.lab ) axis( side=1, at=cp.tic - cp, labels=NA, tcl=par("tcl")/tic.fac ) axis( side=1, at=mean( a.lab ), labels=a.txt, line=1, tcl=0 ) axis( side=1, at=mean( cp.lab - cp ), labels=cp.txt, line=1, tcl=0 ) } if ( 2 %in% sides ) { axis( side=2, at=r.lab, labels=paste( r.lab ) ) axis( side=2, at=r.tic, labels=NA, tcl=par("tcl")/tic.fac ) mtext( side=2, r.txt, line=2.5, las=0 ) } if ( 3 %in% sides ) { axis( side=3, at=a.lab ) axis( side=3, at=a.tic, labels=NA, tcl=par("tcl")/tic.fac ) axis( side=3, at=cp.lab - cp, labels=cp.lab ) axis( side=3, at=cp.tic - cp, labels=NA, tcl=par("tcl")/tic.fac ) axis( side=3, at=mean( a.lab ), labels=a.txt, line=1, tcl=0 ) axis( side=3, at=mean( cp.lab - cp ), labels=cp.txt, line=1, tcl=0 ) } if ( 4 %in% sides ) { axis( side=4, at=c(rr.ref,rr.lab*rr.ref), labels=paste( c(1,rr.lab) ) ) axis( side=4, at=c(rr.ref,rr.tic*rr.ref), labels=NA, tcl=par("tcl")/tic.fac ) mtext( side=4, rr.txt, line=2.5, las=0 ) } # Return the offset for the cohort/period and the RR-factor. options( apc.frame.par = c("cp.offset"=cp,"RR.fac"=rr.ref) ) invisible( c("cp.offset"=cp,"RR.fac"=rr.ref) ) } Epi/R/Ns.r0000644000175100001440000000503612562636173011770 0ustar hornikusers#------------------------------------------------------------------------------- # A wrapper for ns that automatically takes the smallest and largest knots # as boundary knots without further ado, but also allows cenering # around a reference and detrending by means of projection Ns <- function( x, ref = NULL, df = NULL, knots = NULL, intercept = FALSE, Boundary.knots = NULL, detrend = FALSE ) { ## Check sensibility of arguments if( !is.null(ref) ) { if( !is.vector(ref) ) stop( "Argument 'ref' must be a scalar, but it is a ", class(ref), "." ) if( is.vector(ref) & length(ref)>1 ) stop( "Argument 'ref' must be a scalar, but has length ", length(ref), "." ) if( intercept ) { warning( "ref= specified, hence intercept=TRUE is ignored") intercept <- FALSE } } ## Detrending required? if( any(detrend>0) ) ## covers both logical and vector { if( any(detrend<0) ) stop( "Some elements of weight are <0, e.g. no", (ww <- which(detrend<0))[1:min(5,length(ww))], "." ) if( !(length(detrend) %in% c(1,length(x))) ) { warning( "Weights in inner product diagonal matrix set to 1") weight <- rep(1,length(x)) } else weight <- if( is.numeric(detrend) ) detrend else rep(1,length(x)) detrend <- TRUE } if( detrend & intercept ) { warning( "detrend= specified, hence intercept=TRUE is ignored") intercept <- FALSE } ## Here is the specification of the spline basis ## df= specified if( !is.null(df) ) MM <- ns( x, df = df, intercept = (intercept & is.null(ref)) ) else ## knots= specified { if( is.null( Boundary.knots ) ) { if( !is.null( knots ) ) { knots <- sort( unique( knots ) ) ok <- c(1,length(knots)) Boundary.knots <- knots[ok] knots <- knots[-ok] } } MM <- ns( x, knots = knots, Boundary.knots = Boundary.knots, intercept = (intercept & is.null(ref)) ) } ## Reference point specified ? if( !is.null(ref) ) { MM <- MM - ns( rep(ref,length(x)), knots = attr(MM,"knots"), Boundary.knots = attr(MM,"Boundary.knots") ) } ## Detrending required ? if( detrend ) { DD <- detrend( MM, x, weight=weight ) ## NOTE: detrend does not preserve attributes for( aa in c("degree","knots","Boundary.knots","intercept","class") ) attr( DD, aa ) <- attr( MM, aa ) attr( DD, "detrend" ) <- TRUE attr( DD, "proj.wt" ) <- weight MM <- DD } return( MM ) } Epi/R/Termplot.R0000644000175100001440000000341412562641313013144 0ustar hornikusersTermplot <- function( obj, plot = TRUE, xlab = NULL, ylab = NULL, xeq = TRUE, yshr = 1.0, alpha = 0.05, terms = NULL, max.pt = NULL ) { # max.pt suppiled no.max <- missing( max.pt ) # Extract the curves to plot zz <- termplot( obj, se=TRUE, plot=FALSE, terms=terms ) nt <- length( zz ) for( i in 1:nt ) { # Thin the number of points in each returned term if( no.max ) max.pt <- nrow( zz[[i]] ) if( is.numeric(max.pt) & (nrow(zz[[i]]) > max.pt) ) zz[[i]] <- zz[[i]][round(seq(1,nrow(zz[[i]]),,max.pt)),] # Compute the estimate and the c.i. on log-scale zz[[i]] <- cbind( zz[[i]][,1], exp(as.matrix(zz[[i]][,2:3])%*%ci.mat(alpha=alpha)) ) } # Labels if( is.null(xlab) ) xlab <- names( zz ) if( is.null(ylab) ) ylab <- rep("",nt) ## Compute ranges of y and x xw <- sapply( zz, function(x) diff(range(x[,1 ])) ) yl <- sapply( zz, function(x) range(x[,2:4]) ) mr <- max( apply( yl, 2, function(x) exp(diff(log(x)))) ) yl <- apply( yl, 2, function(x) exp(mean(log(x))+c(-1,1)*log(mr)/2*yshr)) if( plot ) { ## Plot the effects side by side par( mfrow=c(1,nt), mar=c(3,3,1,1), mgp=c(3,1,0)/1.6 )#, bty="n", las=1 ) if( xeq ) ## Plot the terms so that x- and y-axes have the same extent { # Margins and total width in inches mw <- sum(par("mai")[c(2,4)]) tw <- par("pin")[1] # Widths of each plot, approx. at least pw <- (tw-nt*mw)*xw/sum(xw)+mw layout( rbind(1:nt), widths=pw ) } for( i in 1:nt ) matplot( zz[[i]][,1], zz[[i]][,2:4], xlab=xlab[i], xaxs="i", xlim=range(zz[[i]][,1]), ylab=ylab[i], yaxs="i", ylim=yl[,i], log="y", type="l", lty=1, lwd=c(5,2,2), col="black" ) } ## Return the extracted terms invisible( zz ) } Epi/R/foreign.R0000644000175100001440000000355312531545751013000 0ustar hornikusers# The msdata method msdata <- function (obj, ...) UseMethod("msdata") msdata.Lexis <- function( obj, time.scale = timeScales(obj)[1], ... ) { tr.mat <- tmat(obj) # Essentially a msdata object is a stacked Lexis object with other variable names tmp <- stack.Lexis( factorize.Lexis(obj) ) lv <- c( match(timeScales(obj),names(tmp)), grep("lex\\." ,names(tmp)) ) # The transitions that we refer to are extracted from lex.Tr: ss <- strsplit( as.character(tmp$lex.Tr), "->" ) # The resulting dataframe is created by renaming columns in the stacked Lexis object data.frame( id = tmp$lex.id, from = sapply( ss, FUN=function(x) x[1] ), to = sapply( ss, FUN=function(x) x[2] ), trans = as.integer( tmp$lex.Tr ), Tstart = tmp[,time.scale], Tstop = tmp[,time.scale] + tmp$lex.dur, time = tmp$lex.dur, status = as.integer( tmp$lex.Fail ), tmp[,-lv] ) } # The etm method etm <- function (obj, ...) UseMethod("etm") etm.data.frame <- function (obj, ...) { etm::etm( data=obj, ... ) } etm.Lexis <- function( obj, time.scale = timeScales(obj)[1], cens.name = "cens", s = 0, t = "last", covariance = TRUE, delta.na = TRUE, ... ) { dfr <- data.frame( id = obj$lex.id, from = as.character(obj$lex.Cst), to = as.character(obj$lex.Xst), entry = obj[,time.scale], exit = obj[,time.scale] + obj$lex.dur, stringsAsFactors = FALSE ) dfr$to <- with( dfr, ifelse( from==to, cens.name, to ) ) etm::etm( data = dfr, state.names = levels( obj$lex.Cst ), tra = tmat(obj,mode="logical"), cens.name = cens.name, s = s, t = t, covariance = covariance, delta.na = delta.na ) } Epi/R/contr.diff.R0000644000175100001440000000057512531361077013401 0ustar hornikuserscontr.diff <- function(n) { if (is.numeric(n) && length(n) == 1) levs <- 1:n else { levs <- n n <- length(n) } contr <- array(0, c(n, n), list(levs, levs)) contr[col(contr) == row(contr)] <- 1 contr[row(contr) - col(contr) == 1] <- -1 if (n < 2) stop(paste("Contrasts not defined for", n - 1, "degrees of freedom")) contr } Epi/R/ci.cum.R0000644000175100001440000000524112554144544012521 0ustar hornikusersci.cum <- function( obj, ctr.mat = NULL, subset = NULL, intl = 1, alpha = 0.05, Exp = TRUE, ci.Exp = FALSE, sample = FALSE ) { # First extract all the coefficients and the variance-covariance matrix cf <- COEF( obj ) vcv <- VCOV( obj ) # Check if the intervals matches ctr.mat if( length( intl ) == 1 ) intl <- rep( intl, nrow( ctr.mat ) ) if( length( intl ) != nrow( ctr.mat ) ) stop( "intl must match ctr.mat" ) # Workaround to expand the vcov matrix with 0s so that it matches # the coefficients vector in case of (extrinsic) aliasing. if( any( is.na( cf ) ) ) { vM <- matrix( 0, length( cf ), length( cf ) ) dimnames( vM ) <- list( names( cf ), names( cf ) ) vM[!is.na(cf),!is.na(cf)] <- vcv cf[is.na(cf)] <- 0 vcv <- vM } if( is.character( subset ) ) { sb <- numeric(0) for( i in 1:length( subset ) ) sb <- c(sb,grep( subset[i], names( cf ) )) subset <- sb # unique( sb ) } # If subset is not given, make it the entire set if( is.null( subset ) ) subset <- 1:length( cf ) # Exclude units where aliasing has produced NAs. # Not needed after replacement with 0s # subset <- subset[!is.na( cf[subset] )] cf <- cf[subset] vcv <- vcv[subset,subset] if( is.null( ctr.mat ) ) { ctr.mat <- diag( length( cf ) ) rownames( ctr.mat ) <- names( cf ) } if( dim( ctr.mat )[2] != length(cf) ) stop( paste("\n Dimension of ", deparse(substitute(ctr.mat)), ": ", paste(dim(ctr.mat), collapse = "x"), ", not compatible with no of parameters in ", deparse(substitute(obj)), ": ", length(cf), sep = "")) # Finally, here is the actual computation of the estimates ct <- ctr.mat %*% cf vc <- ctr.mat %*% vcv %*% t( ctr.mat ) # If a sample is requested replace the eatimate by a sample if( sample ) ct <- t( mvrnorm( sample, ct, vc ) ) # If Exp was requested, we take the exponential of the estimates # before we cumulate the sum if( Exp ) { ct <- exp( ct ) vc <- ( ct[,1] %*% t(ct[,1]) ) * vc } # Here is the cumulation matrix cum.mat <- 1 - upper.tri( diag(ct[,1]) ) # Multiply columns of the matrix with interval lengths cum.mat <- t( intl * t( cum.mat ) ) # This is then multiplied to the coefficients ct <- cum.mat %*% ct if( sample ) return( ct ) else { vc <- cum.mat %*% vc %*% t( cum.mat ) se <- sqrt( diag( vc ) ) if( !ci.Exp ) { cum <- cbind( ct, se ) %*% ci.mat( alpha=alpha ) return( cbind( cum, "StdErr"=se ) ) } else { cum <- exp( cbind( log(ct), se/ct ) %*% ci.mat( alpha=alpha ) ) return( cbind( cum, "Erf"=exp( qnorm(1-alpha/2)*se/as.vector(ct) ) ) ) } } } Epi/R/plotEst.R0000644000175100001440000000726312531361077013000 0ustar hornikusersget.ests <- function( ests, ... ) { # If a model object is supplied, extract the parameters and the # standard errors # if( inherits( ests, c("glm","coxph","clogistic","gnlm","survreg") ) ) ests <- ci.exp( ests, ... ) else if( inherits( ests, c("lm","gls","lme","nls","polr", "mer","MIresult","mipo") ) ) ests <- ci.lin( ests, ... )[,-(2:4)] ests } plotEst <- function( ests, y = dim(ests)[1]:1, txt = rownames(ests), txtpos = y, ylim = range(y)-c(0.5,0), xlab = "", xtic = nice( ests[!is.na(ests)], log=xlog ), xlim = range( xtic ), xlog = FALSE, pch = 16, cex = 1, lwd = 2, col = "black", col.txt = "black", font.txt = 1, col.lines = col, col.points = col, vref = NULL, grid = FALSE, col.grid = gray(0.9), restore.par = TRUE, ... ) { # Function to plot estimates from a model. # Assumes that ests is a p by 3 matrix with estimate, lo and hi as # columns OR a model object. # Extract the estimates if necessary # ests <- get.ests( ests, ... ) # Is it likley that we want a log-axis for the parameters? # mult <- inherits( ests, c("glm","coxph","gnlm") ) if( missing(xlog) ) xlog <- mult # Create an empty plot in order to access the dimension so that # sufficient place can be made for the text in the margin # plot.new() mx <- max( strwidth( txt, units="in" ) ) oldpar <- par( mai=par("mai") + c(0,mx,0,0) ) if( restore.par ) on.exit( par( oldpar ) ) # Set up the coordinate system witout advancing a frame # plot.window( xlim = xlim, ylim = ylim, log = ifelse( xlog, "x", "") ) # Draw a grid if requested # if( !is.logical( grid ) ) abline( v = grid, col = col.grid ) if( is.logical( grid ) & grid[1] ) abline( v = xtic, col = col.grid ) # Draw a vertical reference line # if( !missing( vref ) ) abline( v = vref ) # Draw the estimates with c.i. s # linesEst( ests, y, pch=pch, cex=cex, lwd=lwd, col.points=col.points, col.lines=col.lines ) # Finally the x-axis and the annotation of the estimates # axis( side=1, at=xtic ) mtext( side=1, xlab, line=par("mgp")[1], cex=par("cex")*par("cex.lab") ) axis( side=2, at=txtpos, labels=txt, las=1, lty=0, col.axis=col.txt ) invisible( oldpar ) } pointsEst <- linesEst <- function( ests, y = dim(ests)[1]:1, pch = 16, cex = 1, lwd = 2, col = "black", col.lines = col, col.points = col, ... ) { # Function to add estimates from a model to a drawing. # Assumes that ests is a p by 3 matrix with estimate, lo and hi as # columns. # Extract the estimates if necessary # ests <- get.ests( ests, ... ) # Cut the confidence interval lines to fit inside the plot # before drawing them. # xrng <- if( par("xlog") ) 10^par("usr")[1:2] else par("usr")[1:2] segments( pmax(ests[, 2],xrng[1]), y, pmin(ests[, 3], xrng[2]), y, lwd = lwd, col=col.lines ) # Then the point estimates on top of the lines. # points( ests[, 1], y, pch = pch, cex = cex, col=col.points ) invisible() } nice <- function( x, log = F, lpos = c(1,2,5), ... ) { # Function to produce nice labels also for log-axes. # if( log ) { fc <- floor( log10( min( x ) ) ):ceiling( log10( max( x ) ) ) tick <- as.vector( outer( lpos, 10^fc, "*" ) ) ft <- max( tick[tickmax(x)] ) tick <- tick[tick>=ft & tick<=lt] if( length( tick ) < 4 & missing( lpos ) ) tick <- nice( x, log=T, lpos=c(1:9) ) if( length( tick ) > 10 & missing( lpos ) ) tick <- nice( x, log=T, lpos=1 ) tick } else pretty( x, ... ) } Epi/R/crr.Lexis.r0000644000175100001440000000253712531536633013260 0ustar hornikuserscrr.Lexis <- function( obj, mod, quiet=FALSE, ... ) { # Model formula to be transmitted md <- mod # Outcome variable fc <- as.character( mod[2] ) # Censoring levels cn <- unique( as.character(obj$lex.Xst)[obj$lex.Xst==obj$lex.Cst] ) # The competing causes (the remaining levels) cc <- setdiff( levels(obj$lex.Xst), union(fc,cn) ) # No l.h.s. side of formula when deriving model matrix mod[2] <- NULL # Remember no intercept term cv <- model.matrix( mod, data=obj )[,-1] # Then do it M <- crr( ftime = obj$lex.dur, fstatus = obj$lex.Xst, failcode = fc, cencode = cn, cov1 = cv, ... ) # A table of the no of transitions N <- with( obj, table( Relevel( lex.Xst, c(fc,cc,cn) ) ) ) names( N ) <- paste( rep( c("Event:"," comp:"," cens:"), c(length(fc),length(cc),length(cn)) ), names(N) ) # add model an table to the resulting object M <- c( M, list( model.Lexis = md, transitions = cbind(N) ) ) # remember the class attribute (lost by doing "c") class( M ) <- "crr" # print overview if desired if( !quiet ) cat( "crr analysis of event", paste('"',fc,'"',sep=''), "\n versus", paste('"',cc,'"',sep=''), "\n with", paste('"',cn,'"',sep=''), "as censoring.\n" ) M } Epi/R/float.R0000644000175100001440000000666612531361077012461 0ustar hornikusers"float" <- function(object, factor, iter.max = 50) { float.variance <- function(V, tol=1.0e-3, iter.max = 50) { ## Calculate floated variances for variance matrix V, which is ## assumed to represent a set of treatment contrasts m <- nrow(V) if (!is.matrix(V) || ncol(V) != m || m == 1) stop ("V must be a square matrix of size 2 x 2 or more") evals <- eigen(V, only.values=TRUE)$values if(any(evals < 0)) stop("V not positive definite") ## Starting values from Easton et al (1991) R <- V - diag(diag(V)) V00 <- sum(R)/(m * (m-1)) V10 <- apply(R, 1, sum)/(m-1) fv <- c(V00, V00 - 2 * V10 + diag(V)) for(iter in 1:iter.max) { w <- 1/fv S <- sum(w) w1 <- w[-1]/S ##Augment data matrix V10 <- as.vector(V %*% w1) V00 <- as.vector(1/S + t(w1) %*% V %*% w1) ##Calculate new estimates fv.old <- fv fv <- c(V00, V00 - 2 * V10 + diag(V)) ## Check convergence if(max(abs(fv.old - fv)/fv) < tol) break } if (iter == iter.max) warning("Floated variance estimates did not converge") Vmodel.inv <- S * (diag(w1) - w1 %*% t(w1)) evals <- 1/(eigen(V %*% Vmodel.inv, only.values=TRUE)$values) divergence <- sum(1/evals - 1 + log(evals))/2 return(list(variance=fv, error.limits=sqrt(range(evals)), divergence=divergence)) } if (is.null(object$xlevels)) { stop("No factors in model") } if (missing(factor)) { i <- 1 factor <- names(object$xlevels)[1] } else { i <- pmatch(factor, names(object$xlevels)) if (is.na(i)) { stop(paste("Factor",i,"not found in model")) } } xcontrasts <- object$contrasts[[i]] xlevels <- object$xlevels[[i]] xname <- names(object$xlevels)[i] nlevels <- length(xlevels) ## Extract the coefficients and variance matrix for a single factor ## from object if (nlevels <= 2) { stop ("Floated variances undefined for factors with less than 3 levels") } ## Get contrast matrix C <- if (is.matrix(xcontrasts)) { xcontrasts } else { get(xcontrasts, mode="function")(xlevels) } if (qr(C)$rank < nlevels - 1) { stop ("Impossible to reconstruct treatment contrasts") } ## Get coefficients and variance matrix if(is.null(cnames <- colnames(C))) cnames <- 1:(nlevels-1) contr.names <- paste(xname, cnames, sep="") coef <- coef(object)[contr.names] V <- vcov(object)[contr.names, contr.names] ## Convert to treatment contrast parameterization if (identical(xcontrasts, "contr.treatment")) { V.tc <- V coef.tc <- c(0, coef) } else { D.inv <- cbind(rep(-1,nlevels-1), diag(nlevels-1)) S <- D.inv %*% cbind(rep(1, nlevels), C) S <- S[,-1] ## coefficients coef.tc <- c(0, S %*% coef) ## If we find a baseline level (implicitly defined ## by having a row of zeros in the contrast matrix) ## then adjust the coefficients is.base <- apply(abs(C), 1, sum) == 0 if (any(is.base)) coef.tc <- coef.tc - coef.tc[is.base] ## variance matrix V.tc <- S %*% V %*% t(S) } names(coef.tc) <- xlevels float.out <- float.variance(V.tc, iter.max = iter.max) var <- float.out$var names(var) <- xlevels ans <- list(coef=coef.tc, var=var, limits=float.out$error.limits, factor=factor) class(ans) <- "floated" return(ans) } Epi/R/cutLexis.R0000644000175100001440000002535312531361077013146 0ustar hornikusers doCutLexis <- function(data, cut, timescale, new.scale=FALSE ) { ## new.scale is a character constant with the name of the new timescale ## Code each new interval using new variable lex.cut: ## 0 = unchanged interval (cut occurs after exit) ## 1 = first part of split interval ## 2 = second part of split interval (or cut occurs before interval) cut[is.na(cut)] <- Inf #If a cut time is missing, it never happens ## First intervals (before the cut) in.1 <- entry(data, timescale) ex.1 <- pmin(cut, exit(data, timescale)) ## Create Lexis object for first intervals lx.1 <- data lx.1$lex.dur <- ex.1 - in.1 lx.1$lex.cut <- ifelse(cut < exit(data, timescale), 1, 0) if( new.scale ) lx.1[,"lex.new.scale"] <- NA ## Second intervals (after the cut) in.2 <- pmax(cut, entry(data, timescale)) ex.2 <- exit(data, timescale) ## Create Lexis object for second intervals lx.2 <- data lx.2$lex.dur <- ex.2 - in.2 lx.2$lex.cut <- 2 if( new.scale ) lx.2[,"lex.new.scale"] <- in.2 - cut ## Update entry times lx.2[, timeScales(data)] <- exit(data) - lx.2$lex.dur return(rbind(lx.1, lx.2)) } setStatus.default <- function(data, new.state) { data$lex.Xst[data$lex.cut == 1] <- new.state[data$lex.cut == 1] data$lex.Cst[data$lex.cut == 2] <- new.state return(data) } setStatus.numeric <- function(data, new.state, precursor.states=NULL, progressive=TRUE) { if (!is.numeric(new.state)) { stop("If lex.Cst, lex.Xst are numeric, new.state must be numeric too") } data$lex.Xst[data$lex.cut == 1] <- new.state[data$lex.cut == 1] data$lex.Cst[data$lex.cut == 2] <- new.state exit.state <- data$lex.Xst[data$lex.cut == 2] is.precursor <- exit.state %in% precursor.states if (progressive) { is.precursor <- is.precursor | (exit.state < new.state) } data$lex.Xst[data$lex.cut == 2][is.precursor] <- new.state[is.precursor] return(data) } setStatus.factor <- function( data, new.state, precursor.states=NULL, progressive=TRUE) { if (!is.character(new.state)) { stop("new.state must be a character vector, but it is ",str(new.state)) } current.states <- levels(data$lex.Cst) new.states <- setdiff(new.state,current.states) new.states <- new.states[!is.na(new.states)] ## Modify factor levels if necessary if (length(new.states) > 0) { all.states <- c(current.states, sort(new.states)) new.order <- match( c(intersect(precursor.states,current.states), new.states, setdiff(current.states,precursor.states)), all.states ) levels(data$lex.Cst) <- all.states levels(data$lex.Xst) <- all.states } data$lex.Xst[data$lex.cut == 1] <- new.state[data$lex.cut == 1] data$lex.Cst[data$lex.cut == 2] <- new.state exit.state <- data$lex.Xst[data$lex.cut==2] is.precursor <- exit.state %in% precursor.states if (progressive) { if (is.ordered(data$lex.Xst)) { is.precursor <- is.precursor | (exit.state < new.state) } else { warning("progressive=TRUE argument ignored for unordered factor") } } data$lex.Xst[data$lex.cut==2][is.precursor] <- new.state[is.precursor] # Reorder factor levels sensibly if (!progressive & length(new.states)>0){ data$lex.Cst <- Relevel( data$lex.Cst, new.order ) data$lex.Xst <- Relevel( data$lex.Xst, new.order ) } return(data) } # Added by BxC match.cut <- function( data, cut ) { if( sum(!is.na(match(c("lex.id","cut","new.state"),names(cut))))<3 ) stop( "The dataframe supplied in the cut= argument must have columns", "'lex.id','cut','new.state', but the columns are:\n", names( cut ) ) else { if( length( unique( cut$lex.id ) ) < nrow( cut ) ) stop( "Values of 'lex.id' must be unique in the 'cut' dataframe" ) else zz <- merge( data[,"lex.id",drop=FALSE], cut, all.x=TRUE ) if( is.factor ( data$lex.Cst ) ) zz$new.state <- as.character(zz$new.state) if( is.numeric( data$lex.Cst ) ) zz$new.state <- as.numeric(zz$new.state) return( zz ) } } # End of addition / change cutLexis <- function(data, cut, timescale = 1, new.state = nlevels(data$lex.Cst)+1, new.scale = FALSE, split.states = FALSE, progressive = FALSE, precursor.states = NULL, count = FALSE) { if (!inherits(data, "Lexis")) stop("data must be a Lexis object") if( count ) return( countLexis( data=data, cut=cut, timescale=timescale ) ) if( inherits( cut, "data.frame" ) ){ zz <- match.cut( data, cut ) cut <- zz$cut new.state <- zz$new.state } else if (length(cut) == 1) { cut <- rep(cut, nrow(data)) } else if (length(cut) != nrow(data)) { stop("'cut' must have length 1 or nrow(data) (=", nrow(data), "),\n --- but it has length ", length(cut),".") } timescale <- check.time.scale(data, timescale) if (length(timescale) > 1) { stop("Multiple time scales") } ## If we want to add a new timescale, construct the name if( is.logical(new.scale) ) { if( new.scale ) scale.name <- paste( new.state[1], "dur", sep="." ) } else { scale.name <- new.scale new.scale <- TRUE } if (missing(new.state)) { new.state <- data$lex.Cst #Carry forward last state } else if (length(new.state) == 1) { new.state <- rep(new.state, nrow(data)) } else if (length(new.state) != nrow(data)) { stop("'new.state' must have length 1 or nrow(data) (=", nrow(data), "),\n --- but it has length ", length(new.state)) } if (progressive) { if (is.factor(data$lex.Cst) && !is.ordered(data$lex.Cst)) { stop("progressive=TRUE invalid for unordered factors") } if (any(data$lex.Xst < data$lex.Cst)) { stop("Lexis object is not progressive before splitting") } } lx <- doCutLexis( data, cut, timescale, new.scale=TRUE ) if (is.factor(data$lex.Cst)) { lx <- setStatus.factor(lx, new.state, precursor.states, progressive) } else if (is.numeric(data$lex.Cst)) { lx <- setStatus.numeric(lx, new.state, precursor.states, progressive) } else { lx <- setStatus.default(lx, new.state) } ## Remove redundant intervals lx <- lx[lx$lex.dur > 0,] ## Remove the lex.cut column lx <- lx[,-match("lex.cut",names(lx))] ## Update the states visited after the cut if( split.states & is.factor( data$lex.Cst ) ) { post.states <- setdiff( levels(data$lex.Cst), precursor.states ) tmp.Cst <- as.character( lx$lex.Cst ) tmp.Cst <- ifelse( !is.na(lx$lex.new.scale) & lx$lex.new.scale>0 & tmp.Cst %in% post.states, paste( tmp.Cst,"(",new.state,")",sep="" ), tmp.Cst ) tmp.Xst <- as.character( lx$lex.Xst ) tmp.Xst <- ifelse( !is.na(lx$lex.new.scale) & tmp.Xst %in% post.states, paste( tmp.Xst,"(",new.state,")",sep="" ), tmp.Xst ) all.levels <- unique( c(tmp.Cst,tmp.Xst) ) ## put all the new levels after the old ones xtr.levels <- setdiff( all.levels, levels(lx$lex.Cst) ) new.levels <- c( levels(lx$lex.Cst), xtr.levels ) lx$lex.Cst <- factor( tmp.Cst, levels=new.levels ) lx$lex.Xst <- factor( tmp.Xst, levels=new.levels ) } ## Include the new timescale if( new.scale ) { ## Rename the new timescale variable names(lx)[match("lex.new.scale",names(lx))] <- scale.name ## The timescales' position among columns - used to reorder columns tn <- c( match( attr( data, "time.scales" ), names( lx ) ), ncol(lx) ) oth <- setdiff( 1:ncol(lx), tn ) ## Reorder columns (lx will then lose attributes) and sort rows lx <- lx[order(lx$lex.id,lx[,timescale]),c(tn,oth)] ## Update the attributes new.br <- c( attr( data, "breaks" ), list(NULL) ) names( new.br )[length(new.br)] <- scale.name attr( lx, "time.scales" ) <- c( attr( data, "time.scales" ), scale.name ) attr( lx, "time.since" ) <- c( attr( data, "time.since" ), names(table(new.state)) ) attr( lx, "breaks" ) <- new.br attr( lx, "class" ) <- attr( data, "class" ) } else { # Remove the new timescale and sort rows lx <- lx[order(lx$lex.id,lx[,timescale]),-match("lex.new.scale",names(lx))] # and transfer all the other attributes attr( lx, "time.scales" ) <- attr( data, "time.scales" ) attr( lx, "time.since" ) <- attr( data, "time.since" ) attr( lx, "breaks" ) <- attr( data, "breaks" ) attr( lx, "class" ) <- attr( data, "class" ) } lx } countLexis <- function(data, cut, timescale = 1) { if (!inherits(data, "Lexis")) stop("data must be a Lexis object") if( inherits( cut, "data.frame" ) ){ zz <- match.cut( data, cut ) cut <- zz$cut new.state <- zz$new.state } else if (length(cut) == 1) { cut <- rep(cut, nrow(data)) } else if (length(cut) != nrow(data)) { stop("'cut' must have length 1 or nrow(data) (=", nrow(data), "),\n --- but it has length ", length(cut),".") } timescale <- check.time.scale(data, timescale) if (length(timescale) > 1) { stop("Multiple time scales not meaningful") } lx <- doCutLexis(data, cut, timescale) ## Update status variables lx$lex.Xst[lx$lex.cut == 1] <- lx$lex.Cst[lx$lex.cut == 1] + 1 lx$lex.Cst[lx$lex.cut == 2] <- lx$lex.Cst[lx$lex.cut == 2] + 1 lx$lex.Xst[lx$lex.cut == 2] <- lx$lex.Xst[lx$lex.cut == 2] + 1 ## Remove redundant intervals lx <- lx[lx$lex.dur > 0,] ## Remove the lex.cut column lx <- lx[,-match("lex.cut",names(lx))] ## Retain the attributes attr( lx, "breaks" ) <- attr( data, "breaks" ) attr( lx, "time.scales" ) <- attr( data, "time.scales" ) attr( lx, "class" ) <- attr( data, "class" ) return(lx[order(lx$lex.id,lx[,timescale]),]) } Epi/R/contr.2nd.R0000644000175100001440000000037112531361077013146 0ustar hornikuserscontr.2nd <- function( n ) { if( is.numeric(n) && length(n) == 1 ) levs<- 1:n else { levs <- n n <- length(n) } if( n<3 ) stop( "Contrasts not defined for ", n-2, " degrees of freedom" ) outer( 1:n, 3:n-1, FUN=function(x,y) pmax( x-y, 0 ) ) } Epi/R/boxes.MS.R0000644000175100001440000003666512531361077013014 0ustar hornikuserstbox <- function( txt, x, y, wd, ht, font=2, lwd=2, col.txt=par("fg"), col.border=par("fg"), col.bg="transparent" ) { rect( x-wd/2, y-ht/2, x+wd/2, y+ht/2, lwd=lwd, border=col.border, col=col.bg ) text( x, y, txt, font=font, col=col.txt ) invisible( c( x, y, wd, ht ) ) } dbox <- function( x, y, wd, ht=wd, font=2, lwd=2, cwd=5, col.cross=par("fg"), col.border=par("fg"), col.bg="transparent" ) { rect( x-wd/2, y-ht/2, x+wd/2, y+ht/2, lwd=lwd, border=col.border, col=col.bg ) ch <- ht*2/3 segments( c(x , x-ch/3), c(y+ch/2, y+ch/6), c(x , x+ch/3), c(y-ch/2, y+ch/6), lwd=cwd, col=col.cross ) invisible( c( x, y, wd, ht ) ) } fillarr <- function( x1, y1, x2, y2, gap=2, fr=0.8, angle=17, lwd=2, length=par("pin")[1]/30, ... ) { fr <- 1-gap/sqrt((x1-x2)^2+(y1-y2)^2) if( !missing(fr) ) if( fr > 1 ) fr <- fr/100 for( a in 1:angle ) arrows( x1 + (x2-x1)*(1-fr)/2, y1 + (y2-y1)*(1-fr)/2, x2 - (x2-x1)*(1-fr)/2, y2 - (y2-y1)*(1-fr)/2, angle=a, lwd=lwd, ... ) } std.vec <- function( a, b ) { l <- sqrt(a^2+b^2) if( l==0 ) return( c(0,0) ) else return( c(a/l,b/l) ) } boxarr <- function (b1, b2, offset = FALSE, pos = 0.45, ...) { d <- std.vec(b2[1] - b1[1], b2[2] - b1[2]) dd <- d * offset x1 <- b1[1] - dd[2] y1 <- b1[2] + dd[1] w1 <- b1[3] h1 <- b1[4] x2 <- b2[1] - dd[2] y2 <- b2[2] + dd[1] w2 <- b2[3] h2 <- b2[4] hx1 <- x1 + ifelse((y2-y1) != 0, (x2-x1) * ((h1/2)/abs(y2-y1)), sign(x2-x1) * w1/2) vx1 <- x1 + ifelse((x2-x1) != 0, (x2-x1) * ((w1/2)/abs(x2-x1)), 0) hx2 <- x2 + ifelse((y1-y2) != 0, (x1-x2) * ((h2/2)/abs(y1-y2)), sign(x1-x2) * w2/2) vx2 <- x2 + ifelse((x1-x2) != 0, (x1-x2) * ((w2/2)/abs(x1-x2)), 0) hy1 <- y1 + ifelse((y2-y1) != 0, (y2-y1) * ((h1/2)/abs(y2-y1)), 0) vy1 <- y1 + ifelse((x2-x1) != 0, (y2-y1) * ((w1/2)/abs(x2-x1)), sign(y2-y1) * h1/2) hy2 <- y2 + ifelse((y1-y2) != 0, (y1-y2) * ((h2/2)/abs(y1-y2)), 0) vy2 <- y2 + ifelse((x1-x2) != 0, (y1-y2) * ((w2/2)/abs(x1-x2)), sign(y1-y2) * h2/2) if( abs(vy1-y1) < h1/2 ) { bx1 <- vx1 by1 <- vy1 } else { bx1 <- hx1 by1 <- hy1 } if( abs(vy2-y2) < h2/2 ) { bx2 <- vx2 by2 <- vy2 } else { bx2 <- hx2 by2 <- hy2 } fillarr( bx1, by1, bx2, by2, ... ) invisible( list( x = bx1*(1-pos)+bx2*pos, y = by1*(1-pos)+by2*pos, d = d ) ) } wh.no <- function( tt, i, j ) { ## Utility to count the number of non-NA off diagonal elements with ## row0 ) arrowtext[a] <- formatC( D[i,j], format="f", digits=0, big.mark="," ) else arrowtext[a] <- "" if( show.R & R[i,j]>0 ) arrowtext[a] <- paste( if( !is.null(arrowtext[a]) ) paste( arrowtext[a], DR.sep[1], sep="" ), formatC( R[i,j], format="f", digits=digits.R, big.mark="," ), if( length(DR.sep) > 1 ) DR.sep[2], sep="" ) } else if( !is.null(txt.arr) ) arrowtext[a] <- txt.arr[a] if( !is.null(arrowtext[a]) ) text( arr$x-arr$d[2], arr$y+arr$d[1], arrowtext[a], adj=as.numeric(c(arr$d[2]>0,arr$d[1]<0)), font=font.arr[a], col=col.txt.arr[a] ) } } # Redraw the boxes with white background to remove any arrows for( i in subset ) tbox( pl.nam[i], xx[i], yy[i], wd[i], ht[i], lwd=lwd[i], col.bg=par("bg") ) # Then redraw the boxes again for( i in subset ) tbox( pl.nam[i], xx[i], yy[i], wd[i], ht[i], font=font[i], lwd=lwd[i], col.txt=col.txt[i], col.border=col.border[i], col.bg=col.bg[i] ) # Finally create an object with all information to re-draw the display MSboxes <- list( Boxes = data.frame( xx = xx, yy = yy, wd = wd, ht = ht, font = font, lwd = lwd, col.txt = col.txt, col.border = col.border, col.bg = col.bg, stringsAsFactors=FALSE ), State.names = pl.nam, Tmat = tt, Arrows = data.frame( lwd.arr = lwd.arr, col.arr = col.arr, pos.arr = pos.arr, col.txt.arr = col.txt.arr, font.arr = font.arr, offset.arr = offset.arr, stringsAsFactors=FALSE ), Arrowtext = arrowtext ) class( MSboxes ) <- "MS" invisible( MSboxes ) } boxes.MS <- function( obj, sub.st, sub.tr, cex=1.5, ... ) { if( !inherits(obj,"MS") ) stop( "You must supply an object of class 'MSboxes'" ) n.st <- nrow( obj$Boxes ) n.tr <- nrow( obj$Arrows ) if( missing(sub.st) ) sub.st <- 1:n.st if( missing(sub.tr) ) sub.tr <- 1:n.tr # First setting up the plot area, and restoring the plot parameters later opar <- par( mar=c(0,0,0,0), cex=cex ) on.exit( par(opar) ) plot( NA, bty="n", xlim=0:1*100, ylim=0:1*100, xaxt="n", yaxt="n", xlab="", ylab="" ) # Exercise the subsets by putting the relevant colors to "transparent" obj$Boxes[-sub.st,c("col.txt", "col.border", "col.bg")] <- "transparent" obj$Arrows[-sub.tr,c("col.arr", "col.txt.arr")] <- "transparent" # Then draw the boxes b <- list() for( i in 1:n.st ) b[[i]] <- with( obj$Boxes, tbox( obj$State.names[i], xx[i], yy[i], wd[i], ht[i], font=font[i], lwd=lwd[i], col.txt=col.txt[i], col.border=col.border[i], col.bg=col.bg[i] ) ) # and the arrows for( i in 1:n.st ) for( j in 1:n.st ) { if( !is.na(obj$Tmat[i,j]) & i!=j ) { a <- wh.no( obj$Tmat, i, j ) arr <- with( obj$Arrows, boxarr( b[[i]], b[[j]], offset=(!is.na(obj$Tmat[j,i]))*offset.arr, lwd=lwd.arr[a], col=col.arr[a], pos=pos.arr[a], ... ) ) with( obj$Arrows, text( arr$x-arr$d[2], arr$y+arr$d[1], obj$Arrowtext[a], adj=as.numeric(c(arr$d[2]>0,arr$d[1]<0)), font=font.arr[a], col=col.txt.arr[a] ) ) } } # Redraw the boxes with "bg" background to remove any arrows crossing for( i in sub.st ) with( obj$Boxes, tbox( obj$State.names[i], xx[i], yy[i], wd[i], ht[i], font=font[i], lwd=lwd[i], col.txt=par("bg"), col.border=par("bg"), col.bg=par("bg") ) ) # Then redraw the boxes again for( i in sub.st ) with( obj$Boxes, tbox( obj$State.names[i], xx[i], yy[i], wd[i], ht[i], font=font[i], lwd=lwd[i], col.txt=col.txt[i], col.border=col.border[i], col.bg=col.bg[i] ) ) # Done! invisible( NULL ) } Epi/R/ccwc.R0000644000175100001440000001236212531361077012261 0ustar hornikusersccwc <- function(entry=0, exit, fail, origin=0, controls=1, match=list(), include=list(), data=NULL, silent=FALSE) { # Check arguments entry <- eval(substitute(entry), data) exit <- eval(substitute(exit), data) fail <- eval(substitute(fail), data) origin <- eval(substitute(origin), data) n <- length(fail) if (length(exit)!=n) stop("All vectors must have same length") if (length(entry)!=1 && length(entry)!=n) stop("All vectors must have same length") if (length(origin)==1) { origin <- rep(origin, n) } else { if (length(origin)!=n) stop("All vectors must have same length") } # Transform times to correct scale t.entry <- as.numeric(entry - origin) t.exit <- as.numeric(exit - origin) # match= argument marg <- substitute(match) if (mode(marg)=="name") { match <- list(eval(marg, data)) names(match) <- as.character(marg) } else if (mode(marg)=="call" && marg[[1]]=="list") { mnames <- names(marg) nm <- length(marg) if (!is.null(mnames)) { if (nm>1) { for (i in 2:nm) { if (mode(marg[[i]])=="name") mnames[i] <- as.character(marg[[i]]) else stop("illegal argument (match)") } } else { for (i in 2:nm) { if (mode(marg[[i]])=="name") mnames[i] <- as.character(marg[[i]]) else stop("illegal argument (match)") } mnames[1] <= "" } } names(marg) <- mnames match <- eval(marg, data) } else { stop("illegal argument (match)") } m <- length(match) mnames <- names(match) if (m>0) { for (i in 1:m) { if (length(match[[i]])!=n) { stop("incorrect length for matching variable") } } } # include= argument iarg <- substitute(include) if (mode(iarg)=="name") { include <- list(eval(iarg, data)) names(include) <- as.character(iarg) } else if (mode(iarg)=="call" && iarg[[1]]=="list") { ni <- length(iarg) inames <- names(iarg) if (ni>1) { if (!is.null(inames)) { for (i in 2:ni) { if (mode(iarg[[i]])=="name") inames[i] <- as.character(iarg[[i]]) else stop("illegal argument (include)") } } else { for (i in 2:ni) { if (mode(iarg[[i]])=="name") inames[i] <- as.character(iarg[[i]]) else stop("illegal argument (include)") } inames[1] <= "" } } names(iarg) <- inames include <- eval(iarg, data) } else { stop("illegal argument (include)") } ni <- length(include) inames <- names(include) if (ni>0) { for (i in 1:ni) { if (length(include[[i]])!=n) { stop("incorrect length for included variable") } } } # create group codes using matching variables grp <- rep(1,n) pd <- 1 if (m>0) { for (im in 1:m) { v <- match[[im]] if (length(v)!=n) stop("All vectors must have same length") if (!is.factor(v)) v <- factor(v) grp <- grp + pd*(as.numeric(v) - 1) pd <- pd*length(levels(v)) } } # Create vectors long enough to hold results nn <- (1+controls)*sum(fail!=0) pr <- numeric(nn) sr <- numeric(nn) tr <- vector("numeric", nn) fr <- numeric(nn) nn <- 0 # Sample each group if (!silent) { cat("\nSampling risk sets: ") } set <- 0 nomatch <- 0 incomplete <- 0 ties <- FALSE fg <- unique(grp[fail!=0]) for (g in fg) { # Failure times ft <- unique( t.exit[(grp==g) & (fail!=0)] ) # Create case-control sets for (tf in ft) { if (!silent) { cat(".") } set <- set+1 case <- (grp==g) & (t.exit==tf) & (fail!=0) ncase <- sum(case) if (ncase>0) ties <- TRUE noncase <- (grp==g) & (t.entry<=tf) & (t.exit>=tf) & !case ncont <- controls*ncase if (ncont>sum(noncase)) { ncont <- sum(noncase) if (ncont>0) incomplete <- incomplete + 1 } if (ncont>0) { newnn <- nn+ncase+ncont sr[(nn+1):newnn] <- set tr[(nn+1):newnn] <- tf fr[(nn+1):(nn+ncase)] <- 1 fr[(nn+ncase+1):newnn] <- 0 pr[(nn+1):(nn+ncase)] <- (1:n)[case] pr[(nn+ncase+1):(newnn)] <- sample((1:n)[noncase], size=ncont) nn <- newnn } else { nomatch <- nomatch + ncase } } } if (!silent) { cat("\n") } res <- vector("list", 4+m+ni) if (nn>0) { res[[1]] <- sr[1:nn] res[[2]] <- map <- pr[1:nn] res[[3]] <- tr[1:nn] + origin[map] res[[4]] <- fr[1:nn] } if (m>0) { for (i in 1:m) { res[[4+i]] <- match[[i]][map] } } if (ni>0) { for (i in 1:ni) { res[[4+m+i]] <- include[[i]][map] } } names(res) <- c("Set", "Map", "Time", "Fail", mnames, inames) if (incomplete>0) warning(paste(incomplete, "case-control sets are incomplete")) if (nomatch>0) warning(paste(nomatch, "cases could not be matched")) if (ties) warning("there were tied failure times") data.frame(res) } Epi/R/summary.Lexis.r0000644000175100001440000000540512531361077014162 0ustar hornikuserssummary.Lexis <- function( object, simplify=TRUE, scale=1, by=NULL, Rates=FALSE, ... ) { # If we have a by argument find out what to do if (!is.null(by)) { if (is.character(by)) { if (!all(by %in% names(object))) stop("Wrong 'by' argument: '", paste(by,collapse="','"), "' - must be name(s) of variable(s) in the Lexis object") else return(lapply(split(object, object[, by]), summary.Lexis, by = NULL, simplify = simplify, scale = scale, Rates = Rates, ...)) } else { if (length(by) != nrow(object)) stop("Wrong length of 'by' argument:", length(by), "must be same length as rows of the Lexis object:", nrow(object) ) else return(lapply(split(object, by), summary.Lexis, by = NULL, simplify = simplify, scale = scale, Rates = Rates, ...)) } } # Table(s) of all transitions (no. records) tr <- trans <- with( object, table(lex.Cst,lex.Xst) ) # Remove diagonal, i.e. records with no transition for( i in intersect(rownames(trans),colnames(trans)) ) tr[i,i] <- 0 # Margins added trans <- addmargins(trans) tr <- addmargins(tr) # Sum omitting the diagonal trm <- tr[,ncol(tr)] # Compute person-years in each Cst-state pyrs <- with( object, addmargins( tapply( lex.dur,lex.Cst,sum,na.rm=TRUE), FUN=function(x) sum(x,na.rm=TRUE) ) )/scale pers <- with( object, c( tapply( lex.id, lex.Cst, function(x) length(unique(x)) ), length(unique(lex.id)) ) ) # Amend the table of records with column of events and person-years trans <- cbind( trans, trm, pyrs, pers ) # Annotate the table nicely colnames( trans )[ncol(trans)-2:0] <- c(" Events:","Risk time:"," Persons:" ) colnames( trans )[ncol(tr)] <- " Records:" names( dimnames( trans ) ) <- c("From","\nTransitions:\n To") # Make the rates and annotate the table nicely rates <- sweep( tr, 1, pyrs, "/" ) colnames( rates )[ncol(rates)] <- "Total" names( dimnames( rates ) ) <- c("From", paste("\nRates", if( scale != 1 ) paste(" (per ",scale,")",sep=""), ":\n To", sep="") ) if( simplify ) { trans <- trans[!is.na(pyrs),] rates <- rates[!is.na(pyrs),] } if( nrow(trans)==2 ) trans <- trans[1,,drop = FALSE] res <- list( Transitions=trans, Rates=rates[-nrow(rates),,drop=FALSE] ) if( !Rates ) res <- res[1] class( res ) <- "summary.Lexis" res } print.summary.Lexis <- function( x, ..., digits=2 ) { print( round( x$Transitions, digits ) ) if( length(x) == 2 ) print( round( x$Rates , digits ) ) } Epi/R/gen.exp.R0000644000175100001440000001372412531361077012711 0ustar hornikusers# Acronyms: # dop: date of purchase # dpt: dose per time # amt: amount # dur: duration use.amt.dpt <- function( purchase, push.max = Inf, breaks, lags = NULL, lag.dec = 1 ) { do.call( "rbind", lapply( split( purchase, purchase$id ), function(set) { np <- nrow(set) if( np==1 ) return( NULL ) set <- set[order(set$dop),] # Compute length of exposure periods drug.dur <- set$amt / set$dpt # Put the exposed period head to foot new.start <- min( set$dop ) + c(0,cumsum(drug.dur[-np])) # Move them out so that the start of a period is never earlier than # the dop exp.start <- new.start + cummax( pmax(set$dop-new.start,0) ) # Compute the pushes push.one <- exp.start - set$dop # Revise them to the maximally acceptable push.adj <- pmin( push.one, push.max ) # Revise the starting dates of exposure exp.start <- exp.start - push.one + push.adj # Revise the durations to be at most equal to differences between the # revised starting dates drug.dur <- pmin( drug.dur, c(diff(exp.start),Inf) ) # Compute the end of the intervals exp.end <- exp.start + drug.dur # Intervals in the middle not covered by the drug exposures - note # also that we make a record for the last follow-date followed.by.gap <- c( exp.start[-1]-exp.end[-length(exp.end)] > 0, TRUE ) # To facilitate dfR <- rbind( data.frame( id = set$id[1], dof = exp.start, dpt = set$dpt ), data.frame( id = set$id[1], dof = exp.end[followed.by.gap], dpt = 0 ) ) dfR <- dfR[order(dfR$dof),] # We now compute the cumulative dose at the end of the interval using # interval length and dpt: dfR$cum.amt <- with( dfR, cumsum( c(0, diff(dof)*dpt[-length(dpt)]) ) ) return( dfR ) } ) ) } use.only.amt <- function( purchase, pred.win = Inf, breaks, lags = NULL, lag.dec = 1 ) { # Compute the cumulative dose at all purcase dates and at the last # (unknown) future expiry date, computed based on previous # consumption. The resulting data frame has one more line per person # than no. of purchases. do.call( "rbind", lapply( split( purchase, purchase$id ), function(set) { np <- nrow(set) if( np==1 ) return( NULL ) set <- set[order(set$dop),] # The points to include in the calculation: # All dates after pred.win before last purchase, # but at least the last two purchase dates, wp <- ( set$dop > pmin( max(set$dop)-pred.win, sort(set$dop,decreasing=TRUE)[2] ) ) # Cumulative amount consumed at each dop cum.amt <- cumsum(c(0,set$amt)) # Average slope to use to project the duration last purchase avg.slp <- diff(range(cum.amt[c(wp,FALSE)]))/ diff(range(set$dop[wp])) # Purchase dates and the date of last consumption dof <- c( set$dop, set$dop[np]+set$amt[np]/avg.slp ) return( data.frame( id = set$id[1], dof = dof, cum.amt = cum.amt ) ) } ) ) } gen.exp <- function( purchase, id="id", dop="dop", amt="amt", dpt="dpt", fu, doe="doe", dox="dox", breaks, use.dpt = ( dpt %in% names(purchase) ), lags = NULL, push.max = Inf, pred.win = Inf, lag.dec = 1 ) { # Make sure that the data fames have the right column names wh <- match( c(id,dop,amt), names(purchase) ) if( any( is.na(wh) ) ) stop("Wrong column names for the purchase data frame") names( purchase )[wh] <- c("id","dop","amt") wh <- match( c(id,doe,dox), names(fu) ) if( any( is.na(wh) ) ) stop("Wrong column names for the follow-up data frame") names( fu )[wh] <- c("id","doe","dox") if( use.dpt ) { # This is to allow dpt to be entered as numerical scalar common for all if( is.numeric(dpt) ) { if( length(dpt) > 1 ) stop( "If dpt is numeric it must have length 1" ) purchase$dpt <- dpt } else names( purchase )[match(c(dpt),names(purchase))] <- "dpt" tmp.dfr <- use.amt.dpt( purchase, lags = lags, push.max = push.max, lag.dec = lag.dec ) } else tmp.dfr <- use.only.amt( purchase, lags = lags, pred.win = pred.win, lag.dec = lag.dec ) # Merge in the follow-up period for the persons tmp.dfr <- merge( tmp.dfr, fu, all=T ) # Interpolate to find the cumulative doses at the dates in breaks do.call( "rbind", lapply( split( tmp.dfr, tmp.dfr$id ), function(set) { # All values of these are identical within each set (=person) doe <- set$doe[1] dox <- set$dox[1] # The first and last date of exposure according to the assumption doi <- min(set$dof) doc <- max(set$dof) # Get the breakpoints and the entry end exit dates breaks <- sort( unique( c(breaks,doe,dox) ) ) xval <- breaks[breaks>=doe & breaks<=dox] dfr <- data.frame( id = set$id[1], dof = xval ) dfr$tfi <- pmax(0,xval-doi) dfr$tfc <- pmax(0,xval-doc) dfr$cdos <- approx( set$dof, set$cum.amt, xout=xval, rule=2 )$y for( lg in lags ) dfr[,paste( "ldos", formatC(lg,format="f",digits=lag.dec), sep="." )] <- approx( set$dof, set$cum.amt, xout=xval-lg, rule=2 )$y dfr } ) ) } Epi/R/Icens.R0000644000175100001440000000620012531361077012375 0ustar hornikusersIcens <- function(first.well, last.well, first.ill, formula, model.type=c("MRR","AER"), breaks, boot=FALSE, alpha=0.05, keep.sample=FALSE, data) { ## Create follow-up matrix containing three event times fu.expression <- substitute(cbind(first.well, last.well, first.ill)) fu <- if (missing(data)) { eval(fu.expression) } else { eval(fu.expression, data) } ## Check consistency of arguments missing.f1 <- is.na(fu[,1]) missing.f2 <- is.na(fu[,2]) if (any(missing.f1 & missing.f2)) { stop("You must supply at least one of \"first.well\" and \"last.well\"") } if (any(fu[,1] > fu[,2], na.rm=TRUE) | any(fu[,2] > fu[,3], na.rm=TRUE)) { stop("Some units do not meet: first.well < last.well < first.ill" ) } ## Fill in any gaps fu[,1][missing.f1] <- fu[,2][missing.f1] fu[,2][missing.f2] <- fu[,1][missing.f2] ## Recensor cases that fall after the last break point is.censored <- fu[,3] > max(breaks) is.censored[is.na(is.censored)] <- FALSE fu[is.censored,3] <- NA exp.dat <- expand.data(fu, formula, breaks, data) model.type <- match.arg(model.type) if (missing(formula)) { fit.out <- with(exp.dat, fit.baseline(y, rates.frame)) lambda <- coef(fit.out) } else { fit.out <- switch(model.type, "MRR"=with(exp.dat, fit.mult(y, rates.frame, cov.frame)), "AER"=with(exp.dat, fit.add(y, rates.frame, cov.frame))) lambda <- coef(fit.out$rates) } beta <- if (is.null(fit.out$cov)) numeric(0) else coef(fit.out$cov) params <- c(lambda,beta) if (boot) { nboot <- ifelse (is.numeric(boot), boot, 100) boot.coef <- matrix(NA, nrow=nboot, ncol=length(lambda) + length(beta)) colnames(boot.coef) <- names(params) for (i in 1:nboot) { subsample <- sample(nrow(fu), replace=TRUE) exp.dat <- expand.data(fu[subsample,], formula, breaks, data[subsample,]) if (missing(formula)) { sim.out <- with(exp.dat, fit.baseline(y, rates.frame, params)) boot.coef[i,] <- coef(sim.out) } else { sim.out <- switch(model.type, "MRR"=with(exp.dat, fit.mult(y, rates.frame, cov.frame, params)), "AER"=with(exp.dat, fit.add(y, rates.frame, cov.frame, params))) boot.coef[i,] <- switch(model.type, "MRR"=c(coef(sim.out[[1]]), coef(sim.out[[2]])), "AER"=coef(sim.out[[1]])) } } ci.quantiles=c(0.5, alpha/2, 1 - alpha/2) boot.ci <- t(apply(boot.coef,2,quantile,ci.quantiles)) lower.ci.lab <- paste("lower ", formatC(100 * alpha/2, format="f", digits=1),"%", sep="") upper.ci.lab <- paste("upper ", formatC(100 * (1-alpha/2), format="f", digits=1),"%", sep="") colnames(boot.ci) <- c("median", lower.ci.lab, upper.ci.lab) fit.out$boot.ci <- boot.ci if (keep.sample) { fit.out$sample <- boot.coef } } class( fit.out ) <- "Icens" attr( fit.out, "model" ) <- model.type return( fit.out ) } Epi/R/pctab.R0000644000175100001440000000070212531361077012426 0ustar hornikuserspctab <- function( TT, margin=length( dim( TT ) ), dec=1 ) { nd <- length( dim( TT ) ) sw <- (1:nd)[-margin[1]] rt <- sweep( addmargins( TT, margin, list( list( All=sum, N=function( x ) sum( x )^2/100 ) ) ), sw, apply( TT, sw, sum )/100, "/" ) if( dec ) print( round( rt, dec ) ) invisible( rt ) } Epi/R/simLexis.R0000644000175100001440000002641412531361077013142 0ustar hornikusers# First the utility functions ###################################################################### lint <- function( ci, tt, u ) { # Makes a linear interpolation, but does not crash if all ci values are # identical, but requires that both ci and tt are non-decreasing if( any( diff(ci)<0 ) | any( diff(tt)<0 ) ) stop("Non-icreasing arguments") c.u <- min( c( ci[ci>u], max(ci) ) ) c.l <- max( c( ci[ciu], max(tt) ) ) t.l <- max( c( tt[ci1 ) stop( "More than one lex.Cst present:\n", cst, "\n" ) # Expand each person by the time points prfrm <- nd[rep(1:nr,each=np),] prfrm[,tS] <- prfrm[,tS] + rep(time.pts,nr) prfrm$lex.dur <- il <- min( diff(time.pts) ) # Poisson-models should use the estimated rate at the midpoint of the # intervals: prfrp <- prfrm prfrp[,tS] <- prfrp[,tS]+il/2 # Make a data frame with predicted rates for each of the transitions # out of this state for these times rt <- data.frame( lex.id = prfrm$lex.id ) for( i in 1:length(Tr[[cst]]) ) { if( inherits( Tr[[cst]][[i]], "glm" ) ) rt <- cbind( rt, predict( Tr[[cst]][[i]], type="response", newdata=prfrp ) ) else if( inherits( Tr[[cst]][[i]], "coxph" ) ) rt <- cbind( rt, predict( Tr[[cst]][[i]], type="expected", newdata=prfrm ) ) else if( is.function( Tr[[cst]][[i]] ) ) rt <- cbind( rt, Tr[[cst]][[i]](prfrm) ) else stop( "Invalid object supplied as transition, must be either\n", "glm(poisson) or coxph object fitted to a Lexis object or\n", "a function that takes a Lexis object as argument and returns\n", "average rates for each record in the same units as lex.dur.") } names( rt )[-1] <- names( Tr[[cst]] ) # Then find the transition time and exit state for each person: xx <- match( c("lex.dur","lex.Xst"), names(nd) ) if( any(!is.na(xx)) ) nd <- nd[,-xx[!is.na(xx)]] merge( nd, do.call( "rbind", lapply( split( rt, rt$lex.id ), sim1, time.pts ) ), by="lex.id" ) } ###################################################################### get.next <- function( sf, tr.st, tS, tF ) { # Produces an initial Lexis object for the next simulation for those # who have ended up in a transient state. # Note that this exploits the existence of the "time.since" attribute # for Lexis objects and assumes that a character vector naming the # transient states is supplied as argument. if( nrow(sf)==0 ) return( sf ) nxt <- sf[sf$lex.Xst %in% tr.st,] if( nrow(nxt) == 0 ) return( nxt ) nxt[,tS] <- nxt[,tS] + nxt$lex.dur wh <- tF for( i in 1:length(wh) ) if( wh[i] != "" ) nxt[nxt$lex.Xst==wh[i],tS[i]] <- 0 nxt$lex.Cst <- nxt$lex.Xst return( nxt ) } ###################################################################### chop.lex <- function( obj, tS, cens ) { # A function that chops off all follow-up beyond cens since entry for # each individual # Entry times on 1st timescale zz <- entry( obj, 1, by.id=TRUE ) # Merge with the revised exit times on this timescale ww <- merge( obj, data.frame( lex.id = as.numeric(names(zz)), cens = zz+cens ) ) # Only retain records with an entry time prior to the revised exit time ww <- ww[ww[,tS[1]] < ww$cens,] # Revise the duration according the the revised exit time x.dur <- pmin( ww$lex.dur, ww[,"cens"]-ww[,tS[1]] ) # Change lex.Xst to lex.Cst for those with shortened follow-up ww$lex.Xst[x.dur0] nt <- length(tt) warning("\nSome initiators start in a absorbing state\n", "Initiator states represented are: ", paste( rbind( names(tt), rep(":",nt), paste(tt), rep(" ",nt) ), collapse="" ), "\n", "Transient states are: ", paste( names( Tr ), coll=" " ) ) if( nrow(nxt)==0 ) stop( "\nNo initiators in transient states!" ) } # Then we update those who are in a transient states and keep on doing # that till all are in absorbing states or censored while( nrow(nxt) > 0 ) { nx <- do.call( "rbind", lapply( split( nxt, nxt$lex.Cst ), simX, Tr, time.pts, tS ) ) sf <- rbind( sf, nx ) nxt <- get.next( nx, tr.st, tS, tF ) } # Doctor lex.Xst levels, fix values for the censored sf$lex.Xst <- factor( sf$lex.Xst, levels=levels(sf$lex.Cst) ) sf$lex.Xst[is.na(sf$lex.Xst)] <- sf$lex.Cst[is.na(sf$lex.Xst)] # Nicely order the output by persons, then times and states nord <- match( c( "lex.id", tS, "lex.dur", "lex.Cst", "lex.Xst" ), names(sf) ) noth <- setdiff( 1:ncol(sf), nord ) sf <- sf[order(sf$lex.id,sf[,tS[1]]),c(nord,noth)] rownames(sf) <- NULL # Finally, supply attributes attr( sf, "time.scales" ) <- tS attr( sf, "time.since" ) <- tF chop.lex( sf, tS, max(time.pts) ) } ###################################################################### nState <- function ( obj, at, from, time.scale = 1 ) { # Counts the number of persons in each state of the Lexis object 'obj' # at the times 'at' from the time 'from' in the time scale # 'time.scale' # Determine timescales and absorbing and transient states tS <- check.time.scale(obj,time.scale) TT <- tmat(obj) absorb <- rownames(TT)[apply(!is.na(TT),1,sum)==0] transient <- setdiff( rownames(TT), absorb ) # Expand each record length(at) times tab.frm <- obj[rep(1:nrow(obj),each=length(at)), c(tS,"lex.dur","lex.Cst","lex.Xst")] # Stick in the corresponding times on the chosen time scale tab.frm$when <- rep( at, nrow(obj) ) + from # For transient states keep records that includes these points in time tab.tr <- tab.frm[tab.frm[,tS] <= tab.frm$when & tab.frm[,tS]+tab.frm$lex.dur > tab.frm$when,] tab.tr$State <- tab.tr$lex.Cst # For absorbing states keep records where follow-up ended before tab.ab <- tab.frm[tab.frm[,tS]+tab.frm$lex.dur <= tab.frm$when & tab.frm$lex.Xst %in% absorb,] tab.ab$State <- tab.ab$lex.Xst # Make a table using the combination of those in transient and # absorbing states. with( rbind( tab.ab, tab.tr ), table( when, State ) ) } ###################################################################### pState <- function( nSt, perm=1:ncol(nSt) ) { # Compute cumulative proportions of persons across states in order # designate by 'perm' tt <- t( apply( nSt[,perm], 1, cumsum ) ) tt <- sweep( tt, 1, tt[,ncol(tt)], "/" ) class( tt ) <- c("pState","matrix") tt } ###################################################################### plot.pState <- function( x, col = rainbow(ncol(x)), border = "transparent", xlab = "Time", ylim = 0:1, ylab = "Probability", ... ) { # Function to plot cumulative probabilities along the time scale. matplot( as.numeric(rownames(x)), x, type="n", ylim=ylim, yaxs="i", xaxs="i", xlab=xlab, ylab=ylab, ... ) lines.pState( x, col = col, border = border, ... ) } ###################################################################### lines.pState <- function( x, col = rainbow(ncol(x)), border = "transparent", ... ) { # Function to plot cumulative probabilities along the time scale. # Fixing the colors: nc <- ncol(x) col <- rep( col , nc )[1:nc] border <- rep( border, nc )[1:nc] # Just for coding convenience when plotting polygons pSt <- cbind( 0, x ) for( i in 2:ncol(pSt) ) { polygon( c( as.numeric(rownames(pSt)) , rev(as.numeric(rownames(pSt))) ), c( pSt[,i ], rev(pSt[,i-1]) ), col=col[i-1], border=border[i-1], ... ) } } Epi/R/Lexis.lines.R0000644000175100001440000000631012531361077013533 0ustar hornikusersLexis.lines <- function( entry.date = NA, exit.date = NA, birth.date = NA, entry.age = NA, exit.age = NA, risk.time = NA, col.life = "black", lwd.life = 2, fail = NA, cex.fail = 1, pch.fail = c(NA,16), col.fail = col.life, data = NULL ) { ## Get variables from data argument, if supplied, or from parent ## frame if not. entry.date <- eval(substitute(entry.date), data) entry.age <- eval(substitute(entry.age ), data) exit.date <- eval(substitute(exit.date ), data) exit.age <- eval(substitute(exit.age ), data) risk.time <- eval(substitute(birth.date), data) birth.date <- eval(substitute(birth.date), data) fail <- eval(substitute(fail ), data) # If fail is numeric make it logical if( is.numeric( fail ) ) fail <- ( fail > 0 ) # Complete the information on lifelines XX <- Life.lines( entry.date = entry.date, entry.age = entry.age, exit.date = exit.date, exit.age = exit.age, risk.time = risk.time, birth.date = birth.date ) # Expand lwd.life/col.life/pch.fail/col.fail/cex.fail # Np <- nrow( XX ) if( length( col.life )==1 ) col.life <- rep( col.life, Np ) else if( length( col.life )!=length(fail) ) stop("col.life must have length 1 or length(fail)" ) if( length( lwd.life )==1 ) lwd.life <- rep( lwd.life, Np ) else if( length( lwd.life )!=length(fail) ) stop("lwd.life must have length 1 or length(fail)" ) if( length( col.fail )==1 ) col.fail <- rep( col.fail, Np ) else { if( length( col.fail )==2 ) col.fail <- col.fail[fail+1] } if( length( col.fail )!=length(fail) ) stop("col.fail must have length 1,2 or length(fail)" ) if( length( pch.fail )==1 ) pch.fail <- rep( pch.fail, Np ) else if( length( pch.fail )==2 ) pch.fail <- pch.fail[fail+1] if( length( pch.fail )!=length(fail) ) stop("pch.fail must have length 1,2 or length(fail)" ) if( length( cex.fail )==1 ) cex.fail <- rep( cex.fail, Np ) else if( length( cex.fail )==2 ) cex.fail <- cex.fail[fail+1] if( length( cex.fail )!=length(fail) ) stop("cex.fail must have length 1,2 or length(fail)" ) # Was XX returned as a Date-object? # If so make a numerical version i LL, otherwise just a copy. # if( attr( XX, "Date" ) ) { LL <- data.frame( lapply( XX, unclass ) ) LL[,c(1,3,5)] <- LL[,c(1,3,5)] / 365.25 + 1970 LL[,c(2,4,6)] <- LL[,c(2,4,6)] / 365.25 } else LL <- XX # Find age and date ranges in the current plot. # date <- par( "usr" )[1:2] age <- par( "usr" )[3:4] # Plot the lifelines segments( LL[,1], LL[,2], LL[,3], LL[,4], lwd=lwd.life, col=col.life ) # If there are any non-NAs for pch.fail then blank out the space # where they go before plotting the symbols if( any( !is.na(pch.fail) ) ) points( LL[!is.na(pch.fail),3], LL[!is.na(pch.fail),4], pch=16, col="white", #par()$bg, cex=cex.fail[!is.na(pch.fail)] ) points( LL[,3], LL[,4], pch=pch.fail, col=col.fail, cex=cex.fail ) # Return the untouched version of the completed dataframe # invisible( data.frame( XX, fail=fail ) ) } Epi/R/Pplot.R0000644000175100001440000000346512531361077012444 0ustar hornikusersPplot <- function( rates, age = as.numeric( dimnames( rates )[[1]] ), per = as.numeric( dimnames( rates )[[2]] ), grid = FALSE, p.grid = grid, ygrid = grid, col.grid = gray( 0.9 ), p.lim = range( per, na.rm=TRUE ) + c(0,diff(range(per))/30), ylim = range( rates[rates>0], na.rm=TRUE ), p.lab = names( dimnames( rates ) )[2], ylab = deparse( substitute( rates ) ), at = NULL, labels = paste( at ), type = "l", lwd = 2, lty = 1, col = par( "fg" ), log.ax = "y", las = 1, ann = FALSE, cex.ann = 0.8, xannx = 1/20, a.thin = seq( 1, length( age ), 2 ), ... ) { # Plot the frame if( ann ) p.lim <- p.lim + c(0,diff( range( age ) ) * xannx ) matplot( per, t(rates), type="n", xlim=p.lim, ylim=ylim, xlab=p.lab, ylab=ylab, log=log.ax, las=las, yaxt=if( !is.null( at ) ) "n" else "s" ) if( !is.null( at ) ) axis( side=2, at=at, labels=labels, yaxt="s", las=las ) # and the grid if required if( !missing( p.grid ) | !missing( grid ) ) { if( is.logical( p.grid ) & p.grid[1] ) p.grid <- nice( per, log=par("xlog") ) abline( v=p.grid, col=col.grid ) } if( !missing( ygrid ) | !missing( ygrid ) ) { if( is.logical( ygrid ) & ygrid[1] ) ygrid <- nice( rates[!is.na(rates)], log=par("ylog") ) abline( h=ygrid, col=col.grid ) } box() # then the curves matlines( per, t(rates), lwd=lwd, lty=lty, col=col, type=type, ... ) # annotate them if required (every second by default ) if( ann ) { nr <- nrow( rates ) nc <- ncol( rates ) text( rep( per[nc], nr )[a.thin], rates[,nc][a.thin], paste( "", age[a.thin] ), adj=c(0,0.5), cex=cex.ann, col=if( length(col)==1 ) col else col[a.thin] ) } } Epi/R/lls.R0000644000175100001440000000172112531361077012131 0ustar hornikuserslls <- # A function that expands the functionality of ls() function( pos = 1, pat = "", all=FALSE, print=TRUE ) { # First a function that returns length/dim when you ask for it dimx <- function(dd) if (is.null(dim(dd))) length(dd) else dim(dd) # A vector of object names lll <- ls( pos=pos, pattern=pat, all.names=all ) # Are there any objects at all? if( length(lll) > 0 ) { obj.mode <- obj.clas <- obj.size <- character(0) # Then find mode, class, name and dimension of them and return it for(i in 1:length(lll)) { obj.mode[i] <- eval( parse(text = paste( "mode(", lll[i], ")"))) obj.clas[i] <- paste( eval( parse(text = paste("class(", lll[i], ")"))), collapse=" " ) obj.size[i] <- paste( eval( parse(text = paste( "dimx(", lll[i], ")"))), collapse=" " ) } dfr <- data.frame( name=lll, mode=obj.mode, class=obj.clas, size=obj.size, stringsAsFactors=FALSE ) print( invisible( dfr ), right=FALSE ) } } Epi/R/mh.R0000644000175100001440000001034512531361077011745 0ustar hornikusers# Mantel-Haenszel estimate and test mh <- function(cases, denom, compare = 1, levels = c(1, 2), by = NULL, cohort = !is.integer(denom), confidence = 0.9) { ndim <- length(dim(cases)) edgin <- names(dimnames(cases)) edgen <- paste("Dimension", 1:ndim) if (is.null(edgin)) edges <- edgen else edges <- ifelse(edgin == "", edgen, edgin) if (is.null(edges)) edges <- rep("", ndim) if(length(dim(denom)) != ndim) { stop("Cases and Pyrs arrays of unequal dimension") } if(is.numeric(compare)) { comp <- as.integer(compare) if(comp < 1 || comp > ndim) { stop("Illegal argument: compare") } } else { comp <- (1:ndim)[edges == compare] if(length(comp) != 1) { stop("Illegal argument: compare") } } if(!is.null(by)) { if (!is.numeric(by)) { mtch <- match(by, edges) if (any(is.na(mtch))) { stop("Illegal argument: by") } by <- (1:ndim)[mtch] } if (any(by < 1 | by > ndim | by == comp)) { stop("Illegal argument: by") } } gtxt <- vector("character", 3) gtxt[1] <- edges[comp] gtxt[2] <- dimnames(cases)[[comp]][levels[1]] gtxt[3] <- dimnames(cases)[[comp]][levels[2]] ctxt <- edges[-c(comp, by)] if (length(ctxt) == 0) ctxt <- as.null() others <- (1:ndim)[ - comp] select <- function(a, el) { b <- a[el] ifelse(is.na(b), 0, b) } d1 <- apply(cases, others, select, el = levels[1]) d2 <- apply(cases, others, select, el = levels[2]) if(length(d1) == 0 || length(d2) == 0) { stop("Illegal argument: levels") } y1 <- apply(denom, others, select, el = levels[1]) y2 <- apply(denom, others, select, el = levels[2]) d <- d1 + d2 y <- y1 + y2 if (cohort) { qt <- ifelse(y>0, (d1 * y2)/y, 0) rt <- ifelse(y>0, (d2 * y1)/y, 0) ut <- ifelse(y>0, d1 - ((d * y1)/y), 0) vt <- ifelse(y>0, (d * y1 * y2)/(y^2), 0) } else { s1 <- d1 + y1 s2 <- d2 + y2 t <- s1 + s2 qt <- ifelse(t>1, (d1 * y2)/t, 0) rt <- ifelse(t>1, (d2 * y1)/t, 0) ut <- ifelse(t>1, d1 - ((d * s1)/t), 0) vt <- ifelse(t>1, (d * y * s1 * s2)/((t - 1) * (t^2)), 0) } if(!is.null(by)) { if(length(by) < ndim - 1) { nby <- match(by, others) q <- apply(qt, nby, sum) r <- apply(rt, nby, sum) u <- apply(ut, nby, sum) v <- apply(vt, nby, sum) } else { q <- qt r <- rt u <- ut v <- vt } } else { q <- sum(qt) r <- sum(rt) u <- sum(ut) v <- sum(vt) } rr <- q/r se <- sqrt(v/(q * r)) ch <- (u^2)/v ef <- exp( - qnorm((1 - confidence)/2) * se) if (cohort) ty <- "Rate ratio" else ty <- "Odds ratio" res <- list(groups = gtxt, control = ctxt, type=ty, q=q, r=r, u=u, v=v, ratio = rr, se.log.ratio = se, cl.lower = rr/ef, cl.upper = rr * ef, chisq = ch, p.value = 1 - pchisq( ch, 1)) class(res) <- "mh" res } print.mh <- function(m) { cat("\n") if (!is.null(m$control)) cat("\nMantel-Haenszel comparison for: ") else cat("Comparison for: ") cat(m$groups[1], " (", m$groups[2], "versus", m$groups[3], ")\n") if (!is.null(m$control)) cat("controlled for:", m$control, "\n") cols <- c(m$type, "CL (lower)", "CL (upper)", "Chisq (1 df)", "p-value") nr <- length(m$ratio) if (is.array(m$ratio)) { dnt <- dimnames(m$ratio) size <- dim(m$ratio) nw <- length(dnt) } else { rn <- names(m$ratio) if (length(rn) > 1) dnt <- list(names(m$ratio)) else dnt <- list("") size <- nr nw <- 1 } dno <- vector("list", nw+1) so <- vector("numeric", nw+1) dno[[1]] <- dnt[[1]] dno[[2]] <- cols so[1] <- size[1] so[2] <- 5 if (nw > 1) for (i in 2:nw) { dno[[i+1]] <- dnt[[i]] so[i+1] <- size[i] } s1 <- size[1] tab <- cbind(m$ratio, m$cl.lower, m$cl.upper, m$chisq, m$p.value) # as.matrix(m$ratio, nrow=s1), # as.matrix(m$cl.lower, nrow=s1), # as.matrix(m$cl.upper, nrow=s1), # as.matrix(m$chisq, nrow=s1), # as.matrix(m$p.value, nrow=s1) ) print(array(tab, dim=so, dimnames=dno)) if (nr > 1) { Q <- sum(m$q) R <- sum(m$r) cat("\nOverall Mantel-Haenszel estimate of", m$type, ":", format(Q/R)) h <- sum(((m$q*R-m$r*Q)^2)/m$v)/(Q*R) df <- sum(m$v>0)-1 cat("\nChi-squared test of heterogeneity:", format(h), "(",df," df), p =", format(1-pchisq(h, df)), "\n") } cat("\n") } # Power calculations mh.power <- function(mh, ratio, alpha=0.05) { n.se <- log(ratio)/mh$se.log.ratio pnorm(n.se - qnorm(1-alpha/2)) } Epi/R/fit.baseline.R0000644000175100001440000000065012531361077013702 0ustar hornikusersfit.baseline <- function(y, rates.frame, start) { if (missing(start)) { ## Get starting values from logistic regression model glm.out.init <- glm(y~., family=binomial, data=rates.frame) mu.init <- fitted(glm.out.init, type="response") glm(y~-1 + ., family=binomial(link=log), data=rates.frame, mustart=mu.init) } else { glm(y~-1 + ., family=binomial(link=log), data=rates.frame, start=start) } } Epi/R/print.Icens.r0000644000175100001440000000020012531361077013562 0ustar hornikusersprint.Icens <- function( x, digits=4, scale=1, ... ) { emat <- summary.Icens( x, scale=scale ) print( round( emat, digits ) ) } Epi/R/NArray.R0000644000175100001440000000146212531361077012535 0ustar hornikusersNArray <- function( x, cells=NA ) { if( !is.list(x) ) stop("Argument must be a (named) list." ) array( cells, dimnames=x, dim=sapply( x, length ) ) } ZArray <- function( x, cells=0 ) NArray( x, cells=cells ) larray <- function( data=NA, dim, dimnames ) { if( is.list(data) ) return( array(data=NA,dim=sapply(data,length), dimnames=data) ) else if( is.list(dim) ) return( array(data=data,dim=sapply(dim,length), dimnames=dim) ) else if( is.list(dimnames) ) return( array(data=data,dim=sapply(dimnames,length), dimnames=dimnames) ) else if( !missing(dimnames) ) return( array(data=data,dim=length(data), dimnames=dimnames) ) else return( array(data=data,dim=length(data)) ) } Epi/R/print.floated.R0000644000175100001440000000160312531361077014107 0ustar hornikusers"print.floated" <- function(x, digits=max(3, getOption("digits") - 3), level = 0.95, ...) { K <- qnorm((1+level)/2) n <- length(x$coef) mat <- matrix("", n, 4) ci.mat <- matrix(0, n, 2) cm <- x$coefmat cat("Floating treatment contrasts for factor ", x$factor, "\n\n") mat[,1] <- names(x$coef) se <- sqrt(x$var) ci.mat[, 1] <- x$coef - K * se ci.mat[, 2] <- x$coef + K * se mat[,2] <- format(x$coef, digits=digits) mat[,3] <- format(se, digits=digits) ci.mat <- format(ci.mat, digits=digits) mat[,4] <- paste("(", ci.mat[,1], ",", ci.mat[,2], ")", sep="") dimnames(mat) <- list(rep("", n), c("Level", "Coefficient", "Std. Error", "95% Floating CI")) print(mat, quote=FALSE) cat("\nError limits over all contrasts: ", paste(format(c(0.99, x$limits), digits=2)[-1], collapse=","),"\n") } Epi/R/ftrend.R0000644000175100001440000000520312531361077012620 0ustar hornikusers"ftrend" <- function(object, ...) { if(length(object$xlevels) == 0) { stop("No factors in model") } xname <- names(object$xlevels)[1] if (!identical(object$contrasts[[1]], "contr.treatment")) { stop(paste("Treatment contrasts must be used for variable", xname)) } xlevels <- object$xlevels[[1]] nlevels <- length(xlevels) coefnames <- paste(xname, xlevels, sep="") ncoef <- length(coef(object)) if (!all(coefnames %in% names(coef(object)))) { stop("The model must not have an intercept term") } index1 <- match(coefnames, names(coef(object))) index2 <- (1:ncoef)[-index1] m <- length(index1) ncov <- length(index2) ## Centre the covariates according to Greenland et al (weighted mean = 0) X0 <- model.matrix(object) if (!is.null(object$weights)) { mu <- -apply(X0, 2, weighted.mean, object$weights )[index2] } else { mu <- -apply(X0, 2, mean)[index2] } mu.full <- rep(0, ncoef) mu.full[index2] <- mu X <- sweep(X0, 2, mu.full, "+") ## Information matrix with centred covariates if (!is.null(object$weights)) { J <- crossprod(X, sweep(X, 1, object$weights, "*")) } else { ## linear models J <- crossprod(X,X) } J11 <- J[index1, index1] J12 <- J[index1, index2] J21 <- J[index2, index1] J22 <- J[index2, index2] ## Variance matrix V <- solve(J) V11 <- V[index1, index1] V12 <- V[index1, index2] V21 <- V[index2, index1] V22 <- V[index2, index2] cal.V <- function(mu) { one <- as.matrix(rep(1,m)) mu <- as.matrix(mu) return(V11 - one %*% t(mu) %*% V21 - V12 %*% mu %*% t(one) + matrix(1, m, m) * (t(mu) %*% V22 %*% mu)[1,1]) } f <- function(mu) { V.mu <- cal.V(mu) # lambda is current vector of floating variances D <- sum(diag(V.mu)/lambda) - c(determinant(V.mu)$modulus) + sum(log(lambda)) - m S <- (1/sum(diag(J11)) + t(mu) %*% solve(J22) %*% mu)[1,1] grad1 <- - t(1/lambda) %*% V12 grad2 <- + sum(1/lambda) * t(mu) %*% V22 grad3 <- - t(mu) %*% solve(J22)/S attr(D,"gradient") <- 2 * (grad1 + grad2 + grad3) H1 <- V22 * sum(1/lambda) H2 <- -solve(J22)/S b <- solve(J22) %*% mu/S H3 <- 2 * b %*% t(b) attr(D, "hessian") <- 2 * (H1 + H2 + H3) return(D) } ## Initial value of lambda lambda <- diag(V[index1,index1]) ## Do the minimization nlm.out <- nlm(f, rep(0,ncov), check.analyticals=TRUE, ...) mu2 <- nlm.out$estimate ## Calculate parameter values and their covariance matrix ## if the covariates are appropriately centred coef <- coef(object)[index1] - c(crossprod(mu + mu2, coef(object)[index2])) return(list(coef=coef, vcov=cal.V(mu2))) } Epi/R/Relevel.R0000644000175100001440000000537412531361077012745 0ustar hornikusers # The levels method is already defined (in the utils package) # and hence imported in the NAMESPACE file levels.Lexis <- function( x ) { union( levels(x$lex.Cst), levels(x$lex.Xst) ) } # The Relevel method Relevel <- function (x, ...) UseMethod("Relevel") # The factor method is the default method Relevel.default <- Relevel.factor <- function( x, ref, first=TRUE, collapse="+", ... ) { # Function that collapses multiple sets of levels of a factor # if( !is.factor(x) ) { argnam <- deparse( substitute(x) ) f <- factor( x ) cat( "WARNING: ", argnam, "has been converted to a factor with levels:\n", levels( f ) ) } else f <- x # This is a copy of the relevel function from the base package: # relev <- function (x, ref, ...) { lev <- levels(x) if ( is.character( ref ) ) ref <- match(ref, lev) if ( any( is.na( ref ) ) ) stop( "any values in ref must be an existing level" ) nlev <- length( lev ) if ( any( ref < 1 ) || any( ref > nlev ) ) stop( paste( "ref=", paste( ref, collapse="," ), ": All elements must be in 1:", nlev, sep="" ) ) factor(x, levels = lev[c(ref, seq(along = lev)[-ref])]) } # If called with a non-list argument assume reshuffling of levels # if( !is.list( ref ) ) fnew <- relev( f, ref ) # If called with a list collapse levels in each list element. # if( is.list( ref ) ) { fnew <- f newnames <- levels( f ) uninames <- character( length( ref ) ) for( s in 1:length(ref) ) if ( is.character( ref[[s]] ) ) ref[[s]] <- match( ref[[s]], levels(f) ) # Check for replicates in levels to be grouped if( any( (tt<-table(unlist(ref))) > 1 ) ) stop("Factor level(s) ", levels( f )[as.numeric(names(tt)[tt>1])], " has been allocated to more than one new level.") for( s in 1:length(ref) ) { uninames[s] <- if( is.null( names( ref ) ) ) { paste( levels( f )[ref[[s]]], collapse=collapse ) } else if( names( ref )[s]=="" ) { paste( levels( f )[ref[[s]]], collapse=collapse ) } else names( ref )[s] newnames[ref[[s]]] <- rep( uninames[s], length( ref[[s]] ) ) } levels( fnew ) <- newnames if( !is.null( first ) ) { if( !first ) fnew <- factor( fnew, c( levels( f )[-unlist( ref )], uninames ) ) if( first ) fnew <- factor( fnew, c( uninames, levels( f )[-unlist( ref )] ) ) } } # This is in order to merge levels with identical names # factor( fnew, levels=levels(fnew) ) } Epi/R/contr.cum.R0000644000175100001440000000056612531361077013255 0ustar hornikuserscontr.cum <- function(n) { if (is.numeric(n) && length(n) == 1) levs <- 1:n else { levs <- n n <- length(n) } contr <- array(0, c(n, n), list(levs, levs)) contr[col(contr) <= row(contr)] <- 1 if (n < 2) stop(paste("Contrasts not defined for", n - 1, "degrees of freedom")) contr <- contr[, -1, drop = FALSE] contr } Epi/R/ci.lin.R0000644000175100001440000001643112575535340012523 0ustar hornikusers# The coef() methods in nlme and lme4 do something different # so we make a workaround by specifying our own generic methods COEF <- function( x, ... ) UseMethod("COEF") COEF.default <- function( x, ... ) coef( x, ... ) VCOV <- function( x, ... ) UseMethod("VCOV") VCOV.default <- function( x, ... ) vcov( x, ... ) # Then we can get from these methods what we want from lme, mer etc. COEF.lme <- function( x, ... ) nlme::fixed.effects( x ) COEF.mer <- function( x, ... ) lme4::fixef( x ) COEF.lmerMod <- function( x, ... ) lme4::fixef( x ) # The vcov returns a matrix with the wrong class so we strip that: VCOV.lme <- function( x, ... ) as.matrix(vcov( x )) VCOV.mer <- function( x, ... ) as.matrix(vcov( x )) VCOV.lmerMod <- function( x, ... ) as.matrix(vcov( x )) # For the rest of the non-conforming classes we then just need the methods not defined COEF.crr <- function( object, ... ) object$coef VCOV.crr <- function( object, ... ) object$var COEF.MIresult <- function( object, ... ) object$coefficients VCOV.MIresult <- function( object, ... ) object$variance COEF.mipo <- function( object, ... ) object$qbar VCOV.mipo <- function( object, ... ) object$t COEF.polr <- function( object, ... ) summary(object)$coefficients VCOV.gnlm <- function( object, ... ) object$cov VCOV.rq <- function( object, ... ) summary(object, cov=TRUE)$cov ci.lin <- function( obj, ctr.mat = NULL, subset = NULL, subint = NULL, diffs = FALSE, fnam = !diffs, vcov = FALSE, alpha = 0.05, df = Inf, Exp = FALSE, sample = FALSE ) { # First extract all the coefficients and the variance-covariance matrix cf <- COEF( obj ) vcv <- VCOV( obj ) # Workaround to expand the vcov matrix with 0s so that it matches # the coefficients vector in case of (extrinsic) aliasing. if( any( is.na( cf ) ) ) { if( inherits( obj, c("coxph") ) ) { # aliased parameters are only NAs in coef, but omitted from vcov wh <- !is.na(cf) cf <- cf[wh] vcv <- vcv[wh,wh] } else if( inherits( obj, c("clogistic") ) ) { cf[is.na(cf)] <- 0 } else { vM <- matrix( 0, length( cf ), length( cf ) ) dimnames( vM ) <- list( names( cf ), names( cf ) ) vM[!is.na(cf),!is.na(cf)] <- vcv cf[is.na(cf)] <- 0 vcv <- vM } } # Function for computing a contrast matrix for all possible # differences between a set of parameters. all.dif <- function( cf, pre=FALSE ) { nn <- length( cf ) nr <- nn * ( nn - 1 ) / 2 nam <- names( cf ) # Work out the indexes of parameter pairs to compare # xx <- numeric( 0 ) for( j in 2:nn ) xx <- c(xx, j:nn ) ctr <- cbind( rep( 1:(nn-1), (nn-1):1 ), xx ) # Now for the annotation: # Find out how large a proportion of rownames are identical i <- 1 while( all( substr( nam, 1, i ) == substr( nam[1], 1, i ) ) ) i <- i+1 # If a factor name is given, then use this, otherwise the identical part # of the parameter names if( is.character( pre ) ) { prefix <- pre pre <- TRUE } else { prefix <- substr( nam[1], 1, i-1 ) } rn <- paste( if( pre ) prefix else "", substring( nam[ctr[,1]], i ), "vs.", substring( nam[ctr[,2]], i ) ) # Finally, construct the contrast matrix and attach the rownames cm <- matrix( 0, nr, nn ) cm[cbind(1:nr,ctr[,1])] <- 1 cm[cbind(1:nr,ctr[,2])] <- -1 rownames( cm ) <- rn cm # end of function for all differences } # Were all differences requested? # if( diffs ) { if( is.character( subset ) ) { if ( inherits( obj, "lm" ) & length( grep( subset, names( obj$xlevels ) ) )>0 ) { # The case of factor level differences we find the relevant # subset of parameters by reconstructing names of parameters wf <- grep( subset, af <- names( obj$xlevels ) ) # All factor levels fn <- obj$xlevels[[af[wf]]] # Reconstruct names of relevant parameter names pnam <- paste( af[wf], fn, sep="" ) # Find them in the parameter vector wh <- match( pnam, names( coef( obj ) ) ) # Get the relevant subset, and stick in 0s for NAs cf <- coef( obj )[wh] cf[is.na( cf )] <- 0 vcv <- vcov( obj )[wh,wh] vcv[is.na( vcv )] <- 0 names( cf ) <- rownames( vcv ) <- colnames( vcv ) <- paste( subset, ": ", fn, sep="" ) } else { subset <- grep( subset, names( cf ) ) cf <- cf[subset] vcv <- vcv[subset,subset] } } else { cf <- cf[subset] vcv <- vcv[subset,subset] } ctr.mat <- all.dif( cf, pre=fnam ) } if( !diffs ) { if( is.character( subset ) ) { sb <- numeric(0) for( i in 1:length( subset ) ) sb <- c(sb,grep( subset[i], names( cf ) )) subset <- sb # unique( sb ) } if( is.character( subint ) ) { sb <- 1:length(cf) for( i in 1:length(subint) ) sb <- intersect( sb, grep(subint[i],names(cf)) ) subset <- sb # unique( sb ) } if( is.null( subset ) & is.null( subint ) ) subset <- 1:length( cf ) # Exclude units where aliasing has produced NAs. # Not needed after replacement with 0s # subset <- subset[!is.na( cf[subset] )] cf <- cf[subset] vcv <- vcv[subset,subset] if( is.null( ctr.mat ) ) { ctr.mat <- diag( length( cf ) ) rownames( ctr.mat ) <- names( cf ) } if( dim( ctr.mat )[2] != length(cf) ) stop( paste("\n Dimension of ", deparse(substitute(ctr.mat)), ": ", paste(dim(ctr.mat), collapse = "x"), ", not compatible with no of parameters in ", deparse(substitute(obj)), ": ", length(cf), sep = "")) } # Finally, here is the actual computation if( sample ) { # mvrnorm() returns a vector if sample=1, otherwise a sample x # length(cf) matrix - hence the rbind so we always get a row # matrix and res then becomes an nrow(ctr.mat) x sample matrix res <- ctr.mat %*% t( rbind(mvrnorm( sample, cf, vcv )) ) } else { ct <- ctr.mat %*% cf vc <- ctr.mat %*% vcv %*% t( ctr.mat ) se <- sqrt( diag( vc ) ) ci <- cbind( ct, se ) %*% ci.mat( alpha=alpha, df=df ) t0 <- cbind( se, ct/se, 2 * ( pnorm( -abs( ct / se ) ) ) ) colnames(t0) <- c("StdErr", "z", "P") res <- cbind(ci, t0)[, c(1, 4:6, 2:3), drop=FALSE] if( Exp ) { res <- cbind( res[,1:4 ,drop=FALSE], exp( res[,c(1,5,6),drop=FALSE] ) ) colnames( res )[5] <- "exp(Est.)" } } # Return the requested structure if( sample ) invisible( res ) else if( vcov ) invisible( list( est=ct, vcov=vc ) ) else res } # Handy wrapper ci.exp <- function( ..., Exp=TRUE, pval=FALSE ) { if( Exp ) ci.lin( ..., Exp=TRUE )[,if(pval) c(5:7,4) else 5:7 ,drop=FALSE] else ci.lin( ..., Exp=FALSE )[,if(pval) c(1,5,6,4) else c(1,5,6),drop=FALSE] } # Wrapper for predict.glm ci.pred <- function( obj, newdata, Exp = NULL, alpha = 0.05, df = Inf ) { if( !inherits( obj, "glm" ) ) stop("Not usable for non-glm objects") # get prediction and se on the link scale zz <- predict( obj, newdata=newdata, se.fit=TRUE, type="link" ) # compute ci on link scale zz <- cbind( zz$fit, zz$se.fit ) %*% ci.mat( alpha=alpha, df=df ) # transform as requested if( missing(Exp) ) { return( obj$family$linkinv(zz) ) } else { if( Exp ) { return( exp(zz) ) } else if( !Exp ) return( zz ) } } Epi/R/fit.mult.r0000644000175100001440000000260212531361077013140 0ustar hornikusersfit.mult <- function(y, rates.frame, cov.frame, start) { if (missing(start)) { ## Fit model without covariates to get initial rates estimates glm.out.rates <- fit.baseline(y, rates.frame) ## Initial values for iterative fitting lambda <- coef(glm.out.rates) beta <- rep(0, ncol(cov.frame)) } else { lambda <- start[1:ncol(rates.frame)] beta <- start[ncol(rates.frame) + 1:ncol(cov.frame)] } niter <- 1 cy <- 1 - y while(TRUE) { ## covariates model off <- log(-as.matrix(rates.frame) %*% lambda) glm.out.cov <- glm(cy ~ -1 + offset(off) + ., family=binomial(link=cloglog), data=cov.frame, start=beta, maxit=100) beta <- coef(glm.out.cov) ## rates model wgt <- exp(as.matrix(cov.frame) %*% beta) temp.rates.frame <- wgt * rates.frame glm.out.rates <- glm(y ~ -1 + ., family=binomial(link=log), data=temp.rates.frame, start=lambda, maxit=100) lambda <- coef(glm.out.rates) ## Check convergence ## Convergence <==> deviances are equal TOL <- max(glm.out.cov$control$epsilon, glm.out.rates$control$epsilon) dev1 <- glm.out.cov$deviance dev2 <- glm.out.rates$deviance if (abs(dev1 - dev2)/(0.1 + abs(dev1)) < TOL) break else niter <- niter + 1 } return(list( rates=glm.out.rates, cov=glm.out.cov, niter=niter)) } Epi/R/Wald.R0000644000175100001440000000100412531361077012220 0ustar hornikusersWald <- function( obj, H0=0, ... ) { rl <- ci.lin( obj, ..., vcov=TRUE ) beta <- rl$est vcov <- rl$vcov if( missing( H0 ) ) H0 <- beta*0 if( length(H0) != length(beta) ) stop( "H0 has length ", length(H0), " but the set of selected paramteters has length ", length(beta), ":\n", paste(round(beta,options()[["digits"]]),collapse=" ") ) chi <- t( beta-H0 ) %*% solve( vcov, beta-H0 ) df <- length(beta) p <- 1 - pchisq( chi, df ) c( "Chisq"=chi, "d.f."=df, "P"=p ) } Epi/R/plotevent.r0000644000175100001440000000217112531361077013417 0ustar hornikusersplotevent <- function(last.well,first.ill,data) { subsetdata <- data[!is.na(data[,first.ill]),c(last.well,first.ill)] subsetdata$n <- seq(1,dim(subsetdata)[1]) plot(c(subsetdata[,1], subsetdata[,2]),rep(0,2*nrow(subsetdata)), bty="n", yaxt="n", type="n", xlim=c(round(min(subsetdata[,1],subsetdata[,2],na.rm=T)),round(max(subsetdata[,1],subsetdata[,2],na.rm=T))), ylim=c(-2, max(subsetdata[,3])), xlab="Time",ylab="Conversions", main=paste("Times between ",last.well," and ",first.ill,"",sep="")) mtext(seq(0,nrow(subsetdata),5)[-1],side=2,at=seq(0,nrow(subsetdata),5)[-1],las=1,line=0) mtext("Eq Cl",side=2,at=-2,las=1, padj=0,col="blue",font=2) segments(subsetdata[,1],subsetdata[,3],subsetdata[,2],subsetdata[,3],lwd=1) left <- unique(subsetdata[,1]) right <- unique(subsetdata[,2]) names(left) <- rep("0",length(left)) names(right) <- rep("1",length(right)) MM <- sort(c(left,right)) type <- as.numeric(names(MM)) type2 <- c(type[-1],0) diff <- type-type2 int <- MM[diff<0|diff>0] mat <- matrix(int,length(int)/2,2,byrow=TRUE) d <- mat[,1] - mat[,2] segments(mat[,1][d!=0],-2,mat[,2][d!=0],-2,lwd=3,col="blue") } Epi/R/ci.pd.R0000644000175100001440000000604512531361077012340 0ustar hornikusersci.pd <- function( aa, bb=NULL, cc=NULL, dd=NULL, method = "Nc", alpha = 0.05, conf.level = 0.95, digits = 3, print = TRUE, detail.labs = FALSE ) { # Computes the approximate c.i. for the probability difference # Optional methods: # -- "AC", Agresti and Caffo, Am Statistician (2000), # -- "Nc", method 10 from Newcombe, Stat.Med. 17, (1998), pp.873 ff. if ( !(method %in% c("AC", "Nc") ) ) stop( paste('Method', method, 'unsupported: Only "Nc" and "AC" supported') ) # Fix the confidence level if( missing( alpha ) ) alpha <- 1 - conf.level if( missing( conf.level ) ) conf.level <- 1 - alpha # Allow various forms of vector and matrix input prefix <- "" if( is.vector( aa ) & length( aa ) > 1 ) prefix <- names( aa ) if( length( dim( aa ) ) == 2 ) { bb <- aa[1,2] cc <- aa[2,1] dd <- aa[2,2] aa <- aa[1,1] } if( length( dim( aa ) ) == 3 ) { prefix <- paste( if( is.null( dimnames( aa ) ) ) 1:dim(aa)[3] else dimnames( aa )[[3]], ": ", sep="" ) bb <- aa[1,2,] cc <- aa[2,1,] dd <- aa[2,2,] aa <- aa[1,1,] } if( length( dim( aa ) ) > 3 ) stop( "Maximal array dimension (3) exceeded!" ) # Function to give roots in a 2nd degree polynomial # (Polyroot does not work on vectors of coefficients) pol2 <- function( Aye, Bee, Sea ) { Dee <- Bee^2 - 4 * Aye * Sea lo <- ifelse( Dee >= 0, ( -Bee - sqrt( Dee ) ) / ( 2 * Aye ), NA ) hi <- ifelse( Dee >= 0, ( -Bee + sqrt( Dee ) ) / ( 2 * Aye ), NA ) cbind( lo, hi ) } # Put the data in the right form x1 <- aa n1 <- aa+cc p1 <- x1/n1 x2 <- bb n2 <- bb+dd p2 <- x2/n2 pd <- x1/n1 - x2/n2 z <- qnorm( 1-alpha/2 ) zz <- z^2 if ( method == "AC" ) { x1.1 <- x1+1 n1.2 <- n1+2 x2.1 <- x2+1 n2.2 <- n2+2 p1.1 <- x1.1/n1.2 p2.1 <- x2.1/n2.2 pd.1 <- p1.1 - p2.1 SE.4 <- sqrt( p1.1 * ( 1-p1.1) /n1.2 + p2.1 * ( 1-p2.1) /n2.2 ) res <- cbind( n1, p1, n2, p2, pd, pd.1 - z*SE.4, pd.1 + z*SE.4 ) } else if ( method == "Nc" ) { A1 <- 1 + zz / n1 B1 <- -2*x1/n1 - zz / n1 C1 <- ( x1 / n1 )^2 r1 <- pol2( A1, B1, C1 ) A2 <- 1 + zz / n2 B2 <- -2*x2/n2 - zz / n2 C2 <- ( x2 / n2 )^2 r2 <- pol2( A2, B2, C2 ) dlt <- sqrt( ( x1/n1 - r1[,1] )^2 + ( x2/n2 - r2[,2] )^2 ) eps <- sqrt( ( x1/n1 - r1[,2] )^2 + ( x2/n2 - r2[,1] )^2 ) res <- cbind(n1, p1, n2, p2, pd, pd-dlt, pd+eps ) } colnames( res ) <- c("n1","p1","n2","p2", "diff",paste( alpha/2 *100,"%",sep=""), paste((1-alpha/2)*100,"%",sep="") ) rownames( res ) <- prefix if( detail.labs ) rownames( res ) <- paste( prefix, ": ", aa, "/(", aa, "+", cc, ") - ", bb, "/(", bb, "+", dd, ")", sep="" ) if( print ) print( round( res, digits ) ) invisible( res ) } Epi/R/Cplot.R0000644000175100001440000000441212531361077012420 0ustar hornikusersCplot <- function( rates, age = as.numeric( rownames( rates ) ), per = as.numeric( colnames( rates ) ), grid = FALSE, c.grid = grid, ygrid = grid, col.grid = gray( 0.9 ), c.lim = NULL, ylim = range( rates[rates>0], na.rm=TRUE ), at = NULL, labels = paste( at ), c.lab = names( dimnames( rates ) )[2], ylab = deparse( substitute( rates ) ), type = "l", lwd = 2, lty = 1, col = par( "fg" ), log.ax = "y", las = 1, xannx = 1/20, ann = FALSE, cex.ann = 0.8, a.thin = seq( 1, length( age ), 2 ), ... ) { # First convert the age-period table to an age-cohort table rt <- as.table( rates ) dimnames( rt ) <- list( age = age, per = per ) rtf <- data.frame( rt ) rtf$age <- as.numeric( as.character( rtf$age ) ) rtf$per <- as.numeric( as.character( rtf$per ) ) ac <- tapply( rtf$Freq, list( rtf$age, rtf$per-rtf$age ), mean ) coh <- as.numeric( colnames( ac ) ) if( is.null( c.lim ) ) c.lim <- range( coh, na.rm=TRUE ) + c(0,diff( range( coh ) )/30) * ann # Plot the frame if( ann ) c.lim <- c.lim - c(diff( range( coh ) ) * xannx,0) matplot( coh, t(ac), type="n", xlim=c.lim, ylim=ylim, xlab=c.lab, ylab=ylab, log=log.ax, las=las, yaxt=if( !is.null( at ) ) "n" else "s" ) if( !is.null( at ) ) axis( side=2, at=at, labels=labels, yaxt="s", las=las ) # and the grid if required if( !missing( c.grid ) | !missing( grid ) ) { if( is.logical( c.grid ) & c.grid[1] ) c.grid <- nice( coh, log=par("xlog") ) abline( v=c.grid, col=col.grid ) } if( !missing( ygrid ) | !missing( grid ) ) { if( is.logical( ygrid ) & ygrid[1] ) ygrid <- nice( rates[!is.na(rates)], log=par("ylog") ) abline( h=ygrid, col=col.grid ) } box() # then the curves matlines( coh, t(ac), lwd=lwd, lty=lty, col=col, type=type, ... ) # annotate them if required (every second by default ) if( ann ) { nr <- nrow( ac ) nc <- ncol( ac ) # Find the cohorts for the last rates in each age-class c.end <- rev( per )[1] - age text( c.end[a.thin], rates[,ncol(rates)][a.thin], paste( "", age[a.thin] ), adj=c(0,0.5), cex=cex.ann, col=if( length(col)==1 ) col else col[a.thin] ) } } Epi/R/fit.add.r0000644000175100001440000000117212531361077012710 0ustar hornikusersfit.add <- function(y, rates.frame, cov.frame, start) { ## Modify covariate values rates.sum <- apply(rates.frame, 1, sum) cov.frame <- sweep(cov.frame, 1, rates.sum, "*") model.frame <- cbind(rates.frame, cov.frame) if (missing(start)) { glm.out <- fit.baseline(y, rates.frame) mu.inits <- predict(glm.out, type="response") glm.out <- glm(y~-1 + ., family=binomial(link=log), data=model.frame, mustart=mu.inits, maxit=100) } else { glm.out <- glm(y~-1 + ., family=binomial(link=log), data=model.frame, start=start, maxit=100) } return(list(rates=glm.out)) } Epi/R/as.Date.cal.yr.R0000644000175100001440000000014312531361077014002 0ustar hornikusersas.Date.cal.yr <- function( x, ... ) { structure( round( ( x - 1970 ) * 365.25 ), class="Date" ) } Epi/R/projection.ip.r0000644000175100001440000000163012532105776014164 0ustar hornikusersprojection.ip <- function( X, M, orth = FALSE, weight=rep(1,nrow(X)) ) # Generate the projection of M on span(X) w.r.t the inner # product =sum( x*w*y). # Avoids computing the entire projection matrix # X %*% inverse( X'WX ) %*% (XW)' by first computing # inverse( X'WX ) %*% (XW)'M # (which is (p x p) %*% (p x n) %*% (n x k), i.e. (p x k) ) # and then premultiplying X (n x p) hence avoiding making # a n x n matrix underway (note that n is large, p is small). # Note multiplication by W (diagional matrix) is done by # vector multiplication using the recycling facility of R. { if( nrow(X) != length(weight) ) stop( "Dimension of space and length of weights differ!" ) if( nrow(X) != nrow(M) ) stop( "Dimension of space and rownumber of model matrix differ!" ) Pp <- solve( crossprod( X * sqrt(weight) ), t( X * weight ) ) %*% M PM <- X %*% Pp if (orth) PM <- M - PM else PM } Epi/R/Lexis.diagram.R0000644000175100001440000000742312531361077014033 0ustar hornikusersLexis.diagram <- function( age = c( 0, 60), alab = "Age", date = c( 1940, 2000 ), dlab = "Calendar time", int = 5, lab.int = 2*int, col.life = "black", lwd.life = 2, age.grid = TRUE, date.grid = TRUE, coh.grid = FALSE, col.grid = gray(0.7), lwd.grid = 1, las = 1, entry.date = NA, entry.age = NA, exit.date = NA, exit.age = NA, risk.time = NA, birth.date = NA, fail = NA, cex.fail = 1.1, pch.fail = c(NA,16), col.fail = rep( col.life, 2 ), data = NULL, ... ) { # Function to plot a Lexis-diagram # # BxC, 2002, revsions in 2005 ## Get variables from data argument, if supplied, or from parent ## frame if not. entry.date <- eval(substitute(entry.date), data) entry.age <- eval(substitute(entry.age ), data) exit.date <- eval(substitute(exit.date ), data) exit.age <- eval(substitute(exit.age ), data) risk.time <- eval(substitute(birth.date), data) birth.date <- eval(substitute(birth.date), data) fail <- eval(substitute(fail ), data) # First expand intervals to both dimensions # int[1:2] <- c( int, int)[1:2] lab.int[1:2] <- c(lab.int,lab.int)[1:2] # Plot the diagram # plot( NA, xlim=date, xaxt="n", xaxs="i", xlab=dlab, ylim=age, yaxt="n", yaxs="i", ylab=alab, ... ) axis( side=1, at=seq( date[1], date[2], lab.int[2] ), las=las ) axis( side=2, at=seq( age[1], age[2], lab.int[1] ), las=las ) box( col="white" ) # par("fg") ) # Then the required grids # if ( age.grid ) { abline( h = seq( age[1], age[2], int[1] ), col=col.grid, lwd=lwd.grid ) } if ( date.grid ) { abline( v = seq( date[1], date[2], int[2] ), col=col.grid, lwd=lwd.grid ) } ages <- seq( age[1], age[2], min( int ) ) dates <- seq( date[1], date[2], min( int ) ) if ( coh.grid ) { segments( rep( date[1], length( ages ) ), ages, pmin( date[1] + ( age[2] - ages ), date[2] ), pmin( ages + ( date[2] - date[1] ), age[2] ), col=col.grid, lwd=lwd.grid ) segments( dates, rep( age[1], length( dates ) ), pmin( dates + ( age[2] - age[1] ), date[2] ), pmin( age[1] + ( date[2] - dates ), age[2] ), col=col.grid, lwd=lwd.grid ) } # Check if data for lifelines is supplied and plot lifelines if so # if( sum( !is.na( list( entry.date, entry.age, exit.date, exit.age, birth.date, risk.time ) ) ) > 2 ) { LL <- Lexis.lines( entry.date = entry.date, exit.date = exit.date, birth.date = birth.date, entry.age = entry.age, exit.age = exit.age, risk.time = risk.time, col.life = col.life, lwd.life = lwd.life, fail = fail, cex.fail = cex.fail, pch.fail = pch.fail, col.fail = col.fail, data = data ) invisible( LL ) } } Epi/R/zzz.R0000644000175100001440000000012012531361077012164 0ustar hornikusers.onLoad <- function(lib, pkg) { library.dynam("Epi", pkg, lib, local=FALSE) } Epi/R/apc.plot.R0000644000175100001440000001242512531361077013062 0ustar hornikusersplot.apc <- apc.plot <- function( x, r.txt="Rate", ... ) { if( !inherits( x, "apc" ) ) stop( "Argument must be an apc-object" ) # Determine the ranges of the horizontal axes a.lab = nice( x$Age[,1] ) cp.lab = nice( c(x$Per[,1],x$Coh[,1]), high=0.1 )[-1] # The necessary range of the two vertical axes r.rg <- range( x$Age[,-1] ) rr.rg <- range( rbind( x$Per[,-1], x$Coh[,-1] ) ) # Align the RR with the rates on an integer power of 10 rr.ref <- 10^floor( log10(r.rg[2])-log10(rr.rg[2]) ) # Find the tic-marks for the two vertical axes r.tic <- nice( r.rg, log=T, lpos=1:9 ) rr.tic <- nice( rr.rg, log=T, lpos=1:9 ) # Expand to cover it all r.tic <- sort( unique( c( r.tic, rr.tic*rr.ref ) ) ) rr.tic <- r.tic/rr.ref # Find the places for labels r.lab <- nice( r.tic, log=T, lpos=c(1,2,5) ) rr.lab <- nice( rr.tic, log=T, lpos=c(1,2,5) ) r.lab <- r.lab[ r.lab>min( r.tic) & r.labmin(rr.tic) & rr.lab0], na.rm=TRUE ), at = NULL, labels = paste( at ), a.lab = names( dimnames( rates ) )[1], ylab = deparse( substitute( rates ) ), type = "l", lwd = 2, lty = 1, col = par( "fg" ), log.ax = "y", las = 1, c.col = col, p.col = col, c.ann = FALSE, p.ann = FALSE, xannx = 1/20, cex.ann = 0.8, c.thin = seq( 2, length( age ) + length( per ) - 1, 2 ), p.thin = seq( 1, length( per ), 2 ), p.lines = TRUE, c.lines = !p.lines, ... # arguments passed on to matlines() ) { # Plot the frame if( p.ann ) a.lim <- a.lim + c(0,diff( range( age ) ) * xannx) if( c.ann ) a.lim <- a.lim - c( diff( range( age ) ) * xannx,0) matplot( age, rates, type="n", xlim=a.lim, ylim=ylim, xlab=a.lab, ylab=ylab, log=log.ax, las=las, yaxt=if( !is.null( at ) ) "n" else "s" ) if( !is.null( at ) ) axis( side=2, at=at, labels=labels, yaxt="s", las=las ) # and the grid if required: if( !missing( a.grid ) | !missing( grid ) ) { if( is.logical( a.grid ) & a.grid[1] ) a.grid <- nice( age, log=par("xlog") ) abline( v=a.grid, col=col.grid ) } if( !missing( ygrid ) | !missing( ygrid ) ) { if( is.logical( ygrid ) & ygrid[1] ) ygrid <- nice( rates[!is.na(rates)], log=par("ylog") ) abline( h=ygrid, col=col.grid ) } box() # What lines were required? if( !missing( c.lines ) & missing( p.lines ) ) p.lines <- !c.lines # Period curves: if( p.lines ){ matlines( age, rates, type=type, lwd=lwd, lty=lty, col=p.col, ... ) # annotate them if required (every second by default): if( p.ann ) { nr <- nrow( rates ) nc <- ncol( rates ) text( rep( age[nr], nc )[p.thin], rates[nr,][p.thin], paste( "", per[p.thin] ), adj=c(0,0.5), cex=cex.ann, col=if( length(p.col)==1 ) p.col else p.col[p.thin] ) } } # Cohort curves: if( c.lines ){ # First convert the age-period table to an age-cohort frame rt <- as.table( rates ) dimnames( rt ) <- list( age = age, per = per ) rtf <- data.frame( rt ) rtf$age <- as.numeric( as.character( rtf$age ) ) rtf$per <- as.numeric( as.character( rtf$per ) ) ac <- tapply( rtf$Freq, list( rtf$age, rtf$per-rtf$age ), mean ) matlines( age, ac, type=type, lwd=lwd, lty=lty, col=c.col, ... ) # annotate them if required (every other by default): if( c.ann ) { nr <- nrow( rt ) nc <- ncol( rt ) # Find the ages, cohorts and rates where the cohort curves starts a.min <- c( rev( age ), rep( age[1], nc-1 ) ) p.min <- c( rep( per[1], nr-1 ), per ) c.min <- p.min - a.min r.min <- c(rt[nr:1,1],rt[1,2:nc]) text( a.min[c.thin], r.min[c.thin], paste( "", c.min[c.thin] ), adj=c(1,0.5), cex=cex.ann, col=if( length(c.col)==1 ) c.col else c.col[c.thin] ) } } } Epi/R/stack.Lexis.R0000644000175100001440000000377112531361077013536 0ustar hornikusers# Functions to facilitate analysis of multistate models # The stack method is already defined (in the utils package) # and hence imported in the NAMESPACE file stack.Lexis <- function( x, ... ) { ## Function to stack obervations for survival analysis ## Make sure that lex.Cst and lex.Xst are factors with identical levels x <- Relevel( x ) ## Same covariates xx <- data.frame( cbind( x, lex.Tr="", lex.Fail=FALSE ) )[NULL,] tm <- tmat.Lexis( x ) for( fst in levels(factor(x$lex.Cst)) ) for( tst in levels(factor(x$lex.Xst)) ) if( !is.na(tm[fst,tst]) ) { tr = paste( fst, "->", tst , sep="" ) zz <- x[x$lex.Cst==fst,] xx <- rbind( xx, data.frame( zz, lex.Tr=tr, lex.Fail=(zz$lex.Xst==tst) ) ) } xx$lex.Tr <- factor(xx$lex.Tr) ## Reshuffle the variables wh.om <- match( "lex.Xst", names(xx) ) wh.rl <- match( c("lex.Tr","lex.Fail"), names(xx) ) xx <- xx[,c(1:wh.om,wh.rl,(wh.om+1):(wh.rl[1]-1))] attr(xx,"breaks") <- attr(x, "breaks") attr(xx,"time.scales") <- attr(x, "time.scales") class( xx ) <- c("stacked.Lexis","data.frame") xx } subset.stacked.Lexis <- function(x, ... ) { y <- subset.data.frame(x, ...) attr(y,"breaks") <- attr(x, "breaks") attr(y,"time.scales") <- attr(x, "time.scales") class(y) <- c("stacked.Lexis","data.frame") return(y) } transform.stacked.Lexis <- function(`_data`, ... ) { save.at <- attributes(`_data`) ## We can't use NextMethod here because of the special scoping rules ## used by transform.data.frame y <- base::transform.data.frame(`_data`, ...) save.at[["names"]] <- attr(y, "names") attributes(y) <- save.at y } # The tmat method tmat <- function (x, ...) UseMethod("tmat") tmat.Lexis <- function( x, Y=FALSE, mode="numeric", ... ) { zz <- table(x$lex.Cst,x$lex.Xst) class(zz) <- "matrix" if( Y ) { diag(zz) <- summary( x, simplify=FALSE )[[1]][1:nrow(zz),"Risk time:"] } else diag(zz) <- NA zz[zz==0] <- NA if( mode != "numeric" ) zz <- !is.na(zz) zz } Epi/R/summary.Icens.r0000644000175100001440000000140712531361077014135 0ustar hornikuserssummary.Icens <- function( x, scale=1, ... ) { if( attr( x, "model" ) == "MRR" ) { if( is.null( x$rates ) ) { class( x ) <- "glm" emat <- ci.lin( x ) } else { rate.est <- ci.lin( x$rates ) rate.est[,-(3:4)] <- rate.est[,-(3:4)] * scale emat <- rbind( cbind( rate.est, RR=NA )[,c(1:4,7,5:6)], ci.lin( x$cov, Exp=T ) ) } } if( attr( x, "model" ) == "AER" ) { rate.est <- ci.lin( x$rates ) rate.est[,-(3:4)] <- rate.est[,-(3:4)] * scale emat <- rate.est } if( length( x ) == 4 ) { b.est <- x[["boot.ci"]] colnames( b.est ) <- c( "Boot-med", "lo", "hi" ) emat <- cbind( emat, b.est ) } emat } Epi/vignettes/0000755000175100001440000000000012576333004013020 5ustar hornikusersEpi/vignettes/sim-Lexis.rnw0000644000175100001440000014272412531361102015423 0ustar hornikusers\SweaveOpts{results=verbatim,keep.source=TRUE,include=FALSE,eps=FALSE,prefix.string=sL} %\VignetteIndexEntry{Simulation of multistate models with multiple timescales: simLexis} \documentclass[a4paper,twoside,12pt]{report} %---------------------------------------------------------------------- % General information \newcommand{\Title}{Simulation of\\ multistate models with\\ multiple timescales:\\ \texttt{simLexis} in the \texttt{Epi} package} \newcommand{\Tit}{Multistate models with multiple timescales} \newcommand{\Version}{Version 2.2} \newcommand{\Dates}{\today} \newcommand{\Where}{SDC} \newcommand{\Homepage}{\url{http://BendixCarstensen.com/Epi}} \newcommand{\Faculty}{\begin{tabular}{rl} Bendix Carstensen & Steno Diabetes Center, Gentofte, Denmark\\ & {\small \& Department of Biostatistics, University of Copenhagen} \\ & \texttt{bxc@steno.dk}\\ & \url{http://BendixCarstensen.com} \\[1em] \end{tabular}} %---------------------------------------------------------------------- % Packages %\usepackage[inline]{showlabels} \usepackage[T1]{fontenc} \usepackage[latin1]{inputenc} \usepackage[english]{babel} \usepackage[font=it,labelfont=normalfont]{caption} \usepackage[colorlinks,urlcolor=blue,linkcolor=red]{hyperref} \usepackage[ae,hyper]{Rd} \usepackage[dvipsnames]{xcolor} \usepackage{makeidx,Sweave,floatflt,amsmath,amsfonts,amsbsy,enumitem,verbatim,dcolumn,needspace} \usepackage{booktabs,longtable,rotating,graphicx,verbatim,fancyhdr,datetime,afterpage} % \usepackage{times} \renewcommand{\textfraction}{0.0} \renewcommand{\topfraction}{1.0} \renewcommand{\bottomfraction}{1.0} \renewcommand{\floatpagefraction}{0.9} % \usepackage{mslapa} \definecolor{blaa}{RGB}{99,99,255} \DeclareGraphicsExtensions{.pdf,.jpg} %---------------------------------------------------------------------- % The usual usefuls % \input{c:/util/tex/useful.tex} \providecommand{\pmat}[1]{\text{\rm P}\left\{#1\right\}} \providecommand{\dif}{{\,\mathrm d}} % Simple insertion of figures \newcommand{\rwpre}{sL} \newcommand{\insfig}[3]{ \begin{figure}[h] \centering \includegraphics[width=#2\textwidth]{\rwpre-#1} \caption{#3} \label{fig:#1} \end{figure}} %---------------------------------------------------------------------- % Layout of pages \oddsidemargin 1mm \evensidemargin 1mm \topmargin -5mm \headheight 8mm \headsep 5mm \textheight 240mm \textwidth 165mm %\footheight 5mm \footskip 15mm \renewcommand{\topfraction}{0.9} \renewcommand{\bottomfraction}{0.9} \renewcommand{\textfraction}{0.1} \renewcommand{\floatpagefraction}{0.9} \renewcommand{\headrulewidth}{0.1pt} \setcounter{secnumdepth}{4} \setcounter{tocdepth}{4} %---------------------------------------------------------------------- % Here is the document starting with the titlepage \begin{document} %---------------------------------------------------------------------- % The title page \setcounter{page}{1} \pagenumbering{roman} \pagestyle{plain} \thispagestyle{empty} \vspace*{0.05\textheight} \flushright % The blank below here is necessary in order not to muck up the % linespacing in the title if it has more than 2 lines {\Huge \bfseries \Title }\ \\[-1.5ex] \noindent\textcolor{blaa}{\rule[-1ex]{\textwidth}{5pt}}\\[2.5ex] \large \Where \\ \Dates \\ \Homepage \\ \Version \\[1em] \normalsize Compiled \today,\ \currenttime\\ \normalsize \vfill \Faculty % End of titlepage % \newpage %---------------------------------------------------------------------- % Table of contents \tableofcontents %---------------------------------------------------------------------- % General text layout \raggedright \parindent 1em \parskip 0ex \cleardoublepage %---------------------------------------------------------------------- % General page style \pagenumbering{arabic} \setcounter{page}{1} \pagestyle{fancy} \renewcommand{\chaptermark}[1]{\markboth{\textsl{#1}}{}} \renewcommand{\sectionmark}[1]{\markright{\thesection\ \textsl{#1}}{}} \fancyhead[EL]{\bf \thepage \quad \rm \leftmark} \fancyhead[ER]{\sl \Tit} \fancyhead[OR]{\rm \rightmark \quad \bf \thepage} \fancyfoot{} %---------------------------------------------------------------------- % Here comes the substance \chapter{Using \texttt{simLexis}} \section{Introduction} This vignette explains the machinery behind simulation of life histories through multistate models implemented in \texttt{simLexis}. In \texttt{simLexis} transition rates are allowed to depend on multiple time scales, including timescales defined as time since entry to a particular state (duration). This therefore also covers the case where time \emph{at} entry into a state is an explanatory variable for the rates, since time at entry is merely time minus duration. Thus, the set-up here goes beyond Markov- and semi-Markov-models, and brings simulation based estimation of state-occupancy probabilities into the realm of realistic multistate models. The basic idea is to simulate a new \texttt{Lexis} object \cite{Plummer.2011,Carstensen.2011a} as defined in the \texttt{Epi} package for \R, based on 1) a multistate model defined by its states and the transition rates between them and 2) an initial population of individuals. Thus the output will be a \texttt{Lexis} object describing the transitions of a predefined set of persons through a multistate model. Therefore, if persons are defined to be identical at start, then calculation of the probability of being in a particular state at a given time boils down to a simple enumeration of the fraction of the persons in the particular state at the given time. Bar of course the (binomial) simulation error, but this can be brought down by simulation a sufficiently large number of persons. An observed \texttt{Lexis} object with follow-up of persons through a number of states will normally be the basis for estimation of transition rates between states, and thus will contain all information about covariates determining the occurrence rates, in particular the \emph{timescales} \cite{Iacobelli.2013}. Hence, the natural input to simulation from an estimated multistate model will typically be an object of the same structure as the originally observed. Since transitions and times are what is simulated, any values of \texttt{lex.Xst} and \texttt{lex.dur} in the input object will of course be ignored. This first chapter of this vignette shows by an example how to use the function \texttt{simLexis} and display the results. The subsequent chapter discusses in more detail how the simulation machinery is implemented and is not needed for the practical use of \texttt{simLexis}. \section{\texttt{simLexis} in practice} This section is largely a commented walk-trough of the example from the help-page of \texttt{simLexis}, with a larger number of simulated persons in order to minimize the pure simulation variation. When we want to simulate transition times through a multistate model where transition rates may depend on time since entry to the current or a previous state, it is essential that we have a machinery to keep track of the transition time on \emph{all} time scales, as well as a mechanism that can initiate a new time scale to 0 when a transition occurs to a state where we shall use time since entry as determinant of exit rates from that state. This is provided by \texttt{simLexis}. \subsection{Input for the simulation} Input for simulation of a single trajectory through a multistate model requires a representation of the \emph{current status} of a person; the starting conditions. The object that we supply to the simulation function must contain information about all covariates and all timescales upon which transitions depend, and in particular which one(s) of the timescales that are defined as time since entry into a particular state. Hence, starting conditions should be represented as a \texttt{Lexis} object (where \texttt{lex.dur} and \texttt{lex.Xst} are ignored, since there is no follow-up yet), where the time scale information is in the attributes \texttt{time.scale} and \texttt{time.since} respectively. Thus there are two main arguments to a function to simulate from a multistate model: \begin{enumerate} \item A \texttt{Lexis} object representing the initial states and covariates of the population to be simulated. This has to have the same structure as the original \texttt{Lexis} object representing the multistate model from which transition rates in the model were estimated. As noted above, the values for \texttt{lex.Xst} and \texttt{lex.dur} are not required (since these are the quantities that will be simulated). \item A transition object, representing the transition intensities between states, which should be a list of lists of intensity representations. As an intensity representation we mean a function that for given a \texttt{Lexis} object that can be used to produce estimates of the transition intensities at a set of supplied time points since the state represented in the \texttt{Lexis} object. The names of the elements of the transition object (which are lists) will be names of the \emph{transient} states, that is the states \emph{from} which a transition can occur. The names of the elements of each of these lists are the names of states \emph{to} which transitions can occur (which may be either transient or absorbing states). Hence, if the transition object is called \texttt{Tr} then \verb+TR$A$B+ (or \verb+Tr[["A"]][["B"]]+) will represent the transition intensity from state \texttt{A} to the state \texttt{B}. The entries in the transition object can be either \texttt{glm} objects, representing Poisson models for the transitions, \texttt{coxph} objects representing an intensity model along one time scale, or simply a function that takes a \texttt{Lexis} object as input returns an estimated intensity for each row. \end{enumerate} In addition to these two input items, there will be a couple of tuning parameters. The output of the function will simply be a \texttt{Lexis} object with simulated transitions between states. This will be the basis for deriving sensible statistics from the \texttt{Lexis} object --- see next section. \section{Setting up a \texttt{Lexis} object} As an example we will use the \texttt{DMlate} dataset from the \texttt{Epi} package; it is a dataset simulated to resemble a random sample of 10,000 patients from the Danish National Diabetes Register. We start by loading the \texttt{Epi} package: <>= options( width=90 ) library( Epi ) print( sessionInfo(), l=F ) @ % First we load the diabetes data and set up a simple illness-death model: <>= data(DMlate) dml <- Lexis( entry = list(Per=dodm, Age=dodm-dobth, DMdur=0 ), exit = list(Per=dox), exit.status = factor(!is.na(dodth),labels=c("DM","Dead")), data = DMlate ) @ % This is just data for a simple survival model with states ``DM'' and ``Dead''. Now we cut the follow-up at insulin start, which for the majority of patients (T2D) is a clinical indicator of deterioration of disease regulation. We therefore also introduce a new timescale, and split the non-precursor states, so that we can address the question of ever having been on insulin: <>= dmi <- cutLexis( dml, cut = dml$doins, pre = "DM", new.state = "Ins", new.scale = "t.Ins", split.states = TRUE ) summary( dmi ) str(dmi) @ % $ We can show how many person-years we have and show the number of transitions and transition rates (per 1000), using the \texttt{boxes.Lexis} function to display the states and the number of transitions between them: <>= boxes( dmi, boxpos = list(x=c(20,20,80,80), y=c(80,20,80,20)), scale.R = 1000, show.BE = TRUE ) @ % \insfig{boxes}{0.8}{Data overview for the \textrm{\tt dmi} dataset. Numbers in the boxes are person-years and the number of persons who begin, resp. end their follow-up in each state, and numbers on the arrows are no. of transitions and rates (transition intensities) per 1000 PY.} \section{Analysis of rates} In the \texttt{Lexis} object (which is just a data frame) each person is represented by one record for each transient state he occupies, thus in this case either 1 or 2 records; those who have a recorded time both without and with insulin have two records. In order to be able to fit Poisson models with occurrence rates varying by the different time-scales, we split the follow-up in 6-month intervals for modeling: <>= Si <- splitLexis( dmi, 0:30/2, "DMdur" ) dim( Si ) print( subset( Si, lex.id==97 )[,1:10], digits=6 ) @ % Note that when we split the follow-up each person's follow up now consists of many records, each with the \emph{current} values of the timescales at the start of the interval represented by the record. In the modelling we must necessarily assume that the rates are constant within each 6-month interval, but the \emph{size} of these rates we model as smooth functions of the time scales (that is the values at the beginning of each interval). The approach often used in epidemiology where one parameter is attached to each interval of time (or age) is not feasible when more than one time scale is used, because intervals are not classified the same way on all timescales. We shall use natural splines (restricted cubic splines) for the analysis of rates, and hence we must allocate knots for the splines. This is done for each of the time-scales, and separately for the transition out of states ``DM'' and ``Ins''. For age, we place the knots so that the number of events is the same between each pair of knots, but only half of this beyond each of the boundary knots, whereas for the timescales \texttt{DMdur} and \texttt{tIns} where we have observation from a well-defined 0, we put knots at 0 and place the remaining knots so that the number of events is the same between them and beyond the last: <>= nk <- 5 ( ai.kn <- with( subset(Si,lex.Xst=="Ins"), quantile( Age+lex.dur , probs=(1:nk-0.5)/nk ) ) ) ( ad.kn <- with( subset(Si,lex.Xst=="Dead"), quantile( Age+lex.dur , probs=(1:nk-0.5)/nk ) ) ) ( di.kn <- with( subset(Si,lex.Xst=="Ins"), c(0,quantile( DMdur+lex.dur, probs=(1:(nk-1))/nk ) )) ) ( dd.kn <- with( subset(Si,lex.Xst=="Dead"), c(0,quantile( DMdur+lex.dur, probs=(1:(nk-1))/nk ) )) ) ( ti.kn <- with( subset(Si,lex.Xst=="Dead(Ins)"), c(0,quantile( t.Ins+lex.dur, probs=(1:(nk-1))/nk ) )) ) @ % We then fit Poisson models to transition rates, using the wrapper \texttt{Ns} from the \texttt{Epi} package to simplify the specification of the rates: <>= library( splines ) DM.Ins <- glm( (lex.Xst=="Ins") ~ Ns( Age , knots=ai.kn ) + Ns( DMdur, knots=di.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="DM") ) DM.Dead <- glm( (lex.Xst=="Dead") ~ Ns( Age , knots=ad.kn ) + Ns( DMdur, knots=dd.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="DM") ) Ins.Dead <- glm( (lex.Xst=="Dead(Ins)") ~ Ns( Age , knots=ad.kn ) + Ns( DMdur, knots=dd.kn ) + Ns( t.Ins, knots=ti.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="Ins") ) @ % Note the similarity of the code used to fit the three models, is is mainly redefining the response variable (``to'' state) and the subset of the data used (``from'' state). \section{The mortality rates} This section discusses in some detail how to extract ad display the mortality rates from the models fitted. But it is not necessary for understanding how to use \texttt{simLexis} in practice. \subsection{Proportionality of mortality rates} Note that we have fitted separate models for the three transitions, there is no assumption of proportionality between the mortality rates from \texttt{DM} and \texttt{Ins}. However, there is nothing that prevents us from testing this assumption; we can just fit a model for the mortality rates in the entire data frame \texttt{Si}, and compare the deviance from this with the sum of the deviances from the separate models: <>= with( Si, table(lex.Cst) ) All.Dead <- glm( (lex.Xst %in% c("Dead(Ins)","Dead")) ~ Ns( Age , knots=ad.kn ) + Ns( DMdur, knots=dd.kn ) + lex.Cst + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = Si ) round( ci.exp( All.Dead ), 3 ) @ From the parameter values we would in a simple setting just claim that start of insulin-treatment was associated with a slightly more than doubling of mortality. The model \texttt{All.dead} assumes that the age- and DM-duration effects on mortality in the ``DM'' and ``Ins'' states are the same, and moreover that there is no effect of insulin duration, but merely a mortality that is larger by a multiplicative constant not depending on insulin duration. The model \texttt{DM.Dead} has 8 parameters to describe the dependency on age and DM duration, the model \texttt{Ins.Dead} has 12 for the same plus the insulin duration (a natural spline with $k$ knots gives $k-1$ parameters, and we chose $k=5$ above). We can compare the fit of the proportional hazards model with the fit of the separate models for the two mortality rates, by adding up the deviances and d.f. from these: <>= what <- c("null.deviance","df.null","deviance","df.residual") ( rD <- unlist( DM.Dead[what] ) ) ( rI <- unlist( Ins.Dead[what] ) ) ( rA <- unlist( All.Dead[what] ) ) round( c( dd <- rA-(rI+rD), "pVal"=1-pchisq(dd[3],dd[4]+1) ), 3 ) @ % Thus we see there is a substantial non-proportionality of mortality rates from the two states; but a test provides no clue whatsoever to the particular \emph{shape} of the non-proportionality. To this end, we shall explore the predicted mortalities under the two models quantitatively in more detail. Note that the reason that there is a difference in the null deviances (and a difference of 1 in the null d.f.) is that the null deviance of \texttt{All.Dead} refer to a model with a single intercept, that is a model with constant and \emph{identical} mortality rates from the states ``DM'' and ``Ins'', whereas the null models for \texttt{DM.Dead} and \texttt{Ins.Dead} have constant but \emph{different} mortality rates from the states ``DM'' and ``Ins''. This is however irrelevant for the comparison of the \emph{residual} deviances. \subsection{How the mortality rates look} If we want to see how the mortality rates are modelled in \texttt{DM.Dead} and \texttt{Ins.Dead} in relation to \texttt{All.Dead}, we make a prediction of rates for say men diagnosed in different ages and going on insulin at different times after this. So we consider men diagnosed in ages 40, 50, 60 and 70, and who either never enter insulin treatment or do it 0, 2 or 5 years after diagnosis of DM. To this end we create a prediction data frame where we have observation times from diagnosis and 12 years on (longer would not make sense as this is the extent of the data). But we start by setting up an array to hold the predicted mortality rates, classified by diabetes duration, age at diabetes onset, time of insulin onset, and of course type of model. What we want to do is to plot the age-specific mortality rates for persons not on insulin, and for persons starting insulin at different times after DM. The mortality curves start at the age where the person gets diabetes and continues 12 years; for persons on insulin they start at the age when they initiate insulin. <>= pr.rates <- NArray( list( DMdur = seq(0,12,0.1), DMage = 4:7*10, r.Ins = c(NA,0,2,5), model = c("DM/Ins","All"), what = c("rate","lo","hi") ) ) str( pr.rates ) @ % For convenience the \texttt{Epi} package contains a function that computes predicted (log-)rates with c.i. --- it is merely a wrapper for \texttt{predict.glm}: <<>>= ci.pred @ So set up the prediction data frame and modify it in loops over ages at onset and insulin onset. Note that we set \texttt{lex.dur} to 1000 in the prediction frame, so that we obtain rates in units of events per 1000 PY. <>= nd <- data.frame( DMdur = as.numeric( dimnames(pr.rates)[[1]] ), lex.Cst = factor( 1, levels=1:4, labels=levels(Si$lex.Cst) ), sex = factor( 1, levels=1:2, labels=c("M","F")), lex.dur = 1000 ) for( ia in dimnames(pr.rates)[[2]] ) { dnew <- transform( nd, Age = as.numeric(ia)+DMdur, Per = 1998+DMdur ) pr.rates[,ia,1,"DM/Ins",] <- ci.pred( DM.Dead, newdata = dnew ) pr.rates[,ia,1,"All" ,] <- ci.pred( All.Dead, newdata = dnew ) for( ii in dimnames(pr.rates)[[3]][-1] ) { dnew = transform( dnew, lex.Cst = factor( 2, levels=1:4, labels=levels(Si$lex.Cst) ), t.Ins = ifelse( (DMdur-as.numeric(ii)) >= 0, DMdur-as.numeric(ii), NA ) ) pr.rates[,ia, ii ,"DM/Ins",] <- ci.pred( Ins.Dead, newdata = dnew ) pr.rates[,ia, ii ,"All" ,] <- ci.pred( All.Dead, newdata = dnew ) } } @ % $ So for each age at DM onset we make a plot of the mortality as function of current age both for those with no insulin treatment at those that start 1, 3 and 5 years after, thus 4 curves (with c.i.). These curves are replicated with a different color for the simplified model. <>= par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6, las=1 ) plot( NA, xlim=c(40,82), ylim=c(5,300), bty="n", log="y", xlab="Age", ylab="Mortality rate per 1000 PY" ) abline( v=seq(40,80,5), h=outer(1:9,10^(0:2),"*"), col=gray(0.8) ) for( aa in 4:7*10 ) for( ii in 1:4 ) matlines( aa+as.numeric(dimnames(pr.rates)[[1]]), cbind( pr.rates[,paste(aa),ii,"DM/Ins",], pr.rates[,paste(aa),ii,"All" ,] ), type="l", lty=1, lwd=c(3,1,1), col=rep(c("red","limegreen"),each=3) ) @ % \insfig{mort-int}{0.9}{Estimated mortality rates for male diabetes patients with no insulin (lower sets of curves) and insulin (upper curves), with DM onset in age 40, 50, 60 and 70. The red curves are from the models with separate age effects for persons with and without insulin, and a separate effect of insulin duration. The green curves are from the model with common age-effects and only a time-dependent insulin effect (the classical time-dependent variable approach).} From figure \ref{fig:mort-int} we see that there is a substantial insulin-duration effect which is not accommodated by the simple model with only one time-dependent variable to describe the insulin effect. Note that the simple model (green curves) for those on insulin does not depend in insulin duration, and hence the mortality curves for those on insulin are just parallel to the mortality curves for those not on insulin, regardless of diabetes duration (or age) at the time of insulin initiation. This is the proportional hazards assumption. Thus the effect of insulin initiation is under-estimated for short duration of insulin and over-estimated for long duration of insulin. This is the major discrepancy between the two models, and illustrates the importance of being able to accommodate different time scales, but there is also a declining overall insulin effect by age which is not accommodated by the proportional hazards approach. Finally, this plot illustrates an important feature in reporting models with multiple timescales; all timescales must be represented in the predicted rates, only reporting the effect of one timescale, conditional on a fixed value of other timescales is misleading since all timescales by definition advance at the same pace. For example, the age-effect for a fixed value of insulin duration is a misnomer since it does not correspond to any real person's follow-up. \section{Input to the \texttt{simLexis} function} In order to simulate from the multistate model with the estimated transition rates, and get the follow-up of a hypothetical cohort, we must supply \emph{both} the transition rates and the structure of the model \emph{as well as} the initial cohort status to \texttt{simLexis}. \subsection{The transition object} We first put the models into an object representing the transitions; note this is a list of lists, the latter having \texttt{glm} objects as elements: <>= Tr <- list( "DM" = list( "Ins" = DM.Ins, "Dead" = DM.Dead ), "Ins" = list( "Dead(Ins)" = Ins.Dead ) ) @ % Now we have the description of the rates and of the structure of the model. The \texttt{Tr} object defines the states and models for all transitions between them; the object \verb|Tr$A$B| is the model for the transition intensity from state \texttt{A} to state \texttt{B}. \subsection{The initial cohort} We now define an initial \texttt{Lexis} object of persons with all relevant covariates defined. Note that we use \texttt{subset} to get a \texttt{Lexis} object, this conserves the \texttt{time.scale} and \texttt{time.since} attributes which is needed for the simulation (the usual ``\texttt{[}'' operator does not preserve these attributes when you select columns): <>= str( Si[NULL,1:9] ) ini <- subset(Si,FALSE,select=1:9) str( ini ) ini <- subset(Si,select=1:9)[NULL,] str( ini ) @ % We now have an empty \texttt{Lexis} object with attributes reflecting the timescales in multistate model we want to simulate, so we must now enter some data to represent the persons whose follow-up we want to simulate through the model; we set up an initial dataset with one man and one woman: <>= ini[1:2,"lex.id"] <- 1:2 ini[1:2,"lex.Cst"] <- "DM" ini[1:2,"Per"] <- 1995 ini[1:2,"Age"] <- 60 ini[1:2,"DMdur"] <- 5 ini[1:2,"sex"] <- c("M","F") ini @ % \section{Simulation of the follow-up} Now we simulate 5000 of each of these persons using the estimated model. The \texttt{t.range} argument gives the times range where the integrated intensities (cumulative rates) are evaluated and where linear interpolation is used when simulating transition times. Note that this must be given in the same units as \texttt{lex.dur} in the \texttt{Lexis} object used for fitting the models for the transitions. <>= Nsim <- 500 system.time( simL <- simLexis( Tr, ini, t.range = 12, N = Nsim ) ) @ % The result is a \texttt{Lexis} object --- a data frame representing the simulated follow-up of 10,000 persons (5000 identical men and 5000 identical women) according to the rates we estimated from the original dataset. <>= summary( simL, by="sex" ) @ % \subsection{Using other models for simulation} \subsubsection{Proportional hazards Poisson model} We fitted a proportional mortality model \texttt{All.Dead} (which fitted worse than the other two), this is a model for \emph{both} the transition from ``DM'' to ``Death'' \emph{and} from ``Ins'' to ``Dead(Ins)'', assuming that they are proportional. But it can easily be used in the simulation set-up, because the state is embedded in the model via the term \texttt{lex.Cst}, which is updated during the simulation. Simulation using this instead just requires that we supply a different transition object: <>= Tr.p <- list( "DM" = list( "Ins" = DM.Ins, "Dead" = All.Dead ), "Ins" = list( "Dead(Ins)" = All.Dead ) ) system.time( simP <- simLexis( Tr.p, ini, t.range = 12, N = Nsim ) ) summary( simP, by="sex" ) @ % \subsubsection{Proportional hazards Cox model} A third possibility would be to replace the two-time scale proportional mortality model by a one-time-scale Cox-model, using diabetes duration as time scale, and age at diagnosis of DM as (fixed) covariate: <>= library( survival ) Cox.Dead <- coxph( Surv( DMdur, DMdur+lex.dur, lex.Xst %in% c("Dead(Ins)","Dead")) ~ Ns( Age-DMdur, knots=ad.kn ) + I(lex.Cst=="Ins") + I(Per-2000) + sex, data = Si ) round( ci.exp( Cox.Dead ), 3 ) round( ci.exp( All.Dead ), 3 ) @ % Note that in order for this model to be usable for simulation, it is necessary that we use the components of the \texttt{Lexis} object to specify the survival. Each record in the data frame \texttt{Si} represents follow up from \texttt{DMdur} to \texttt{DMdur+lex.dur}, so the model is a Cox model with diabetes duration as underlying timescale and age at diagnosis, \texttt{Age-DMdur}, as covariate. Also note that we used \texttt{I(lex.Cst=="Ins")} instead of just \texttt{lex.Cst}, because \texttt{coxph} assigns design matrix columns to all levels of \texttt{lex.Cst}, also those not present in data, which would give \texttt{NA}s among the parameter estimates and \texttt{NA}s as mortality outcomes. We see that the effect of insulin and the other covariates are pretty much the same as in the two-time-scale model. We can simulate from this model too; there is no restrictions on what type of model can be used for different transitions <>= Tr.c <- list( "DM" = list( "Ins" = Tr$DM$Ins, "Dead" = Cox.Dead ), "Ins" = list( "Dead(Ins)" = Cox.Dead ) ) system.time( simC <- simLexis( Tr.c, ini, t.range = 12, N = Nsim ) ) summary( simC, by="sex" ) @ \section{Reporting the simulation results} We can now tabulate the number of persons in each state at a predefined set of times on a given time scale. Note that in order for this to be sensible, the \texttt{from} argument would normally be equal to the starting time for the simulated individuals. <>= system.time( nSt <- nState( subset(simL,sex=="M"), at=seq(0,11,0.2), from=1995, time.scale="Per" ) ) nSt[1:10,] @ % We see that as time goes by, the 5000 men slowly move away from the starting state (\texttt{DM}). Based on this table (\texttt{nSt} is a table) we can now compute the fractions in each state, or, rather more relevant, the cumulative fraction across the states in some specified order, so that a plot of the stacked probabilities can be made, using either the default rather colorful layout, or a more minimalistic version (both in figure \ref{fig:pstate0}): <>= pM <- pState( nSt, perm=c(1,2,4,3) ) head( pM ) par( mfrow=c(1,2), mar=c(3,3,1,1), mgp=c(3,1,0)/1.6 ) plot( pM ) plot( pM, border="black", col="transparent", lwd=3 ) text( rep(as.numeric(rownames(pM)[nrow(pM)-1]),ncol(pM)), pM[nrow(pM),]-diff(c(0,pM[nrow(pM),]))/5, colnames( pM ), adj=1 ) box( col="white", lwd=3 ) box() @ % \insfig{pstate0}{1.0}{Default layout of the \textrm{\tt plot.pState} graph (left), and a version with the state probabilites as lines and annotation of states.} A more useful set-up of the graph would include a more through annotation and sensible choice of colors, as seen in figure \ref{fig:pstatex}: <>= clr <- c("limegreen","orange") # expand with a lighter version of the two chosen colors clx <- c( clr, rgb( t( col2rgb( clr[2:1] )*2 + rep(255,3) ) / 3, max=255 ) ) par( mfrow=c(1,2), las=1, mar=c(3,3,4,2), mgp=c(3,1,0)/1.6 ) # Men plot( pM, col=clx ) lines( as.numeric(rownames(pM)), pM[,2], lwd=3 ) mtext( "60 year old male, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:19/20, labels=FALSE ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) # Women pF <- pState( nState( subset(simL,sex=="F"), at=seq(0,11,0.2), from=1995, time.scale="Per" ), perm=c(1,2,4,3) ) plot( pF, col=clx ) lines( as.numeric(rownames(pF)), pF[,2], lwd=3 ) mtext( "60 year old female, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:19/20, labels=FALSE ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) @ % \insfig{pstatex}{1.0}{\textrm{\tt plot.pState} graphs where persons ever on insulin are given in orange and persons never on insulin in green, and the overall survival (dead over the line) as a black line.} If we instead wanted to show the results on the age-scale, we would use age as timescale when constructing the probabilities; otherwise the code is pretty much the same as before (Figure \ref{fig:pstatey}): <>= par( mfrow=c(1,2), las=1, mar=c(3,3,4,2), mgp=c(3,1,0)/1.6 ) # Men pM <- pState( nState( subset(simL,sex=="M"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) plot( pM, col=clx, xlab="Age" ) lines( as.numeric(rownames(pM)), pM[,2], lwd=3 ) mtext( "60 year old male, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:19/20, labels=FALSE ) axis( side=4, at=1:19/20, labels=FALSE, tcl=-0.4 ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) # Women pF <- pState( nState( subset(simL,sex=="F"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) plot( pF, col=clx, xlab="Age" ) lines( as.numeric(rownames(pF)), pF[,2], lwd=3 ) mtext( "60 year old female, diagnosed 1990, aged 55", side=3, line=2.5, adj=0, col=gray(0.6) ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) axis( side=4, at=1:9/10, labels=FALSE ) axis( side=4, at=1:19/20, labels=FALSE, tcl=-0.4 ) axis( side=4, at=1:99/100, labels=FALSE, tcl=-0.3 ) @ % Note the several statements with \texttt{axis(side=4,...}; they are nesessary to get the fine tick-marks in the right hand side of the plots that you will need in order to read off the probabilities at 2006 (or 71 years). \insfig{pstatey}{1.0}{\textrm{\tt plot.pState} graphs where persons ever on insulin are given in orange and persons never on insulin in green, and the overall survival (dead over the line) as a black line.} \subsection{Comparing predictions from different models} We have actually fitted different models for the transitions, and we have simulated Lexis objects from all three approaches, so we can plot these predictions on top of each other: <>= PrM <- pState( nState( subset(simP,sex=="M"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) PrF <- pState( nState( subset(simP,sex=="F"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) CoxM <- pState( nState( subset(simC,sex=="M"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) CoxF <- pState( nState( subset(simC,sex=="F"), at=seq(0,11,0.2), from=60, time.scale="Age" ), perm=c(1,2,4,3) ) par( mfrow=c(1,2), mar=c(3,3,1,1), mgp=c(3,1,0)/1.6 ) plot( pM, border="black", col="transparent", lwd=3 ) lines( PrM, border="blue" , col="transparent", lwd=3 ) lines( CoxM, border="red" , col="transparent", lwd=3 ) text( 60.5, 0.05, "M" ) box( lwd=3 ) plot( pF, border="black", col="transparent", lwd=3 ) lines( PrF, border="blue" , col="transparent", lwd=3 ) lines( CoxF, border="red" , col="transparent", lwd=3 ) text( 60.5, 0.05, "F" ) box( lwd=3 ) @ % \insfig{comp-0}{1.0}{Comparison of the simulated state occupancy probabilities using separate Poisson models for the mortality rates with and without insulin (black) and using proportional hazards Poisson models (blue) and Cox-models with diabetes duration as timescale and age at diabetes diagnosis as covariate (red).} From figure \ref{fig:comp-0} it is clear that the two proportional hazards models (blue and red curves) produce pretty much the same estimates of the state occupancy probabilites over time, but also that they relative to the model with separately estimated transition intensities overestimates the probability of being alive without insulin and underestimates the probabilities of being dead without insulin. However both the overall survival, and the fraction of persons on insulin are quite well in agreement with the more elaborate model. Thus the proportional hazards models overestimate the relative mortality of the insulin treated diabetes patients relative to the non-insulin treated. Interestingly, we also see a bump in th estimated probabilities from the Cox-model based model, but this is entirely an artifact that comes from the estimation method for the baseline hazard of the Cox-model that lets the (cumulative) hazard have large jumps at event times where the risk set is small. So also here it shows up that an assumption of continuous underlying hazards leads to more credible estimates. \chapter{Simulation of transitions in multistate models} \section{Theory} Suppose that the rate functions for the transitions out of the current state to, say, 3 different states are $\lambda_1$, $\lambda_2$ and $\lambda_3$, and the corresponding cumulative rates are $\Lambda_1$, $\Lambda_2$ and $\Lambda_3$, and we want to simulate an exit time and an exit state (that is either 1, 2 or 3). This can be done in two slightly different ways: \begin{enumerate} \item First time, then state: \begin{enumerate} \item Compute the survival function, $S(t) = \exp\bigl(-\Lambda_1(t)-\Lambda_2(t)-\Lambda_3(t)\bigr)$ \item Simulate a random U(0,1) variate, $u$, say. \item The simulated exit time is then the solution $t_u$ to the equation $S(t_u) = u \quad \Leftrightarrow \quad \sum_j\Lambda_j(t_u) = -\log(u)$. \item A simulated transition at $t_u$ is then found by simulating a random draw from the multinomial distribution with probabilities $p_i=\lambda_i(t_u) / \sum_j\lambda_j(t_u)$. \end{enumerate} \item Separate cumulative incidences: \begin{enumerate} \item Simulate 3 independent U(0,1) random variates $u_1$, $u_2$ and $u_3$. \item Solve the equations $\Lambda_i(t_i)=-\log(u_i), i=1,2,3$ and get $(t_1,t_2,t_3)$. \item The simulated survival time is then $\min(t_1,t_2,t_3)$, and the simulated transition is to the state corresponding to this, that is $k \in \{1,2,3\}$, where $t_k=\min(t_1,t_2,t_3)$ \end{enumerate} \end{enumerate} The intuitive argument is that with three possible transition there are 3 independent processes running, but only the first transition is observed. The latter approach is used in the implementation in \texttt{simLexis}. The formal argument for the equality of the two approaches goes as follows: \begin{enumerate} \item Equality of the transition times: \begin{enumerate} \item In the first approach we simulate from a distribution with cumulative rate $\Lambda_1(t)+\Lambda_2(t)+\Lambda_3(t)$, hence from a distribution with survival function: \begin{align*} S(t) & = \exp\bigl(-(\Lambda_1(t)+\Lambda_2(t)+\Lambda_3(t))\bigr) \\ & = \exp\bigl(-\Lambda_1(t)\bigr)\times \exp\bigl(-\Lambda_2(t)\bigr)\times \exp\bigl(-\Lambda_3(t)\bigr) \end{align*} \item In the second approach we choose the smallest of three independent survival times, with survival functions $\exp(-\Lambda_i), i=1,2,3$. Now, the survival function for the minimum of three independent survival times is: \begin{align*} S_\text{min}(t) & = \pmat{\min(t_1,t_2,t_3)>t} \\ & = \pmat{t_1>t} \times \pmat{t_2>t} \times \pmat{t_3>t} \\ & = \exp\bigl(-\Lambda_1(t)\bigr)\times \exp\bigl(-\Lambda_2(t)\bigr)\times \exp\bigl(-\Lambda_3(t)\bigr) \end{align*} which is the same survival function as derived above. \end{enumerate} \item Type of transition: \begin{enumerate} \item In the first instance the probability of a transition to state $i$, conditional on the transition time being $t$, is as known from standard probability theory: $\lambda_i(t)/\bigl(\lambda_1(t)+\lambda_2(t)+\lambda_3(t)\bigr)$. \item In the second approach we choose the transition corresponding to the the smallest of the transition times. So when we condition on the event that a transition takes place at time $t$, we have to show that the conditional probability that the smallest of the three simulated transition times was actually the $i$th, is as above. But conditional on \emph{survival} till $t$, the probabilities that events of type $1,2,3$ takes place in the interval $(t,t+\dif t)$ are $\lambda_1(t)\dif t$, $\lambda_2(t)\dif t$ and $\lambda_1(t)\dif t$, respectively (assuming that the probability of more than one event in the interval of length $\dif t$ is 0). Hence the conditional probabilities \emph{given a transition time} in $(t,t+\dif t)$ is: \[ \frac{\lambda_i(t)\dif t}{\lambda_1(t)\dif t+\lambda_2(t)\dif t+\lambda_3(t)\dif t}= \frac{\lambda_i(t)}{\lambda_1(t)+\lambda_2(t)+\lambda_3(t)} \] --- exactly as above. \end{enumerate} \end{enumerate} \section{Components of \texttt{simLexis}} This section explains the actually existing code for \texttt{simLexis}, as it is in the current version of \texttt{Epi}. <>= options( keep.source=TRUE ) @ % The function \texttt{simLexis} takes a \texttt{Lexis} object as input. This defines the initial state(s) and times of the start for a number of persons. Since the purpose is to simulate a history through the estimated multistate model, the input values of the outcome variables \texttt{lex.Xst} and \texttt{lex.dur} are ignored --- the aim is to simulate values for them. Note however that the attribute \texttt{time.since} must be present in the object. This is used for initializing timescales defined as time since entry into a particular state, it is a character vector of the same length as the \texttt{time.scales} attribute, with value equal to a state name if the corresponding time scale is defined as time since entry into that state. In this example the 4th timescale is time since entry into the ``Ins'' state, and hence: <<>>= cbind( attr( ini, "time.scale" ), attr( ini, "time.since" ) ) @ % \texttt{Lexis} objects will have this attribute set for time scales created using \texttt{cutLexis}. The other necessary argument is a transition object \texttt{Tr}, which is a list of lists. The elements of the lists should be \texttt{glm} objects derived by fitting Poisson models to a \texttt{Lexis} object representing follow-up data in a multistate model. It is assumed (but not checked) that timescales enter in the model via the timescales of the \texttt{Lexis} object. Formally, there are no assumptions about how \texttt{lex.dur} enters in the model. Optional arguments are \texttt{t.range}, \texttt{n.int} or \texttt{time.pts}, specifying the times after entry at which the cumulative rates will be computed (the maximum of which will be taken as the censoring time), and \texttt{N} a scalar or numerical vector of the number of persons with a given initial state each record of the \texttt{init} object should represent. The central part of the functions uses a \texttt{do.call} / \texttt{lapply} / \texttt{split} construction to do simulations for different initial states. This is the construction in the middle that calls \texttt{simX}. \texttt{simLexis} also calls \texttt{get.next} which is further detailed below. <<>>= simLexis @ % \subsection{\texttt{simX}} \texttt{simX} is called by \texttt{simLexis} and simulates transition-times and -types for a set of patients assumed to be in the same state. It is called from \texttt{simLexis} with a data frame as argument, uses the state in \texttt{lex.Cst} to select the relevant component of \texttt{Tr} and compute predicted cumulative intensities for all states reachable from this state. Note that it is here the switch between \texttt{glm}, \texttt{coxph} and objects of class \texttt{function} is made. The dataset on which this prediction is done has \texttt{length(time.pts)} rows per person. <<>>= Epi:::simX @ % As we see, \texttt{simX} calls \texttt{sim1} which simulates the transition for one person. \subsection{\texttt{sim1}} The predicted cumulative intensities are fed, person by person, to \texttt{sim1} --- again via a \texttt{do.call} / \texttt{lapply} / \texttt{split} construction --- and the resulting time and state is appended to the \texttt{nd} data frame. This way we have simulated \emph{one} transition (time and state) for each person: <<>>= Epi:::sim1 @ % The \texttt{sim1} function uses \texttt{lint} to do linear interpolation. \subsection{\texttt{lint}} We do not use \texttt{approx} to do the linear interpolation, because this function does not do the right thing if the cumulative incidences (\texttt{ci}) are constant across a number of times. Therefore we have a custom made linear interpolator that does the interpolation exploiting the fact the the \texttt{ci} is non-decreasing and \texttt{tt} is strictly monotonously increasing: <<>>= Epi:::lint @ \subsection{\texttt{get.next}} We must repeat the simulation operation on those that have a simulated entry to a transient state, and also make sure that any time scales defined as time since entry to one of these states be initialized to 0 before a call to \texttt{simX} is made for these persons. This accomplished by the function \texttt{get.next}: <<>>= Epi:::get.next @ \subsection{\texttt{chop.lex}} The operation so far has censored individuals \texttt{max(time.pts)} after \emph{each} new entry to a transient state. In order to groom the output data we use \texttt{chop.lex} to censor all persons at the same designated time after \emph{initial} entry. <<>>= Epi:::chop.lex @ \section{Probabilities from simulated \texttt{Lexis} objects} Once we have simulated a Lexis object we will of course want to use it for estimating probabilities, so basically we will want to enumerate the number of persons in each state at a pre-specified set of time points. \subsection{\texttt{nState}} Since we are dealing with multistate model with potentially multiple time scales, it is necessary to define the timescale (\texttt{time.scale}), the starting point on this timescale (\texttt{from}) and the points after this where we compute the number of occupants in each state, (\texttt{at}). <<>>= nState @ % \subsection{\texttt{pState}, \texttt{plot.pState} and \texttt{lines.pState}} In order to plot probabilities of state-occupancy it is useful to compute cumulative probabilities across states in any given order; this is done by the function \texttt{pState}, which returns a matrix of class \texttt{pState}: <<>>= pState @ % There is also a \texttt{plot} and \texttt{lines} method for the resulting \texttt{pState} objects: <<>>= plot.pState lines.pState @ % \bibliographystyle{plain} \begin{thebibliography}{1} \bibitem{Carstensen.2011a} B.~Carstensen and M.~Plummer. \newblock Using {L}exis objects for multi-state models in {R}. \newblock {\em Journal of Statistical Software}, 38(6):1--18, 1 2011. \bibitem{Iacobelli.2013} S.~Iacobelli and B.~Carstensen. \newblock {{M}ultiple time scales in multi-state models}. \newblock {\em Stat Med}, 32(30):5315--5327, Dec 2013. \bibitem{Plummer.2011} M.~Plummer and B.~Carstensen. \newblock Lexis: An {R} class for epidemiological studies with long-term follow-up. \newblock {\em Journal of Statistical Software}, 38(5):1--12, 1 2011. \end{thebibliography} \addcontentsline{toc}{chapter}{References} \end{document} Epi/vignettes/Follow-up.rnw0000644000175100001440000003624112531361102015431 0ustar hornikusers\SweaveOpts{results=verbatim,keep.source=TRUE,include=FALSE} %\VignetteIndexEntry{Follow-up data with the Epi package} \documentclass[a4paper,twoside,12pt]{article} \usepackage[english]{babel} \usepackage{booktabs,rotating,graphicx,amsmath,verbatim,fancyhdr,Sweave} \usepackage[colorlinks,linkcolor=red,urlcolor=blue]{hyperref} \newcommand{\R}{\textsf{\bf R}} \renewcommand{\topfraction}{0.95} \renewcommand{\bottomfraction}{0.95} \renewcommand{\textfraction}{0.1} \renewcommand{\floatpagefraction}{0.9} \DeclareGraphicsExtensions{.pdf,.jpg} \setcounter{secnumdepth}{1} \setcounter{tocdepth}{1} \oddsidemargin 1mm \evensidemargin 1mm \textwidth 160mm \textheight 230mm \topmargin -5mm \headheight 8mm \headsep 5mm \footskip 15mm \begin{document} \raggedleft \pagestyle{empty} \vspace*{0.1\textheight} \Huge {\bf Follow-up data with the\\ \texttt{Epi} package} \noindent\rule[-1ex]{\textwidth}{5pt}\\[2.5ex] \Large Summer 2014 \vfill \normalsize \begin{tabular}{rl} Michael Hills & Retired \\ & Highgate, London \\[1em] Martyn Plummer & International Agency for Research on Cancer, Lyon\\ & \texttt{plummer@iarc.fr} \\[1em] Bendix Carstensen & Steno Diabetes Center, Gentofte, Denmark\\ & \small \& Department of Biostatistics, University of Copenhagen\\ & \normalsize \texttt{bxc@steno.dk} \\ & \url{www.pubhealth.ku.dk/~bxc} \end{tabular} \normalsize \newpage \raggedright \parindent 3ex \parskip 0ex \tableofcontents \cleardoublepage \setcounter{page}{1} \pagestyle{fancy} \renewcommand{\sectionmark}[1]{\markboth{\thesection #1}{\thesection \ #1}} \fancyhead[OL]{\sl Follow-up data with the \texttt{Epi} package.} \fancyhead[ER]{\sl \rightmark} \fancyhead[EL,OR]{\bf \thepage} \fancyfoot{} \renewcommand{\headrulewidth}{0.1pt} <<>>= library(Epi) print( sessionInfo(), l=F ) @ \section{Follow-up data in the \texttt{Epi} package} In the \texttt{Epi}-package, follow-up data is represented by adding some extra variables to a dataframe. Such a dataframe is called a \texttt{Lexis} object. The tools for handling follow-up data then use the structure of this for special plots, tabulations etc. Follow-up data basically consists of a time of entry, a time of exit and an indication of the status at exit (normally either ``alive'' or ``dead''). Implicitly is also assumed a status \emph{during} the follow-up (usually ``alive''). \begin{figure}[htbp] \centering \setlength{\unitlength}{1pt} \begin{picture}(210,70)(0,75) %\scriptsize \thicklines \put( 0,80){\makebox(0,0)[r]{Age-scale}} \put( 50,80){\line(1,0){150}} \put( 50,80){\line(0,1){5}} \put(100,80){\line(0,1){5}} \put(150,80){\line(0,1){5}} \put(200,80){\line(0,1){5}} \put( 50,77){\makebox(0,0)[t]{35}} \put(100,77){\makebox(0,0)[t]{40}} \put(150,77){\makebox(0,0)[t]{45}} \put(200,77){\makebox(0,0)[t]{50}} \put( 0,115){\makebox(0,0)[r]{Follow-up}} \put( 80,105){\makebox(0,0)[r]{\small Two}} \put( 90,105){\line(1,0){87}} \put( 90,100){\line(0,1){10}} \put(100,100){\line(0,1){10}} \put(150,100){\line(0,1){10}} \put(180,105){\circle{6}} \put( 95,110){\makebox(0,0)[b]{1}} \put(125,110){\makebox(0,0)[b]{5}} \put(165,110){\makebox(0,0)[b]{3}} \put( 50,130){\makebox(0,0)[r]{\small One}} \put( 60,130){\line(1,0){70}} \put( 60,125){\line(0,1){10}} \put(100,125){\line(0,1){10}} \put(130,130){\circle*{6}} \put( 80,135){\makebox(0,0)[b]{4}} \put(115,135){\makebox(0,0)[b]{3}} \end{picture} \caption{\it Follow-up of two persons} \label{fig:fu2} \end{figure} \section{Timescales} A timescale is a variable that varies deterministically \emph{within} each person during follow-up, \textit{e.g.}: \begin{itemize} \item Age \item Calendar time \item Time since treatment \item Time since relapse \end{itemize} All timescales advance at the same pace, so the time followed is the same on all timescales. Therefore, it suffices to use only the entry point on each of the time scale, for example: \begin{itemize} \item Age at entry. \item Date of entry. \item Time since treatment (\emph{at} treatment this is 0). \item Time since relapse (\emph{at} relapse this is 0).. \end{itemize} In the \texttt{Epi} package, follow-up in a cohort is represented in a \texttt{Lexis} object. A \texttt{Lexis} object is a dataframe with a bit of extra structure representing the follow-up. For the \texttt{nickel} data we would construct a \texttt{Lexis} object by: <<>>= data( nickel ) nicL <- Lexis( entry = list( per=agein+dob, age=agein, tfh=agein-age1st ), exit = list( age=ageout ), exit.status = ( icd %in% c(162,163) )*1, data = nickel ) @ The \texttt{entry} argument is a \emph{named} list with the entry points on each of the timescales we want to use. It defines the names of the timescales and the entry points. The \texttt{exit} argument gives the exit time on \emph{one} of the timescales, so the name of the element in this list must match one of the neames of the \texttt{entry} list. This is sufficient, because the follow-up time on all time scales is the same, in this case \texttt{ageout - agein}. Now take a look at the result: <<>>= str( nickel ) str( nicL ) head( nicL ) @ The \texttt{Lexis} object \texttt{nicL} has a variable for each timescale which is the entry point on this timescale. The follow-up time is in the variable \texttt{lex.dur} (\textbf{dur}ation). There is a \texttt{summary} function for \texttt{Lexis} objects that list the numer of transitions and records as well as the total follow-up time: <<>>= summary( nicL ) @ We defined the exit status to be death from lung cancer (ICD7 162,163), i.e. this variable is 1 if follow-up ended with a death from this cause. If follow-up ended alive or by death from another cause, the exit status is coded 0, i.e. as a censoring. Note that the exit status is in the variable \texttt{lex.Xst} (e\textbf{X}it \textbf{st}atus. The variable \texttt{lex.Cst} is the state where the follow-up takes place (\textbf{C}urrent \textbf{st}atus), in this case 0 (alive). It is possible to get a visualization of the follow-up along the timescales chosen by using the \texttt{plot} method for \texttt{Lexis} objects. \texttt{nicL} is an object of \emph{class} \texttt{Lexis}, so using the function \texttt{plot()} on it means that \R\ will look for the function \texttt{plot.Lexis} and use this function. <>= plot( nicL ) @ The function allows a lot of control over the output, and a \texttt{points.Lexis} function allows plotting of the endpoints of follow-up: <>= par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6 ) plot( nicL, 1:2, lwd=1, col=c("blue","red")[(nicL$exp>0)+1], grid=TRUE, lty.grid=1, col.grid=gray(0.7), xlim=1900+c(0,90), xaxs="i", ylim= 10+c(0,90), yaxs="i", las=1 ) points( nicL, 1:2, pch=c(NA,3)[nicL$lex.Xst+1], col="lightgray", lwd=3, cex=1.5 ) points( nicL, 1:2, pch=c(NA,3)[nicL$lex.Xst+1], col=c("blue","red")[(nicL$exp>0)+1], lwd=1, cex=1.5 ) @ The results of these two plotting commands are in figure \ref{fig:Lexis-diagram}. \begin{figure}[tb] \centering \label{fig:Lexis-diagram} \includegraphics[width=0.39\textwidth]{Follow-up-nicL1} \includegraphics[width=0.59\textwidth]{Follow-up-nicL2} \caption{\it Lexis diagram of the \texttt{nickel} dataset, left panel the default version, the right one with bells and whistles. The red lines are for persons with exposure$>0$, so it is pretty evident that the oldest ones are the exposed part of the cohort.} \end{figure} \section{Splitting the follow-up time along a timescale} The follow-up time in a cohort can be subdivided by for example current age. This is achieved by the \texttt{splitLexis} (note that it is \emph{not} called \texttt{split.Lexis}). This requires that the timescale and the breakpoints on this timescale are supplied. Try: <<>>= nicS1 <- splitLexis( nicL, "age", breaks=seq(0,100,10) ) summary( nicL ) summary( nicS1 ) @ So we see that the number of events and the amount of follow-up is the same in the two datasets; only the number of records differ. To see how records are split for each individual, it is useful to list the results for a few individuals: <<>>= round( subset( nicS1, id %in% 8:10 ), 2 ) @ The resulting object, \texttt{nicS1}, is again a \texttt{Lexis} object, and so follow-up may be split further along another timescale. Try this and list the results for individuals 8, 9 and 10 again: <<>>= nicS2 <- splitLexis( nicS1, "tfh", breaks=c(0,1,5,10,20,30,100) ) round( subset( nicS2, id %in% 8:10 ), 2 ) @ If we want to model the effect of these timescales we will for each interval use either the value of the left endpoint in each interval or the middle. There is a function \texttt{timeBand} which returns these. Try: <<>>= timeBand( nicS2, "age", "middle" )[1:20] # For nice printing and column labelling use the data.frame() function: data.frame( nicS2[,c("id","lex.id","per","age","tfh","lex.dur")], mid.age=timeBand( nicS2, "age", "middle" ), mid.tfh=timeBand( nicS2, "tfh", "middle" ) )[1:20,] @ Note that these are the midpoints of the intervals defined by \texttt{breaks=}, \emph{not} the midpoints of the actual follow-up intervals. This is because the variable to be used in modelling must be independent of the consoring and mortality pattern --- it should only depend on the chosen grouping of the timescale. \section{Splitting time at a specific date} If we have a recording of the date of a specific event as for example recovery or relapse, we may classify follow-up time as being before of after this intermediate event. This is achieved with the function \texttt{cutLexis}, which takes three arguments: the time point, the timescale, and the value of the (new) state following the date. Now we define the age for the nickel vorkers where the cumulative exposure exceeds 50 exposure years: <<>>= subset( nicL, id %in% 8:10 ) agehi <- nicL$age1st + 50 / nicL$exposure nicC <- cutLexis( data=nicL, cut=agehi, timescale="age", new.state=2, precursor.states=0 ) subset( nicC, id %in% 8:10 ) @ (The \texttt{precursor.states=} argument is explained below). Note that individual 6 has had his follow-up split at age 25 where 50 exposure-years were attained. This could also have been achieved in the split dataset \texttt{nicS2} instead of \texttt{nicL}, try: <<>>= subset( nicS2, id %in% 8:10 ) agehi <- nicS2$age1st + 50 / nicS2$exposure nicS2C <- cutLexis( data=nicS2, cut=agehi, timescale="age", new.state=2, precursor.states=0 ) subset( nicS2C, id %in% 8:10 ) @ Note that follow-up subsequent to the event is classified as being in state 2, but that the final transition to state 1 (death from lung cancer) is preserved. This is the point of the \texttt{precursor.states=} argument. It names the states (in this case 0, ``Alive'') that will be over-witten by \texttt{new.state} (in this case state 2, ``High exposure''). Clearly, state 1 (``Dead'') should not be updated even if it is after the time where the persons moves to state 2. In other words, only state 0 is a precursor to state 2, state 1 is always subsequent to state 2. Note if the intermediate event is to be used as a time-dependent variable in a Cox-model, then \texttt{lex.Cst} should be used as the time-dependent variable, and \texttt{lex.Xst==1} as the event. \section{Competing risks --- multiple types of events} If we want to consider death from lung cancer and death from other causes as separate events we can code these as for example 1 and 2. <<>>= data( nickel ) nicL <- Lexis( entry = list( per=agein+dob, age=agein, tfh=agein-age1st ), exit = list( age=ageout ), exit.status = ( icd > 0 ) + ( icd %in% c(162,163) ), data = nickel ) summary( nicL ) subset( nicL, id %in% 8:10 ) @ If we want to label the states, we can enter the names of these in the \texttt{states} parameter, try for example: <<>>= nicL <- Lexis( entry = list( per=agein+dob, age=agein, tfh=agein-age1st ), exit = list( age=ageout ), exit.status = ( icd > 0 ) + ( icd %in% c(162,163) ), data = nickel, states = c("Alive","D.oth","D.lung") ) summary( nicL ) @ Note that the \texttt{Lexis} function automatically assumes that all persons enter in the first level (given in the \texttt{states=} argument) When we cut at a date as in this case, the date where cumulative exposure exceeds 50 exposure-years, we get the follow-up \emph{after} the date classified as being in the new state if the exit (\texttt{lex.Xst}) was to a state we defined as one of the \texttt{precursor.states}: <<>>= nicL$agehi <- nicL$age1st + 50 / nicL$exposure nicC <- cutLexis( data = nicL, cut = nicL$agehi, timescale = "age", new.state = "HiExp", precursor.states = "Alive" ) subset( nicC, id %in% 8:10 ) summary( nicC, scale=1000 ) @ Note that the persons-years is the same, but that the number of events has changed. This is because events are now defined as any transition from alive, including the transitions to \texttt{HiExp}. Also note that (so far) it is necessary to specify the variable with the cutpoints in full, using only \texttt{cut=agehi} would give an error. \subsection{Subdivision of existing states} It may be of interest to subdivide the states following the intermediate event according to wheter the event has occurred or not. That is done by the argument \texttt{split.states=TRUE}. Moreover, it will also often be of interest to introduce a new timescale indicating the time since intermediate event. This can be done by the argument \texttt{new.scale=TRUE}, alternatively \texttt{new.scale="tfevent"}, as illustrated here: <<>>= nicC <- cutLexis( data = nicL, cut = nicL$agehi, timescale = "age", new.state = "Hi", split.states=TRUE, new.scale=TRUE, precursor.states = "Alive" ) subset( nicC, id %in% 8:10 ) summary( nicC, scale=1000 ) @ \section{Multiple events of the same type (recurrent events)} Sometimes more events of the same type are recorded for each person and one would then like to count these and put follow-up time in states accordingly. Essentially, each set of cutpoints represents progressions from one state to the next. Therefore the states should be numbered, and the numbering of states subsequently occupied be increased accordingly. This is a behaviour different from the one outlined above, and it is achieved by the argument \texttt{count=TRUE} to \texttt{cutLexis}. When \texttt{count} is set to \texttt{TRUE}, the value of the arguments \texttt{new.state} and \texttt{precursor.states} are ignored. Actually, when using the argument \texttt{count=TRUE}, the function \texttt{countLexis} is called, so an alternative is to use this directly. \end{document} Epi/MD50000644000175100001440000002203612576452270011332 0ustar hornikusers517f6211f22acaf9b2a2d8548d9387b2 *CHANGES 45fffc3ca16b80dbe0a76c305d7e2c5e *DESCRIPTION ff88a8b07bbeb1389faef9bde0e743e1 *NAMESPACE fac3c7f01ab0bd6930cf3cbea20e6bcc *R/Aplot.R 75d172080863bd6e3b2286a0c94b0d34 *R/Cplot.R 6a1c4f4cbbf509d1f3c40b9f0adb6399 *R/Icens.R d802fc697f55ab0ef5ccd45e51776e46 *R/Lexis.diagram.R c6b992622881e60fb82ee4b8c3357c9d *R/Lexis.lines.R 05c38381cc48ab25b681e8615ac31ea8 *R/Life.lines.R a25e4901b0899a6b00285713117e3f21 *R/N2Y.r 8743609003b32036a7a1d02fb4b8e17b *R/NArray.R 1d3f55ffad7833ae4615bdde4c7308c2 *R/Ns.r e9f582b5dc17d07bf2df96de74441963 *R/Pplot.R b1c0f04443499205645ad578a3dd8573 *R/ROC.R 0a7bbd37e75920b54fd0fbe00a86511d *R/Relevel.R 0e3e3d23b8ab79dcf3046828190462b2 *R/Termplot.R 28a0ec0c8584e22759b516ff69e5bd78 *R/Wald.R 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diagram, i.e. register and cohort follow-up data, in particular representation and manipulation of multistate data - the Lexis suite of functions, which includes interfaces to mstate, etm and cmprsk packages. Also contains functions for Age-Period-Cohort modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well some epidemiological datasets. License: GPL-2 URL: http://BendixCarstensen.com/Epi/ NeedsCompilation: yes Packaged: 2015-09-16 18:33:40 UTC; bendix Author: Bendix Carstensen [aut, cre], Martyn Plummer [aut], Esa Laara [ctb], Michael Hills [ctb] Maintainer: Bendix Carstensen Repository: CRAN Date/Publication: 2015-09-17 07:50:48 Epi/man/0000755000175100001440000000000012576333004011563 5ustar hornikusersEpi/man/Lexis.lines.Rd0000644000175100001440000000447712531361107014257 0ustar hornikusers\name{Lexis.lines} \alias{Lexis.lines} \title{Draw life lines in a Lexis diagram.} \description{ Add life lines to a Lexis diagram. } \usage{ Lexis.lines( entry.date = NA, exit.date = NA, birth.date = NA, entry.age = NA, exit.age = NA, risk.time = NA, col.life = "black", lwd.life = 2, fail = NA, cex.fail = 1, pch.fail = c(NA, 16), col.fail = col.life, data = NULL ) } \arguments{ \item{entry.date, entry.age, exit.date, exit.age, risk.time, birth.date}{Numerical vectors defining lifelines to be plotted in the diagram. At least three must be given to produce lines. Not all subsets of three will suffice, the given subset has to define life lines. If insufficient data is given, no life lines are produced.} \item{col.life}{Colour of the life lines.} \item{lwd.life}{Width of the life lines.} \item{fail}{Logical of event status at exit for the persons whose life lines are plotted.} \item{cex.fail}{The size of the status marks at the end of life lines.} \item{pch.fail}{The status marks at the end of the life lines.} \item{col.fail}{Colour of the marks for censorings and failures respectively.} \item{data}{Data frame in which to interpret values.} } \value{ If sufficient information on lifelines is given, a data frame with one row per person and columns with entry ages and dates, birth date, risk time and status filled in. Side effect: Life lines are added to an existing Lexis diagram. Lexis.lines adds life lines to an existing plot. } \author{ Bendix Carstensen, Steno Diabetes Center, \url{http://BendixCarstensen.com} } \examples{ Lexis.diagram( entry.age = c(3,30,45), risk.time = c(25,5,14), birth.date = c(1970,1931,1925.7), fail = c(TRUE,TRUE,FALSE) ) Lexis.lines( entry.age = sample( 0:50, 100, replace=TRUE ), risk.time = sample( 5:40, 100, r=TRUE), birth.date = sample( 1910:1980, 100, r=TRUE ), fail = sample(0:1,100,r=TRUE), cex.fail = 0.5, lwd.life = 1 ) } \keyword{ hplot } \keyword{ dplot } \seealso{ \code{\link{Lexis.diagram}}, \code{\link{Life.lines}} } Epi/man/cutLexis.Rd0000644000175100001440000001676412531361107013664 0ustar hornikusers\name{cutLexis} \alias{cutLexis} \alias{countLexis} \title{ Cut follow-up at a specified date for each person. } \description{ Follow-up intervals in a Lexis object are divided into two sub-intervals: one before and one after an intermediate event. The intermediate event may denote a change of state, in which case the entry and exit status variables in the split Lexis object are modified. } \usage{ cutLexis( data, cut, timescale = 1, new.state = nlevels(data$lex.Cst)+1, new.scale = FALSE, split.states = FALSE, progressive = FALSE, precursor.states = NULL, count = FALSE) countLexis( data, cut, timescale = 1 ) } \arguments{ \item{data}{A \code{Lexis} object.} \item{cut}{A numeric vector with the times of the intermediate event. If a time is missing (\code{NA}) then the event is assumed to occur at time \code{Inf}. \code{cut} can also be a dataframe, see details.} \item{timescale}{The timescale that \code{cut} refers to. Numeric or character.} \item{new.state}{The state to which a transition occur at time \code{cut}. It may be a single value, which is then applied to all rows of \code{data}, or a vector with a separate value for each row} \item{new.scale}{Name of the timescale defined as "time since entry to new.state". If \code{TRUE} a name for the new scale is constructed. See details.} \item{split.states}{Should states that are not precursor states be split according to whether the intermediate event has occurred.} \item{progressive}{a logical flag that determines the changes to exit status. See details.} \item{precursor.states}{an optional vector of states to be considered as "less severe" than \code{new.state}. See Details below} \item{count}{logical indicating whether the \code{countLexis} options should be used. Specifying \code{count=TRUE} amounts to calling \code{countLexis}, in which case the arguments \code{new.state}, \code{progressive} and \code{precursor.states} will be ignored. } } \value{ A \code{Lexis} object, for which each follow-up interval containing the cutpoint is split in two: one before and one after the cutpoint. An extra time-scale is added; the time since the event at \code{cut}. This is \code{NA} for any follow-up prior to the intermediate event. } \note{ The \code{cutLexis} function superficially resembles the \code{splitLexis} function. However, the \code{splitLexis} function splits on a vector of common cut-points for all rows of the Lexis object, whereas the \code{cutLexis} function splits on a single time point, which may be distinct for each row, modifies the status variables, adds a new timescale and updates the attribute "time.since". This attribute is a character vector of the same length as the "time.scales" attribute, whose value is '""' if the corresponding timescale is defined for any piece of follow-up, and if the corresponding time scale is defined by say \code{cutLexis(obj,new.state="A",new.scale=TRUE)}, it has the value "A". } \details{ The \code{cutLexis} function allows a number of different ways of specifying the cutpoints and of modifying the status variable. If the \code{cut} argument is a dataframe it must have columns \code{lex.id}, \code{cut} and \code{new.state}. The values of \code{lex.id} must be unique. In this case it is assumed that each row represents a cutpoint (on the timescale indicated in the argument \code{timescale}). This cutpoint will be applied to all records in \code{data} with the corresponding \code{lex.id}. This makes it possible to apply \code{cutLexis} to a split \code{Lexis} object. If a \code{new.state} argument is supplied, the status variable is only modified at the time of the cut point. However, it is often useful to modify the status variable after the cutpoint when an important event occurs. There are three distinct ways of doing this. If the \code{progressive=TRUE} argument is given, then a "progressive" model is assumed, in which the status can either remain the same or increase during follow-up, but never decrease. This assumes that the state variables \code{lex.Cst} and \code{lex.Xst} are either numeric or ordered factors. In this case, if \code{new.state=X}, then any exit status with a value less than \code{X} is replaced with \code{X}. The Lexis object must already be progressive, so that there are no rows for which the exit status is less than the entry status. If \code{lex.Cst} and \code{lex.Xst} are factors they must be ordered factors if \code{progressive=TRUE} is given. As an alternative to the \code{progressive} argument, an explicit vector of precursor states, that are considered less severe than the new state, may be given. If \code{new.state=X} and \code{precursor.states=c(Y,Z)} then any exit status of \code{Y} or \code{Z} in the second interval is replaced with \code{X} and all other values for the exit status are retained. The \code{countLexis} function is a variant of \code{cutLexis} when the cutpoint marks a recurrent event, and the status variable is used to count the number of events that have occurred. Times given in \code{cut} represent times of new events. Splitting with \code{countLexis} increases the status variable by 1. If the current status is \code{X} and the exit status is \code{Y} before cutting, then after cutting the entry status is \code{X}, \code{X+1} for the first and second intervals, respectively, and the exit status is \code{X+1}, \code{Y+1} respectively. Moreover the values of the status is increased by 1 for all intervals for all intervals after the cut for the person in question. Hence, a call to \code{countLexis} is needed for as many times as the person with most events. But also it is immaterial in what order the cutpoints are entered. } \author{Bendix Carstensen, Steno Diabetes Center, \email{bxc@steno.dk}, Martyn Plummer, IARC, \email{plummer@iarc.fr} } \seealso{ \code{\link{splitLexis}}, \code{\link{Lexis}}, \code{\link{summary.Lexis}}, \code{\link{boxes.Lexis}} } \examples{ # A small artificial example xx <- Lexis( entry=list(age=c(17,24,33,29),per=c(1920,1933,1930,1929)), duration=c(23,57,12,15), exit.status=c(1,2,1,2) ) xx cut <- c(33,47,29,50) cutLexis(xx, cut, new.state=3, precursor=1) cutLexis(xx, cut, new.state=3, precursor=2) cutLexis(xx, cut, new.state=3, precursor=1:2) # The same as the last example cutLexis(xx, cut, new.state=3) # The same example with a factor status variable yy <- Lexis(entry = list(age=c(17,24,33,29),per=c(1920,1933,1930,1929)), duration = c(23,57,12,15), entry.status = factor(rep("alpha",4), levels=c("alpha","beta","gamma")), exit.status = factor(c("alpha","beta","alpha","beta"), levels=c("alpha","beta","gamma"))) cutLexis(yy,c(33,47,29,50),precursor="alpha",new.state="gamma") cutLexis(yy,c(33,47,29,50),precursor=c("alpha","beta"),new.state="aleph") ## Using a dataframe as cut argument rl <- data.frame( lex.id=1:3, cut=c(19,53,26), timescale="age", new.state=3 ) rl cutLexis( xx, rl ) cutLexis( xx, rl, precursor=1 ) cutLexis( xx, rl, precursor=0:2 ) ## It is immaterial in what order splitting and cutting is done xs <- splitLexis( xx, breaks=seq(0,100,10), time.scale="age" ) xs xsC <- cutLexis(xs, rl, precursor=0 ) xC <- cutLexis( xx, rl, pre=0 ) xC xCs <- splitLexis( xC, breaks=seq(0,100,10), time.scale="age" ) xCs str(xCs) } \keyword{survival} Epi/man/bdendo.Rd0000644000175100001440000000410712531361107013303 0ustar hornikusers\name{bdendo} \alias{bdendo} \docType{data} \title{A case-control study of endometrial cancer} \description{ The \code{bdendo} data frame has 315 rows and 13 columns. These data concern a study in which each case of endometrial cancer was matched with 4 controls. Matching was by date of birth (within one year), marital status, and residence. } \format{ This data frame contains the following columns: \tabular{rl}{ \code{set}: \tab Case-control set: a numeric vector \cr \code{d}: \tab Case or control: a numeric vector (1=case, 0=control) \cr \code{gall}: \tab Gall bladder disease: a factor with levels \code{No} \code{Yes}. \cr \code{hyp}: \tab Hypertension: a factor with levels \code{No} \code{Yes}. \cr \code{ob}: \tab Obesity: a factor with levels \code{No} \code{Yes}. \cr \code{est}: \tab A factor with levels \code{No} \code{Yes}. \cr \code{dur}: \tab Duration of conjugated oestrogen therapy: an ordered factor with levels \code{0} < \code{1} < \code{2} < \code{3} < \code{4}. \cr \code{non}: \tab Use of non oestrogen drugs: a factor with levels \code{No} \code{Yes}. \cr \code{duration}: \tab Months of oestrogen therapy: a numeric vector. \cr \code{age}: \tab A numeric vector. \cr \code{cest}: \tab Conjugated oestrogen dose: an ordered factor with levels \code{0} < \code{1} < \code{2} < \code{3}. \cr \code{agegrp}: \tab A factor with levels \code{55-59} \code{60-64} \code{65-69} \code{70-74} \code{75-79} \code{80-84} \cr \code{age3}: \tab a factor with levels \code{<64} \code{65-74} \code{75+} \cr } } \source{ Breslow NE, and Day N, Statistical Methods in Cancer Research. Volume I: The Analysis of Case-Control Studies. IARC Scientific Publications, IARC:Lyon, 1980. } \examples{ data(bdendo) } \keyword{datasets} Epi/man/ci.cum.Rd0000644000175100001440000000753712575470210013244 0ustar hornikusers\name{ci.cum} \alias{ci.cum} \title{ Compute cumulative sum of estimates. } \description{ Computes the cumulative sum of parameter functions and the standard error of it. Optionally the exponential is applied to the parameter functions before it is cumulated. } \usage{ ci.cum( obj, ctr.mat = NULL, subset = NULL, intl = 1, alpha = 0.05, Exp = TRUE, ci.Exp = FALSE, sample = FALSE ) } \arguments{ \item{obj}{A model object (of class \code{lm}, \code{glm}, \code{coxph}, \code{survreg}, \code{lme},\code{mer},\code{nls},\code{gnlm}, \code{MIresult} or \code{polr}). } \item{ctr.mat}{ Contrast matrix defining the parameter functions from the parameters of the model. } \item{subset}{ Subset of the parameters of the model to which \code{ctr.mat} should be applied. } \item{intl}{ Interval length for the cumulation. Either a constant or a numerical vector of length \code{nrow(ctr.mat)}. } \item{alpha}{ Significance level used when computing confidence limits. } \item{Exp}{ Should the parameter function be exponentiated before it is cumulated?} \item{ci.Exp}{ Should the confidence limits for the cumulative rate be computed on the log-scale, thus ensuring that exp(-cum.rate) is always in [0,1]?} \item{sample}{Should a sample of the original parameters be used to compute a cumulative rate?} } \details{ The purpose of this function is to compute cumulative rate based on a model for the rates. If the model is a multiplicative model for the rates, the purpose of \code{ctr.mat} is to return a vector of rates or log-rates when applied to the coefficients of the model. If log-rates are returned from the model, the they should be exponentiated before cumulated, and the variances computed accordingly. Since the primary use is for log-linear Poisson models the \code{Exp} parameter defaults to TRUE. The \code{ci.Exp} argumen ensures that the confidence intervals for } \value{ A matrix with 4 columns: Estimate, lower and upper c.i. and standard error. If \code{sample} is TRUE, a sampled vector is reurned, if \code{sample} is numeric a matrix with \code{sample} columns is returned, each column a cumulative rate based on a random sample from the distribution of the parameter estimates. } \author{ Bendix Carstensen, \url{http://BendixCarstensen.com} } \seealso{ See also \code{\link{ci.lin}} } \examples{ # Packages required for this example library( splines ) library( survival ) data( lung ) par( mfrow=c(1,2) ) # Plot the Kaplan-meier-estimator plot( survfit( Surv( time, status==2 ) ~ 1, data=lung ) ) # Declare data as Lexis lungL <- Lexis( exit=list("tfd"=time), exit.status=(status==2)*1, data=lung ) summary( lungL ) # Cut the follow-up every 10 days sL <- splitLexis( lungL, "tfd", breaks=seq(0,1100,10) ) str( sL ) summary( sL ) # Fit a Poisson model with a natural spline for the effect of time. # Extract the variables needed D <- status(sL, "exit") Y <- dur(sL) tB <- timeBand( sL, "tfd", "left" ) MM <- ns( tB, knots=c(50,100,200,400,700), intercept=TRUE ) mp <- glm( D ~ MM - 1 + offset(log(Y)), family=poisson, eps=10^-8, maxit=25 ) # Contrast matrix to extract effects, i.e. matrix to multiply with the # coefficients to produce the log-rates: unique rows of MM, in time order. T.pt <- sort( unique( tB ) ) T.wh <- match( T.pt, tB ) Lambda <- ci.cum( mp, ctr.mat=MM[T.wh,], intl=diff(c(0,T.pt)) ) # Put the estimated survival function on top of the KM-estimator matlines( c(0,T.pt[-1]), exp(-Lambda[,1:3]), lwd=c(3,1,1), lty=1, col="Red" ) # Extract and plot the fitted intensity function lambda <- ci.lin( mp, ctr.mat=MM[T.wh,], Exp=TRUE ) matplot( T.pt, lambda[,5:7]*10^3, type="l", lwd=c(3,1,1), col="black", lty=1, log="y", ylim=c(0.2,20) ) } \keyword{models} \keyword{regression} Epi/man/blcaIT.Rd0000644000175100001440000000127612531361107013212 0ustar hornikusers\name{blcaIT} \alias{blcaIT} \docType{data} \title{Bladder cancer mortality in Italian males} \description{ Number of deaths from bladder cancer and person-years in the Italian male population 1955--1979, in ages 25--79. } % \usage{data(blcaIT)} \format{ A data frame with 55 observations on the following 4 variables: \tabular{rl}{ \code{age}: \tab Age at death. Left endpoint of age class \cr \code{period}: \tab Period of death. Left endpoint of period \cr \code{D}: \tab Number of deaths \cr \code{Y}: \tab Number of person-years. } } % \source{ % Reference to a source... % } % \references{ % Reference to a publication... % } \examples{ data(blcaIT) } \keyword{datasets} Epi/man/N.dk.Rd0000644000175100001440000000141512531361107012641 0ustar hornikusers\name{N.dk} \alias{N.dk} \docType{data} \title{Population size in Denmark} \description{ The population size at 1st January in ages 0-99. } \usage{data(N.dk)} \format{ A data frame with 7200 observations on the following 4 variables. \describe{ \item{\code{sex}}{Sex, 1:males, 2:females} \item{\code{A}}{Age. 0:0, 1:1, ..., 98:98, 99:99+} \item{\code{P}}{Year} \item{\code{N}}{Number of persons alive at 1st January year \code{P}} } } \source{ \url{http://www.statistikbanken.dk/statbank5a/SelectTable/omrade0.asp?SubjectCode=02&PLanguage=1&ShowNews=OFF} } \examples{ data(N.dk) str(N.dk) with(N.dk,addmargins(tapply(N,list(P,sex),sum),2)) with(subset(N.dk,P==max(P)),addmargins(tapply(N,list(A,sex),sum))) } \keyword{datasets} Epi/man/lls.Rd0000644000175100001440000000324012531361107012637 0ustar hornikusers\name{lls} \alias{lls} \alias{clear} \title{Functions to manage and explore the workspace } \description{These functions help you to find out what has gone wrong and to start afresh if needed. } \usage{ lls(pos = 1, pat = "", all=FALSE, print=TRUE ) clear() } \arguments{ \item{pos}{Numeric. What position in the search path do you want listed.} \item{pat}{Character. List only objects that have this string in their name.} \item{all}{Logical. Should invisible objects be printed too - see \code{\link{ls}} to which this argument is passed.} \item{print}{Logical. Should the result be printed?} } \details{\code{lls} is designed to give a quick overview of the name, mode, class and dimension of the object in your workspace. They may not always be what you think they are. \code{clear} clears all your objects from workspace, and all attached objects too --- it only leaves the loaded packages in the search path; thus allowing a fresh start without closing and restarting R. } \value{ \code{lls} returns a data frame with four character variables: code{name}, code{mode}, code{class} and code{size} and one row per object in the workspace (if \code{pos=1}). \code{size} is either the length or the dimension of the object. The data frame is by default printed with left-justified columns. } \author{\code{lls}: Unknown. Modified by Bendix Carstensen from a long forgotten snatch. \code{clear}: Michael Hills / David Clayton.} \examples{ x <- 1:10 y <- rbinom(10, 1, 0.5) m1 <- glm( y ~ x, family=binomial ) M <- matrix( 1:20, 4, 5 ) .M <- M lls() clear() lls() } \keyword{attributes}Epi/man/time.scales.Rd0000644000175100001440000000102312531361107014251 0ustar hornikusers\name{timeScales} \alias{timeScales} \title{The time scales of a Lexis object} \description{ Function to get the names of the time scales of a \code{Lexis} object. } \usage{ timeScales(x) } \arguments{ \item{x}{an object of class \code{Lexis}.} } \value{ A character vector containing the names of the variables in \code{x} that represent the time scales. Extracted from the \code{time.scales} attribute of the object. } \author{Martyn Plummer} \seealso{\code{\link{Lexis}}, \code{\link{splitLexis}}} \keyword{attribute} Epi/man/pc.lines.Rd0000644000175100001440000000273112531361107013564 0ustar hornikusers\name{pc.lines} \alias{pc.points} \alias{pc.lines} \alias{pc.matpoints} \alias{pc.matlines} \alias{cp.points} \alias{cp.lines} \alias{cp.matpoints} \alias{cp.matlines} \title{ Plot period and cohort effects in an APC-frame. } \description{ When an APC-frame has been produced by \code{\link{apc.frame}}, this function draws curves in the period/cohort part of the frame. } \usage{ pc.points( x, y, ... ) pc.lines( x, y, ... ) pc.matpoints( x, y, ... ) pc.matlines( x, y, ... ) cp.points( x, y, ... ) cp.lines( x, y, ... ) cp.matpoints( x, y, ... ) cp.matlines( x, y, ... ) } \arguments{ \item{x}{vector of \code{x}-coordinates.} \item{y}{vector of \code{y}-coordinates.} \item{...}{Further parameters to be transmitted to points, lines, matpoints or matlines used for plotting the three sets of curves.} } \details{Since the Age-part of the frame is referred to by its real coordinates plotting in the calendar time part requires translation and scaling to put things correctly there, that is done by the functions \code{pc.points} etc. The functions \code{cp.points} etc. are just synonyms for these, in recognition of the fact that you can never remember whether it is "pc" pr "cp". } \value{ The functions return nothing. } \author{ Bendix Carstensen, Steno Diabetes Center, \url{http://BendixCarstensen.com} } \seealso{ \code{\link{apc.frame}}, \code{\link{apc.fit}}, \code{\link{plot.apc}}, \code{\link{lines.apc}} } \keyword{hplot} Epi/man/Lexis.Rd0000644000175100001440000001617012531361107013137 0ustar hornikusers\name{Lexis} \alias{Lexis} \title{Create a Lexis object} \description{ Create an object of class \code{Lexis} to represent follow-up in multiple states on multiple time scales. } \usage{ Lexis( entry, exit, duration, entry.status = 0, exit.status = 0, id, data, merge=TRUE, states, tol=.Machine$double.eps^0.5, keep.dropped=FALSE ) } \arguments{ \item{entry}{a named list of entry times. Each element of the list is a numeric variable representing the entry time on the named time scale. All time scales must have the same units (e.g. years). The names of the timescales must be different from any column name in \code{data}.} \item{exit}{a named list of exit times.} \item{duration}{a numeric vector giving the duration of follow-up.} \item{entry.status}{a vector or a factor giving the status at entry} \item{exit.status}{a vector or factor giving status at exit. Any change in status during follow-up is assumed to take place exactly at the exit time.} \item{id}{a vector giving a unique identity value for each person represented in the Lexis object. Defaults to \code{1:nrow(data)}} \item{data}{an optional data frame, list, or environment containing the variables. If not found in \code{data}, the variables are taken from the environment from which \code{Lexis} was called.} \item{merge}{a logical flag. If \code{TRUE} then the \code{data} argument will be coerced to a data frame and then merged with the resulting \code{Lexis} object.} \item{states}{A vector of labels for the states. If given, the state variables \code{lex.Cst} and \code{lex.Xst} are returned as factors with identical levels attributes equal to \code{states}.} \item{tol}{Numerical tolerance for follow-up time. Rows with duration less than this value are automatically dropped.} \item{keep.dropped}{Logical. Should dropped rows from \code{data} be saved as an attribute with the object for inspection?} } \details{ The analysis of long-term population-based follow-up studies typically requires multiple time scales to be taken into account, such as age, calender time, or time since an event. A \code{Lexis} object is a data frame with additional attributes that allows these multiple time dimensions of follow-up to be managed. Separate variables for current end exit state allows representation of multistate data. Lexis objects are named after the German demographer Wilhelm Lexis (1837-1914), who is credited with the invention of the "Lexis diagram" for representing population dynamics simultaneously by several timescales. The \code{Lexis} function can create a minimal \code{Lexis} object with only those variables required to define the follow-up history in each row. Additional variables can be merged into the \code{Lexis} object using the \code{merge} method for \code{Lexis} objects. The latter is the default. There are also \code{merge}, \code{subset} and \code{transform} methods for \code{Lexis} objects. They work as the corresponding methods for data-frames but ensures that the result is a \code{Lexis} object. } \note{ Only two of the three arguments \code{entry}, \code{exit} and \code{duration} need to be given. If the third parameter is missing, it is imputed. \code{entry}, \code{exit} must be numeric, using \code{\link{Date}} variables will cause some of the utilites to crash. Transformation by \code{\link{cal.yr}} is recommended. If only either \code{exit} or \code{duration} are supplied it is assumed that \code{entry} is 0. This is only meaningful (and therefore checked) if there is only one timescale. If any of \code{entry.status} or \code{exit.status} are of mode character, they will both be converted to factors. If \code{entry.status} is not given, then its class is automatically set to that of \code{exit.status}. If \code{exit.status} is a character or factor, the value of \code{entry.status} is set to the first level. This may be highly undesirable, and therefore noted. For example, if \code{exit.status} is character the first level will be the first in the alphabetical ordering; slightly unfortunate if values are \code{c("Well","Diseased")}. If \code{exit.status} is logical, the value of \code{entry.status} set to \code{FALSE}. If \code{exit.status} is numeric, the value of \code{entry.status} set to 0. If \code{entry.status} or \code{exit.status} are factors or character, the corresponding state variables in the returned \code{Lexis} object, \code{lex.Cst} and \code{lex.Xst} will be (unordered) factors with identical set of levels, namely the union of the levels of \code{entry.status} and \code{exit.status}. } \value{ An object of class \code{Lexis}. This is represented as a data frame with a column for each time scale (with neaes equal to the union of the names of \code{entry} and \code{exit}), and additional columns with the following names: \item{lex.id}{Identification of the persons.} \item{lex.dur}{Duration of follow-up.} \item{lex.Cst}{Entry status (\code{C}urrent \code{st}ate), i.e. the state in which the follow up takes place.} \item{lex.Xst}{Exit status (e\code{X}it \code{st}ate), i.e. that state taken up after \code{dur} in \code{lex.Cst}.} If \code{merge=TRUE} (the default) then the \code{Lexis} object will also contain all variables from the \code{data} argument. } \author{Martyn Plummer with contributions from Bendix Carstensen} \examples{ # A small bogus cohort xcoh <- structure( list( id = c("A", "B", "C"), birth = c("14/07/1952", "01/04/1954", "10/06/1987"), entry = c("04/08/1965", "08/09/1972", "23/12/1991"), exit = c("27/06/1997", "23/05/1995", "24/07/1998"), fail = c(1, 0, 1) ), .Names = c("id", "birth", "entry", "exit", "fail"), row.names = c("1", "2", "3"), class = "data.frame" ) # Convert the character dates into numerical variables (fractional years) xcoh <- cal.yr( xcoh, format="\%d/\%m/\%Y", wh=2:4 ) # See how it looks xcoh str( xcoh ) # Define as Lexis object with timescales calendar time and age Lcoh <- Lexis( entry = list( per=entry ), exit = list( per=exit, age=exit-birth ), exit.status = fail, data = xcoh ) Lcoh # Using character states may have undesired effects: xcoh$Fail <- c("Dead","Well","Dead") Lexis( entry = list( per=entry ), exit = list( per=exit, age=exit-birth ), exit.status = Fail, data = xcoh ) # ...unless you order the levels correctly ( xcoh$Fail <- factor( xcoh$Fail, levels=c("Well","Dead") ) ) Lexis( entry = list( per=entry ), exit = list( per=exit, age=exit-birth ), exit.status = Fail, data = xcoh ) } \seealso{ \code{\link{plot.Lexis}}, \code{\link{splitLexis}}, \code{\link{cutLexis}}, \code{\link{merge.Lexis}}, \code{\link{subset.Lexis}}, \code{\link{transform.Lexis}}, \code{\link{summary.Lexis}}, \code{\link{timeScales}}, \code{\link{timeBand}}, \code{\link{entry}}, \code{\link{exit}}, \code{\link{dur}} } \keyword{survival} Epi/man/DMlate.Rd0000644000175100001440000000373712531361107013226 0ustar hornikusers\name{DMlate} \Rdversion{1.1} \alias{DMlate} \alias{DMrand} \docType{data} \title{ The Danish National Diabetes Register. } \description{ These two datasets each contain a random sample of 10,000 persons from the Danish National Diabetes Register. \code{DMrand} is a random sample from the register, whereas \code{DMlate} is a random sample among those with date of diagnosis after 1.1.1995. All dates are radomly jittered by adding a U(-7,7) (days). } \usage{data(DMrand) data(DMlate)} \format{ A data frame with 10000 observations on the following 7 variables. \describe{ \item{\code{sex}}{Sex, a factor with levels \code{M} \code{F}} \item{\code{dobth}}{Date of birth} \item{\code{dodm}}{Date of inclusion in the register} \item{\code{dodth}}{Date of death} \item{\code{dooad}}{Date of 2nd prescription of OAD} \item{\code{doins}}{Date of 2nd insulin prescription} \item{\code{dox}}{Date of exit from follow-up.} } } \details{All dates are given in fractions of years, so 1997.00 corresponds to 1 January 1997 and 1997.997 to 31 December 1997. } \source{ Danish National Board of Health. } \references{ B Carstensen, JK Kristensen, P Ottosen and K Borch-Johnsen: The Danish National Diabetes Register: Trends in incidence, prevalence and mortality, Diabetologia, 51, pp 2187--2196, 2008. In partucular see the appendix at the end of the paper. } \examples{ data(DMlate) str(DMlate) dml <- Lexis( entry=list(Per=dodm, Age=dodm-dobth, DMdur=0 ), exit=list(Per=dox), exit.status=factor(!is.na(dodth),labels=c("DM","Dead")), data=DMlate ) # Cut the follow-up at insulin start, and introduce a new timescale, # and split non-precursor states system.time( dmi <- cutLexis( dml, cut = dml$doins, pre = "DM", new.state = "Ins", new.scale = "t.Ins", split.states = TRUE ) ) summary( dmi ) } \keyword{datasets} Epi/man/mh.Rd0000644000175100001440000000677612531361107012472 0ustar hornikusers\name{mh} \alias{mh} \title{ Mantel-Haenszel analyses of cohort and case-control studies } \description{ This function carries out Mantel-Haenszel comparisons in tabulated data derived from both cohort and case-control studies. } \usage{ mh(cases, denom, compare=1, levels=c(1, 2), by=NULL, cohort=!is.integer(denom), confidence=0.9) } \arguments{ \item{cases}{ the table of case frequencies (a multiway array). } \item{denom}{ the denominator table. For cohort studies this should be a table of person-years observation, while for case-control studies it should be a table of control frequencies. } \item{compare}{ the dimension of the table which defines the comparison groups (can be referred to either by number or by name). The default is the first dimension of the table. } \item{levels}{ a vector identifying (either by number or by name) the two groups to be compared. The default is the first two levels of the selected dimension. } \item{by}{ the dimensions not to be collapsed in the Mantel-Haenszel computations. Thus, this argument defines the structure of the resulting tables of estimates and tests. } \item{cohort}{ an indicator whether the data derive from a cohort or a case-control study. If the denominator table is stored as an integer, a case-control study is assumed. } \item{confidence}{ the approximate coverage probability for the confidence intervals to be computed. }} \value{ A list giving tables of rate (odds) ratio estimates, their standard errors (on a log scale), lower and upper confidence limits, chi-squared tests (1 degree of freedom) and the corresponding p-values. The result list also includes numerator and denominator of the Mantel-Haenszel estimates (q, r), and score test statistics and score variance (u, v). } \section{Side Effects}{ None } \details{ Multiway tables of data are accepted and any two levels of any dimension can be chosen as defining the comparison groups. The rate (odds) ratio estimates and the associated significance tests may be collapsed over all the remaining dimensions of the table, or over selected dimensions only, so that tables of estimates and tests are computed. } \references{ Clayton, D. and Hills, M. : Statistical Models in Epidemiology, Oxford University Press (1993). } \seealso{ \code{\link{Lexis}} } \examples{ # If d and y are 3-way tables of cases and person-years # observation formed by tabulation by two confounders # (named "C1" and "C2") an exposure of interest ("E"), # the following command will calculate an overall # Mantel-Haenszel comparison of the first two exposure # groups. # # Generate some bogus data dnam <- list( E=c("low","medium","high"), C1=letters[1:2], C2=LETTERS[1:4] ) d <- array( sample( 2:80, 24), dimnames=dnam, dim=sapply( dnam, length ) ) y <- array( abs( rnorm( 24, 227, 50 ) ), dimnames=dnam, dim=sapply( dnam, length ) ) mh(d, y, compare="E") # # Or, if exposure levels named "low" and "high" are to be # compared and these are not the first two levels of E : # mh(d, y, compare="E", levels=c("low", "high")) # # If we wish to carry out an analysis which controls for C1, # but examines the results at each level of C2: # mh(d, y, compare="E", by="C2") # # It is also possible to look at rate ratios for every # combination of C1 and C2 : # mh(d, y, compare="E", by=c("C1", "C2")) # # If dimensions and levels of the table are unnamed, they must # be referred to by number. # } \keyword{htest} Epi/man/Y.dk.Rd0000644000175100001440000000315712531361107012661 0ustar hornikusers\name{Y.dk} \alias{Y.dk} \docType{data} \title{Population risk time in Denmark} \description{ Risk time (person-years) in the Danish population, classified by sex, age, period and date of birth in 1-year classes. This corresponds to triangles in a Lexis diagram. } \usage{data(Y.dk)} \format{ A data frame with 13860 observations on the following 6 variables. \describe{ \item{\code{sex}}{Sex. 1:males, 2:females} \item{\code{A}}{One-year age class} \item{\code{P}}{Period} \item{\code{C}}{Birth cohort} \item{\code{Y}}{Person-years} \item{\code{upper}}{Indicator of upper triangle in the Lexis diagram} } } \details{ The risk time is computed from the population size figures in \code{\link{N.dk}}, using the formulae devised in B. Carstensen: "Demography and epidemiology: Age-Period-Cohort models in the computer age", \url{http://biostat.ku.dk/reports/2006/ResearchReport06-1.pdf/}, later published as: B. Carstensen: Age-period-cohort models for the Lexis diagram. Statistics in Medicine, 10; 26(15):3018-45, 2007. } \source{ \url{http://www.statistikbanken.dk/statbank5a/SelectTable/omrade0.asp?SubjectCode=02&PLanguage=1&ShowNews=OFF} } \examples{ data(Y.dk) str(Y.dk) # Compute mean age, period for the triangles attach( Y.dk ) age <- A + (1+upper)/3 per <- P + (2-upper)/3 # Plot a Lexis diagram library( Epi ) Lexis.diagram( age=c(0,10), date=c(1990,2000), coh.grid=TRUE, int=1 ) box() # Print the person-years for males there text( per[sex==1], age[sex==1], formatC( Y[sex==1]/1000, format="f", digits=1 ) ) } \keyword{datasets} Epi/man/effx.Rd0000644000175100001440000000737412531361107013011 0ustar hornikusers\name{effx} \alias{effx} \title{Function to calculate effects} \description{ The function calculates the effects of an exposure on a response, possibly stratified by a stratifying variable, and/or controlled for one or more confounding variables. } \usage{ effx( response, type = "metric", fup = NULL, exposure, strata = NULL, control = NULL, weights = NULL, eff = NULL, alpha = 0.05, base = 1, digits = 3, data = NULL ) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{response}{The \code{response} variable - must be numeric or logical. If logical, \code{TRUE} is considered the outcome.} \item{type}{The type of response\code{type} - must be one of "metric", "binary", "failure", or "count"} \item{fup}{The \code{fup} variable contains the follow-up time for a failure response}. This must be numeric. \item{exposure}{The \code{exposure} variable can be numeric or a factor} \item{strata}{The \code{strata} stratifying variable - must be a factor} \item{control}{The \code{control} variable(s) (confounders) - these are passed as a list if there are more than one.} \item{weights}{Frequency weights for binary response only} \item{eff}{How should effects be measured. If \code{response} is binomial, the default is "OR" (odds-ratio) with "RR" (relative risk) as an option. If \code{response} is failure, the default is "RR" (rate-ratio) with "RD" (rate difference) as an option.} \item{base}{Baseline for the effects of a categorical exposure, either a number or a name of the level. Defaults to 1} \item{digits}{Number of significant digits for the effects, default 3} \item{alpha}{1 - confidence level} \item{data}{\code{data} refers to the data used to evaluate the function} } \details{The function is a wrapper for glm. Effects are calculated as differences in means for a metric response, odds ratios/relatiev risks for a binary response, and rate ratios/rate differences for a failure or count response. The k-1 effects for a categorical exposure with k levels are relative to a baseline which, by default, is the first level. The effect of a metric (quantitative) exposure is calculated per unit of exposure. The exposure variable can be numeric or a factor, but if it is an ordered factor the order will be ignored.} \value{ % ~Describe the value returned % If it is a LIST, use \item{comp1 }{Effects of exposure} \item{comp2 }{Tests of significance} % ... } \references{ www.mhills.pwp.blueyonder.co.uk } \author{Michael Hills} %\note{ ~~further notes~~ } %\seealso{ ~~objects to See Also as \code{\link{~~fun~~}}, ~~~ } \examples{ library(Epi) data(births) births$hyp <- factor(births$hyp,labels=c("normal","hyper")) births$sex <- factor(births$sex,labels=c("M","F")) # bweight is the birth weight of the baby in gms, and is a metric # response (the default) # effect of hypertension on birth weight effx(bweight,exposure=hyp,data=births) # effect of hypertension on birth weight stratified by sex effx(bweight,exposure=hyp,strata=sex,data=births) # effect of hypertension on birth weight controlled for sex effx(bweight,exposure=hyp,control=sex,data=births) # effect of gestation time on birth weight effx(bweight,exposure=gestwks,data=births) # effect of gestation time on birth weight stratified by sex effx(bweight,exposure=gestwks,strata=sex,data=births) # effect of gestation time on birth weight controlled for sex effx(bweight,exposure=gestwks,control=sex,data=births) # lowbw is a binary response coded 1 for low birth weight and 0 otherwise # effect of hypertension on low birth weight effx(lowbw,type="binary",exposure=hyp,data=births) effx(lowbw,type="binary",exposure=hyp,eff="RR",data=births) } \keyword{ models } \keyword{ regression } Epi/man/lines.apc.Rd0000644000175100001440000001001712531361107013721 0ustar hornikusers\name{apc.lines} \alias{lines.apc} \alias{apc.lines} \title{ Plot APC-estimates in an APC-frame. } \description{ When an APC-frame has been produced by \code{\link{apc.frame}}, this function draws a set of estimates from an APC-fit in the frame. An optional drift parameter can be added to the period parameters and subtracted from the cohort and age parameters. } \usage{ \method{lines}{apc}( x, P, C, scale = c("log","ln","rates","inc","RR"), frame.par = options()[["apc.frame.par"]], drift = 0, c0 = median( C[,1] ), a0 = median( A[,1] ), p0 = c0 + a0, ci = rep( FALSE, 3 ), lwd = c(3,1,1), lty = 1, col = "black", type = "l", knots = FALSE, ... ) apc.lines( x, P, C, scale = c("log","ln","rates","inc","RR"), frame.par = options()[["apc.frame.par"]], drift = 0, c0 = median( C[,1] ), a0 = median( A[,1] ), p0 = c0 + a0, ci = rep( FALSE, 3 ), lwd = c(3,1,1), lty = 1, col = "black", type = "l", knots = FALSE, ... ) } \arguments{ \item{x}{If an \code{apc}-object, (see \code{\link{apc.fit}}), then the arguments \code{P}, \code{C}, \code{c0}, \code{a0} and \code{p0} are ignored, and the estimates from \code{x} plotted. Can also be a 4-column matrix with columns age, age-specific rates, lower and upper c.i., in which case period and cohort effects are taken from the arguments \code{P} and \code{C}. } \item{P}{Period effects. Rate-ratios. Same form as for the age-effects.} \item{C}{Cohort effects. Rate-ratios. Same form as for the age-effects.} \item{scale}{Are effects given on a log-scale? Character variable, one of \code{"log"}, \code{"ln"}, \code{"rates"}, \code{"inc"}, \code{"RR"}. If \code{"log"} or \code{"ln"} it is assumed that effects are log(rates) and log(RRs) otherwise the actual effects are assumed given in \code{A}, \code{P} and \code{C}. If \code{A} is of class \code{apc}, it is assumed to be \code{"rates"}.} \item{frame.par}{2-element vector with the cohort-period offset and RR multiplicator. This will typically be the result from the call of \code{\link{apc.frame}}. See this for details.} \item{drift}{The drift parameter to be added to the period effect. If \code{scale="log"} this is assumed to be on the log-scale, otherwise it is assumed to be a multiplicative factor per unit of the first columns of \code{A}, \code{P} and \code{C} } \item{c0}{The cohort where the drift is assumed to be 0; the subtracted drift effect is \code{drift*(C[,1]-c0)}.} \item{a0}{The age where the drift is assumed to be 0.} \item{p0}{The period where the drift is assumed to be 0.} \item{ci}{Should confidence interval be drawn. Logical or character. If character, any occurrence of \code{"a"} or \code{"A"} produces confidence intervals for the age-effect. Similarly for period and cohort.} \item{lwd}{Line widths for estimates, lower and upper confidence limits.} \item{lty}{Linetypes for the three effects.} \item{col}{Colours for the three effects.} \item{type}{What type of lines / points should be used.} \item{knots}{Should knots from the model be shown?} \item{...}{Further parameters to be transmitted to \code{points} \code{lines}, \code{matpoints} or \code{matlines} used for plotting the three sets of curves.} } \details{ The drawing of three effects in an APC-frame is a rather trivial task, and the main purpose of the utility is to provide a function that easily adds the functionality of adding a drift so that several sets of lines can be easily produced in the same frame. } \value{ \code{APC.lines} returns (invisibly) a list of three matrices of the effects plotted. } \author{ Bendix Carstensen, Steno Diabetes Center, \url{http://BendixCarstensen.com} } \seealso{ \code{\link{apc.frame}}, \code{\link{pc.lines}}, \code{\link{apc.fit}}, \code{\link{apc.plot}} } \keyword{hplot} Epi/man/testisDK.Rd0000644000175100001440000000115412531361107013601 0ustar hornikusers\name{testisDK} \alias{testisDK} \docType{data} \title{Testis cancer incidence in Denmark, 1943--1996 } \description{Number of testiscancer cases and male person-years in the Danish population 1943--1996} \usage{data(testisDK)} \format{ A data frame with 4860 observations on the following 4 variables. \describe{ \item{\code{A}}{Age class, 0,1,2,...,89} \item{\code{P}}{Year, 1943,...,1996} \item{\code{D}}{Number of testis cancer cases} \item{\code{Y}}{Person years} } } \source{The Danish Cancer Registry } \examples{ data(testisDK) head(testisDK) } \keyword{datasets} Epi/man/stack.Lexis.Rd0000644000175100001440000000541612531361107014244 0ustar hornikusers\name{stack.Lexis} \Rdversion{1.1} \alias{stack.Lexis} \alias{tmat} \alias{tmat.Lexis} \title{ Functions to facilitate analysis of multistate models. } \description{ \code{stack.Lexis} produces a stacked object suited for analysis of several transition intensities simultaneously. } \usage{ \method{stack}{Lexis}(x, ...) tmat( x, ... ) \method{tmat}{Lexis}(x, Y=FALSE, mode = "numeric", ...) } \arguments{ \item{x}{A \code{\link{Lexis}} object.} \item{Y}{Logical. Should the risk time be put in the diagonal? This is a facility which is used by \code{\link{boxes.Lexis}}.} \item{mode}{Should the matrix be returned as a numeric matrix with \code{NA}s at unused places or (\code{mode="logical"}) as a logical matrix with \code{FALSE} on the diagonal.} \item{\dots}{Not used.} } \value{ \code{tmat.Lexis} returns a square transition matrix, classified by the levels of \code{lex.Cst} and \code{lex.Xst}, for every transition occurring the entry is the number of transitions occurring and \code{NA} in all oter entries. If \code{Y=TRUE}, the diagonal will contain the risk time in each of the states. \code{stack.Lexis} returns a dataframe to be used for analysis of multistate data when all transitions are modelled together, for example if some parameters are required to be the same for different transitions. The dataframe has class \code{stacked.Lexis}, and inherits the attributes \code{time.scales} and \code{breaks} from the \code{Lexis} object, and so function \code{\link{timeBand}} applies to a \code{stacked.Lexis} object too. The dataframe has same variables as the original \code{Lexis} object, but with each record duplicated as many times as there are possible exits from the current state, \code{lex.Cst}. Two variables are added: \code{lex.Fail}, an indicator of wheter an event for the transition named in the factor \code{lex.Tr} has occurred or not. \code{lex.Tr} is a factor with levels made up of combinations of the levels of \code{lex.Cst} and \code{lex.Xst} that do occur together in \code{x}, joined by a "\code{->}".} \author{ Bendix Carstensen, \email{bxc@steno.dk}, \url{http://BendixCarstensen.com} } \examples{ data(DMlate) str(DMlate) dml <- Lexis( entry=list(Per=dodm, Age=dodm-dobth, DMdur=0 ), exit=list(Per=dox), exit.status=factor(!is.na(dodth),labels=c("DM","Dead")), data=DMlate ) dmi <- cutLexis( dml, cut=dml$doins, new.state="Ins", pre="DM" ) summary( dmi ) ls.dmi <- stack( dmi ) str( ls.dmi ) # Check that all the transitions and person-years got across. with( ls.dmi, rbind( table(lex.Fail,lex.Tr), tapply(lex.dur,lex.Tr,sum) ) ) } \seealso{ \code{\link{splitLexis}} \code{\link{cutLexis}} \code{\link{Lexis}} } \keyword{survival} Epi/man/fit.mult.Rd0000644000175100001440000000357312531361107013620 0ustar hornikusers\name{fit.mult} \alias{fit.mult} \title{ Fits a multiplicative relative risk model to interval censored data. } \description{ Utility function. The model fitted assumes a piecewise constant baseline rate in intervals specified by the argument \code{breaks}, and a multiplicative relative risk function. } \usage{ fit.mult( y, rates.frame, cov.frame, start ) } \arguments{ \item{y}{Binary vector of outcomes} \item{rates.frame}{Dataframe expanded from the original data by \code{\link{expand.data}}, cooresponding to covariates for the rate parameters.} \item{cov.frame}{ do., but covariates corresponding to the \code{formula} argument of \code{\link{Icens}}} \item{start}{Starting values for the rate parameters. If not supplied, then starting values are generated.} } \details{ The model is fitted by alternating between two generalized linear models where one estimates the underlying rates in the intervals, and the other estimates the log-relative risks. } \value{ A list with three components: \item{rates}{A glm object from a binomial model with log-link, estimating the baseline rates.} \item{cov}{A glm object from a binomial model with complementary log-log link, estimating the log-rate-ratios} \item{niter}{Nuber of iterations, a scalar} } \references{ B Carstensen: Regression models for interval censored survival data: application to HIV infection in Danish homosexual men. Statistics in Medicine, 15(20):2177-2189, 1996. CP Farrington: Interval censored survival data: a generalized linear modelling approach. Statistics in Medicine, 15(3):283-292, 1996. } \author{ Martyn Plummer, \email{plummer@iarc.fr}, Bendix Carstensen, \email{bxc@steno.dk} } \seealso{ \code{\link{Icens}} \code{\link{fit.add}} } \examples{ data( HIV.dk ) } \keyword{ models } \keyword{ regression } \keyword{ survival } Epi/man/hivDK.Rd0000644000175100001440000000260412531361107013055 0ustar hornikusers\name{ hivDK } \alias{ hivDK } \docType{ data } \title{ hivDK: seroconversion in a cohort of Danish men} \description{ Data from a survey of HIV-positivity of a cohort of Danish men followed by regular tests from 1983 to 1989. } \usage{ data(hivDK) } \format{ A data frame with 297 observations on the following 7 variables. \describe{ \item{\code{id}}{ID of the person} \item{\code{entry}}{Date of entry to the study. Date variable.} \item{\code{well}}{Date last seen seronegative. Date variable.} \item{\code{ill}}{Date first seen seroconverted. Date variable.} \item{\code{bth}}{Year of birth minus 1950.} \item{\code{pyr}}{Annual number of sexual partners.} \item{\code{us}}{Indicator of wheter the person has visited the USA.} } } \source{ Mads Melbye, Statens Seruminstitut. } \references{ Becker N.G. and Melbye M.: Use of a log-linear model to compute the empirical survival curve from interval-censored data, with application to data on tests for HIV-positivity, Australian Journal of Statistics, 33, 125--133, 1990. Melbye M., Biggar R.J., Ebbesen P., Sarngadharan M.G., Weiss S.H., Gallo R.C. and Blattner W.A.: Seroepidemiology of HTLV-III antibody in Danish homosexual men: prevalence, transmission and disease outcome. British Medical Journal, 289, 573--575, 1984. } \examples{ data(hivDK) str(hivDK) } \keyword{ datasets } Epi/man/boxes.MS.Rd0000644000175100001440000003642012531361107013511 0ustar hornikusers\name{boxes.MS} \Rdversion{1.1} \alias{tbox} \alias{dbox} \alias{fillarr} \alias{boxarr} \alias{boxes} \alias{boxes.Lexis} \alias{boxes.matrix} \alias{boxes.MS} \title{ Draw boxes and arrows for illustration of multistate models. } \description{ Boxes can be drawn with text (\code{tbox}) or a cross (\code{dbox}), and arrows pointing between the boxes (\code{boxarr}) can be drawn automatically not overlapping the boxes. The \code{boxes} method for \code{\link{Lexis}} objects generates displays of states with person-years and transitions with events or rates. } \usage{ tbox( txt, x, y, wd, ht, font=2, lwd=2, col.txt=par("fg"), col.border=par("fg"), col.bg="transparent" ) dbox( x, y, wd, ht=wd, font=2, lwd=2, cwd=5, col.cross=par("fg"), col.border=par("fg"), col.bg="transparent" ) boxarr( b1, b2, offset=FALSE, pos=0.45, ... ) \method{boxes}{Lexis}( obj, boxpos = FALSE, wmult = 1.15, hmult = 1.15, cex = 1.45, show = inherits( obj, "Lexis" ), show.Y = show, scale.Y = 1, digits.Y = 1, show.BE = FALSE, BE.sep = c("",""," ",""), show.D = show, scale.D = FALSE, digits.D = as.numeric(as.logical(scale.D)), show.R = is.numeric(scale.R), scale.R = 1, digits.R = as.numeric(as.logical(scale.R)), DR.sep = if( show.D ) c("\n(",")") else c("",""), eq.wd = TRUE, eq.ht = TRUE, wd, ht, subset = NULL, exclude = NULL, font = 2, lwd = 2, col.txt = par("fg"), col.border = col.txt, col.bg = "transparent", col.arr = par("fg"), lwd.arr = 2, font.arr = 2, pos.arr = 0.45, txt.arr = NULL, col.txt.arr = col.arr, offset.arr = 2, ... ) \method{boxes}{matrix}( obj, ... ) \method{boxes}{MS}( obj, sub.st, sub.tr, cex=1.5, ... ) fillarr( x1, y1, x2, y2, gap=2, fr=0.8, angle=17, lwd=2, length=par("pin")[1]/30, ... ) } \arguments{ \item{txt}{Text to be placed inside the box.} \item{x}{x-coordinate of center of box.} \item{y}{y-coordinate of center of box.} \item{wd}{width of boxes in percentage of the plot width.} \item{ht}{height of boxes in percentage of the plot height.} \item{font}{Font for the text. Defaults to 2 (=bold).} \item{lwd}{Line width of the boxborders.} \item{col.txt}{Color for the text in boxes.} \item{col.border}{Color of the box border.} \item{col.bg}{Background color for the interior of the box.} \item{\dots}{Arguments to be passed on to the call of other functions.} \item{cwd}{Width of the lines in the cross.} \item{col.cross}{Color of the cross.} \item{b1}{Coordinates of the "from" box. A vector with 4 components, \code{x}, \code{y}, \code{w}, \code{h}.} \item{b2}{Coordinates of the "to" box; like \code{b1}.} \item{offset}{Logical. Should the arrow be offset a bit to the left.} \item{pos}{Numerical between 0 and 1, determines the position of the point on the arrow which is returned.} \item{obj}{A \code{\link{Lexis}} object or a transition matrix; that is a square matrix indexed by state in both dimensions, and the \eqn{(i,j)}th entry different from \code{NA} if a transition \eqn{i} to \eqn{j} can occur. If \code{show.D=TRUE}, the arrows between states are annotated by these numbers. If \code{show.Y=TRUE}, the boxes representing states are annotated by the numbers in the diagonal of \code{obj}. For \code{boxes.matrix} \code{obj} is a matrix and for \code{boxes.MS}, \code{obj} is an \code{MS.boxes} object (see below).} \item{boxpos}{If \code{TRUE} the boxes are positioned equidistantly on a circle, if \code{FALSE} (the default) you are queried to click on the screen for the positions. This argument can also be a named list with elements \code{x} and \code{y}, both numerical vectors, giving the centers of the boxes.} \item{wmult}{Multiplier for the width of the box relative to the width of the text in the box.} \item{hmult}{Multiplier for the height of the box relative to the height of the text in the box.} \item{cex}{Character expansion for text in the box.} \item{show}{Should person-years and transitions be put in the plot. Ignored if \code{obj} is not a \code{Lexis} object.} \item{show.Y}{If logical: Should person-years be put in the boxes. If numeric: Numbers to put in boxes.} \item{scale.Y}{What scale should be used for annotation of person-years.} \item{digits.Y}{How many digits after the decimal point should be used for the person-years.} \item{show.BE}{Logical. Should number of persons beginning resp. ending follow up in each state be shown?} \item{BE.sep}{Character vector of length 4, used for annotation of the number of persons beginning and ending in each state: 1st elemet precedes no. beginning, 2nd trails it, 3rd precedes the no. ending (defaults to 8 spaces), and the 4th trails the no. ending.} \item{show.D}{Should no. transitions be put alongside the arrows. Ignored if \code{obj} is not a \code{Lexis} object.} \item{scale.D}{Synonumous with \code{scale.R}, retained for compatability.} \item{digits.D}{Synonumous with \code{digits.R}, retained for compatability.} \item{show.R}{Should the transition rates be shown on the arrows?} \item{scale.R}{If this a scalar, rates instead of no. transitions are printed at the arrows, scaled by \code{scale.R}.} \item{digits.R}{How many digits after the decimal point should be used for the rates.} \item{DR.sep}{Character vector of length 2. If rates are shown, the first element is inserted before and the second after the rate.} \item{eq.wd}{Should boxes all have the same width?} \item{eq.ht}{Should boxes all have the same height?} \item{subset}{Draw only boxes and arrows for a subset of the states. Can be given either as a numerical vector or character vector state names.} \item{exclude}{Exclude states from the plot. The complementary of \code{subset}. Ignored if \code{subset} is given.} \item{col.arr}{Color of the arrows between boxes. A vector of character strings, the arrows are referred to as the row-wise sequence of non-NA elements of the transition matrix. Thus the first ones refer to the transitions out of state 1, in order of states.} \item{lwd.arr}{Line withs of the arrows.} \item{font.arr}{Font of the text annotation the arrows.} \item{pos.arr}{Numerical between 0 and 1, determines the position on the arrows where the text is written.} \item{txt.arr}{Text put on the arrows.} \item{col.txt.arr}{Colors for text on the arrows.} \item{offset.arr}{The amount offset between arrows representing two-way transitions, that is where there are arrows both ways between two boxes.} \item{sub.st}{Subset of the states to be drawn.} \item{sub.tr}{Subset of the transitions to be drawn.} \item{x1}{x-coordinate of the starting point.} \item{y1}{y-coordinate of the starting point.} \item{x2}{x-coordinate of the end point.} \item{y2}{y-coordinate of the end point.} \item{gap}{Length of the gap between the box and the ends of the arrows.} \item{fr}{Length of the arrow as the fraction of the distance between the boxes. Ignored unless given explicitly, in which case any value given for \code{gap} is ignored.} \item{angle}{What angle should the arrow-head have?} \item{length}{Length of the arrow head in inches. Defaults to 1/30 of the physical width of the plot.} } \details{ These functions are designed to facilitate the drawing of multistate models, mainly by automatic calculation of the arrows between boxes. \code{tbox} draws a box with centered text, and returns a vector of location, height and width of the box. This is used when drawing arrows between boxes. \code{dbox} draws a box with a cross, symbolizing a death state. \code{boxarr} draws an arrow between two boxes, making sure it does not intersect the boxes. Only straight lines are drawn. \code{boxes.Lexis} takes as input a Lexis object sets up an empty plot area (with axes 0 to 100 in both directions) and if \code{boxpos=FALSE} (the default) prompts you to click on the locations for the state boxes, and then draws arrows implied by the actual transitions in the \code{Lexis} object. The default is to annotate the transitions with the number of transitions. A transition matrix can also be supplied, in which case the row/column names are used as state names, diagnonal elements taken as person-years, and off-diagnonal elements as number of transitions. This also works for \code{boxes.matrix}. Optionally returns the R-code reproducing the plot in a file, which can be useful if you want to produce exactly the same plot with differing arrow colors etc. \code{boxarr} draws an arrow between two boxes, on the line connecting the two box centers. The \code{offset} argument is used to offset the arrow a bit to the left (as seen in the direction of the arrow) on order to accommodate arrows both ways between boxes. \code{boxarr} returns a named list with elements \code{x}, \code{y} and \code{d}, where the two former give the location of a point on the arrow used for printing (see argument \code{pos}) and the latter is a unit vector in the direction of the arrow, which is used by \code{boxes.Lexis} to position the annotation of arrows with the number of transitions. \code{boxes.MS} re-draws what \code{boxes.Lexis} has done based on the object of class \code{MS} produced by \code{boxes.Lexis}. The point being that the \code{MS} object is easily modifiable, and thus it is a machinery to make variations of the plot with different color annotations etc. \code{fill.arr} is just a utility drawing nicer arrows than the default \code{\link{arrows}} command, basically by using filled arrow-heads; called by \code{boxarr}. } \value{The functions \code{tbox} and \code{dbox} return the location and dimension of the boxes, \code{c(x,y,w,h)}, which are designed to be used as input to the \code{boxarr} function. The \code{boxarr} function returns the coordinates (as a named list with names \code{x} and \code{y}) of a point on the arrow, designated to be used for annotation of the arrow. The function \code{boxes.Lexis} returns an \code{MS} object, a list with five elements: 1) \code{Boxes} - a dataframe with one row per box and columns \code{xx}, \code{yy}, \code{wd}, \code{ht}, \code{font}, \code{lwd}, \code{col.txt}, \code{col.border} and \code{col.bg}, 2) an object \code{State.names} with names of states (possibly an expression, hence not possible to include as a column in \code{Boxes}), 3) a matrix \code{Tmat}, the transition matrix, 4) a data frame, \code{Arrows} with one row per transition and columns: \code{lwd.arr}, \code{col.arr}, \code{pos.arr}, \code{col.txt.arr}, \code{font.arr} and \code{offset.arr} and 5) an object \code{Arrowtext} with names of states (possibly an expression, hence not possible to include as a column in \code{Arrows}) An \code{MS} object is used as input to \code{boxes.MS}, the primary use is to modify selected entries in the \code{MS} object first, e.g. colors, or supply subsetting arguments in order to produce displays that have the same structure, but with different colors etc. } \author{Bendix Carstensen} \examples{ par( mar=c(0,0,0,0), cex=1.5 ) plot( NA, bty="n", xlim=0:1*100, ylim=0:1*100, xaxt="n", yaxt="n", xlab="", ylab="" ) bw <- tbox( "Well" , 10, 60, 22, 10, col.txt="blue" ) bo <- tbox( "other Ca", 45, 80, 22, 10, col.txt="gray" ) bc <- tbox( "Ca" , 45, 60, 22, 10, col.txt="red" ) bd <- tbox( "DM" , 45, 40, 22, 10, col.txt="blue" ) bcd <- tbox( "Ca + DM" , 80, 60, 22, 10, col.txt="gray" ) bdc <- tbox( "DM + Ca" , 80, 40, 22, 10, col.txt="red" ) boxarr( bw, bo , col=gray(0.7), lwd=3 ) # Note the argument adj= can takes values outside (0,1) text( boxarr( bw, bc , col="blue", lwd=3 ), expression( lambda[Well] ), col="blue", adj=c(1,-0.2), cex=0.8 ) boxarr( bw, bd , col=gray(0.7) , lwd=3 ) boxarr( bc, bcd, col=gray(0.7) , lwd=3 ) text( boxarr( bd, bdc, col="blue", lwd=3 ), expression( lambda[DM] ), col="blue", adj=c(1.1,-0.2), cex=0.8 ) # Set up a transition matrix allowing recovery tm <- rbind( c(NA,1,1), c(1,NA,1), c(NA,NA,NA) ) rownames(tm) <- colnames(tm) <- c("Cancer","Recurrence","Dead") tm boxes.matrix( tm, boxpos=TRUE ) # Illustrate texting of arrows boxes.Lexis( tm, boxpos=TRUE, txt.arr=c("en","to","tre","fire") ) zz <- boxes( tm, boxpos=TRUE, txt.arr=c(expression(lambda[C]), expression(mu[C]), "recovery", expression(mu[R]) ) ) # Change color of a box zz$Boxes[3,c("col.bg","col.border")] <- "green" boxes( zz ) # Set up a Lexis object data(DMlate) str(DMlate) dml <- Lexis( entry=list(Per=dodm, Age=dodm-dobth, DMdur=0 ), exit=list(Per=dox), exit.status=factor(!is.na(dodth),labels=c("DM","Dead")), data=DMlate[1:1000,] ) # Cut follow-up at Insulin dmi <- cutLexis( dml, cut=dml$doins, new.state="Ins", pre="DM" ) summary( dmi ) boxes( dmi, boxpos=TRUE ) boxes( dmi, boxpos=TRUE, show.BE=TRUE ) boxes( dmi, boxpos=TRUE, show.BE="nz" ) boxes( dmi, boxpos=TRUE, show.BE="nz", BE.sep=c("In:"," Out:","") ) # Set up a bogus recovery date just to illustrate two-way transitions dmi$dorec <- dmi$doins + runif(nrow(dmi),0.5,10) dmi$dorec[dmi$dorec>dmi$dox] <- NA dmR <- cutLexis( dmi, cut=dmi$dorec, new.state="DM", pre="Ins" ) summary( dmR ) boxes( dmR, boxpos=TRUE ) boxes( dmR, boxpos=TRUE, show.D=FALSE ) boxes( dmR, boxpos=TRUE, show.D=FALSE, show.Y=FALSE ) boxes( dmR, boxpos=TRUE, scale.R=1000 ) MSobj <- boxes( dmR, boxpos=TRUE, scale.R=1000, show.D=FALSE ) MSobj <- boxes( dmR, boxpos=TRUE, scale.R=1000, DR.sep=c(" (",")") ) class( MSobj ) boxes( MSobj ) MSobj$Boxes[1,c("col.txt","col.border")] <- "red" MSobj$Arrows[1:2,"col.arr"] <- "red" boxes( MSobj ) } \seealso{ \code{\link{tmat.Lexis}} } \keyword{survival} \keyword{hplot} \keyword{iplot} Epi/man/plotEst.Rd0000644000175100001440000001057512531361107013510 0ustar hornikusers\name{plotEst} \alias{plotEst} \alias{pointsEst} \alias{linesEst} \title{ Plot estimates with confidence limits (forest plot) } \description{ Plots parameter estimates with confidence intervals, annotated with parameter names. A dot is plotted at the estimate and a horizontal line extending from the lower to the upper limit is superimposed. } \usage{ plotEst( ests, y = dim(ests)[1]:1, txt = rownames(ests), txtpos = y, ylim = range(y)-c(0.5,0), xlab = "", xtic = nice(ests[!is.na(ests)], log = xlog), xlim = range( xtic ), xlog = FALSE, pch = 16, cex = 1, lwd = 2, col = "black", col.txt = "black", font.txt = 1, col.lines = col, col.points = col, vref = NULL, grid = FALSE, col.grid = gray(0.9), restore.par = TRUE, ... ) linesEst( ests, y = dim(ests)[1]:1, pch = 16, cex = 1, lwd = 2, col="black", col.lines=col, col.points=col, ... ) pointsEst( ests, y = dim(ests)[1]:1, pch = 16, cex = 1, lwd = 2, col="black", col.lines=col, col.points=col, ... ) } \arguments{ \item{ests}{Matrix with three columns: Estimate, lower limit, upper limit. If a model object is supplied, \code{\link{ci.lin}} is invoked for this object first.} \item{y}{Vertical position of the lines.} \item{txt}{Annotation of the estimates. Either a character vector or an expression vector.} \item{txtpos}{Vertical position of the text. Defaults to \code{y}.} \item{ylim}{Extent of the vertical axis.} \item{xlab}{Annotation of the horizontal axis.} \item{xtic}{Location of tickmarks on the x-axis.} \item{xlim}{Extent of the x-axis.} \item{xlog}{Should the x-axis be logarithmic?} \item{pch}{What symbol should be used?} \item{cex}{Expansion of the symbol.} \item{col}{Colour of the points and lines.} \item{col.txt}{Colour of the text annotating the estimates.} \item{font.txt}{Font for the text annotating the estimates.} \item{col.lines}{Colour of the lines.} \item{col.points}{Colour of the symbol.} \item{lwd}{Thickness of the lines.} \item{vref}{Where should vertical reference line(s) be drawn?} \item{grid}{If TRUE, vertical gridlines are drawn at the tickmarks. If a numerical vector is given vertical lines are drawn at \code{grid}.} \item{col.grid}{Colour of the vertical gridlines} \item{restore.par}{Should the graphics parameters be restored? If set to \code{FALSE} the coordinate system will still be available for additional plotting, and \code{par("mai")} will still have the very large value set in order to make room for the labelling of the estimates.} \item{...}{Arguments passed on to \code{ci.lin} when a model object is supplied as \code{ests}.} } \details{ \code{plotEst} makes a news plot, whereas \code{linesEst} and \code{pointsEst} (identical functions) adds to an existing plot. If a model object of class \code{"glm"}, \code{"coxph"}, \code{"clogistic"} or \code{"gnlm"} is supplied the argument \code{xlog} defaults to \code{TRUE}, and exponentiated estimates are extracted by default. } \value{ NULL } \author{ Bendix Carstensen, \email{bxc@steno.dk}, \url{http://BendixCarstensen.com}} \seealso{ ci.lin } \examples{ # Bogus data and a linear model f <- factor( sample( letters[1:5], 100, replace=TRUE ) ) x <- rnorm( 100 ) y <- 5 + 2 * as.integer( f ) + 0.8 * x + rnorm(100) * 2 m1 <- lm( y ~ f ) # Produce some confidence intervals for contrast to first level ( cf <- ci.lin( m1, subset=-1 )[,-(2:4)] ) # Plots with increasing amounts of bells and whistles par( mfcol=c(3,2), mar=c(3,3,2,1) ) plotEst( cf ) plotEst( cf, grid=TRUE, cex=2, lwd=3 ) plotEst( cf, grid=TRUE, cex=2, col.points="red", col.lines="green" ) plotEst( cf, grid=TRUE, cex=2, col.points="red", col.lines="green", xlog=TRUE, xtic=c(1:8), xlim=c(0.8,6) ) rownames( cf )[1] <- "Contrast to fa:\n fb" plotEst( cf, grid=TRUE, cex=2, col.points=rainbow(4), col.lines=rainbow(4), vref=1 ) # etxt <- expression("Plain text, qouted", "combined with maths:"*sqrt(a)*phi[c], f^d*" Hb"*A[1][c], eff^e*" kg/"*m^2) plotEst( cf, txt=etxt, grid=TRUE, cex=2, col.points=rainbow(4), col.lines =rainbow(4), vref=1 ) } \keyword{hplot} \keyword{models} Epi/man/foreign.Lexis.Rd0000644000175100001440000000610212531361107014561 0ustar hornikusers\name{foreign.Lexis} \Rdversion{1.1} \alias{msdata} \alias{msdata.Lexis} \alias{etm} \alias{etm.Lexis} \title{Create a data structures suitable for use with packages mstate, etm. } \description{ The \code{mstate} package requires input in the form of a stacked dataset with specific variable names. This is provided by \code{msdata.Lexis}. The resulting dataframe contains the same information as the result of a call to \code{\link{stack.Lexis}}. The \code{etm} package requires input (almost) in the form of a \code{Lexis} object, but with specific column names etc. This is provided by \code{etm.Lexis}. } \usage{ msdata(obj, ...) \method{msdata}{Lexis}(obj, time.scale = timeScales(obj)[1], ... ) \method{etm}{Lexis}( obj, time.scale = timeScales(obj)[1], cens.name = "cens", s = 0, t = "last", covariance = TRUE, delta.na = TRUE, ... ) } \arguments{ \item{obj}{A \code{\link{Lexis}} object.} \item{time.scale}{Name or number of timescale in the \code{Lexis} object.} \item{cens.name}{Name of the code for censoring used by \code{etm}. It is only necessary to change this if one of the states in the \code{Lexis} object has name "\code{cens}".} \item{s}{Passed on to \code{etm}.} \item{t}{Passed on to \code{etm}.} \item{covariance}{Passed on to \code{etm}.} \item{delta.na}{Passed on to \code{etm}.} \item{\dots}{Further arguments.} } \value{ \code{msdata.Lexis} returns a dataframe with the \code{Lexis} specific variables stripped, and with the following added: \code{id}, \code{Tstart}, \code{Tstop}, \code{from}, \code{to}, \code{trans}, \code{status}, which are used in the \code{mstate} package. \code{etm.Lexis} transforms the \code{Lexis} object into a dataframe suitable for analysis by the function \code{etm} from the \code{etm} package, and actually calls this function, so returns an object of class \code{etm}. } \author{ Bendix Carstensen, \email{bxc@steno.dk}, \url{http://BendixCarstensen.com} } \examples{ data(DMlate) str(DMlate) dml <- Lexis( entry = list(Per=dodm,Age=dodm-dobth,DMdur=0), exit = list(Per=dox), exit.status = factor(!is.na(dodth),labels=c("DM","Dead")), data = DMlate[1:1000,] ) dmi <- cutLexis( dml, cut=dml$doins, new.state="Ins", pre="DM" ) summary( dmi ) # Use the interface to the mstate package if( require(mstate) ) { ms.dmi <- msdata.Lexis( dmi ) # Check that all the transitions and person-years got across. with( ms.dmi, rbind( table(status,trans), tapply(Tstop-Tstart,trans,sum) ) ) } # Use the etm package directly with a Lexis object if( require(etm) ) { dmi <- subset(dmi,lex.id<1000) etm.D <- etm.Lexis( dmi, time.scale=3 ) plot( etm.D, col=rainbow(5), lwd=2, lty=1, xlab="DM duration" ) } } \seealso{ \code{\link{stack.Lexis}}, \code{\link[mstate:msprep]{msdata}}, \code{\link[etm:etm]{etm}} } \keyword{survival} Epi/man/splitLexis.Rd0000644000175100001440000000667112531361107014220 0ustar hornikusers\name{splitLexis} \alias{splitLexis} \title{Split follow-up time in a Lexis object} \description{ The \code{splitLexis} function divides each row of a \code{Lexis} object into disjoint follow-up intervals according to the supplied break points. } \usage{ splitLexis(lex, breaks, time.scale, tol=.Machine$double.eps^0.5) } \arguments{ \item{lex}{an object of class \code{Lexis}} \item{breaks}{a vector of break points} \item{time.scale}{the name or number of the time scale to be split} \item{tol}{numeric value >= 0. Intervals shorter than this value are dropped} } \value{ An object of class \code{Lexis} with multiple rows for each row of the argument \code{lex}. Each row of the new \code{Lexis} object contains the part of the follow-up interval that falls inside one of the time bands defined by the break points. The variables representing the various time scales, are appropriately updated in the new \code{Lexis} object. The entry and exit status variables are also updated according to the rule that the entry status is retained until the end of follow-up. All other variables are considered to represent variables that are constant in time, and so are replicated across all rows having the same id value. } \note{ The \code{splitLexis()} function divides follow-up time into intervals using breakpoints that are common to all rows of the \code{Lexis} object. To split a \code{Lexis} object by break points that are unique to each row, use the \code{cut.Lexis} function. } \author{Martyn Plummer} \examples{ # A small bogus cohort xcoh <- structure( list( id = c("A", "B", "C"), birth = c("14/07/1952", "01/04/1954", "10/06/1987"), entry = c("04/08/1965", "08/09/1972", "23/12/1991"), exit = c("27/06/1997", "23/05/1995", "24/07/1998"), fail = c(1, 0, 1) ), .Names = c("id", "birth", "entry", "exit", "fail"), row.names = c("1", "2", "3"), class = "data.frame" ) # Convert the character dates into numerical variables (fractional years) xcoh$bt <- cal.yr( xcoh$birth, format="\%d/\%m/\%Y" ) xcoh$en <- cal.yr( xcoh$entry, format="\%d/\%m/\%Y" ) xcoh$ex <- cal.yr( xcoh$exit , format="\%d/\%m/\%Y" ) # See how it looks xcoh # Define as Lexis object with timescales calendar time and age Lcoh <- Lexis( entry = list( per=en ), exit = list( per=ex, age=ex-bt ), exit.status = fail, data = xcoh ) # Default plot of follow-up plot( Lcoh ) # With a grid and deaths as endpoints plot( Lcoh, grid=0:10*10, col="black" ) points( Lcoh, pch=c(NA,16)[Lcoh$lex.Xst+1] ) # With a lot of bells and whistles: plot( Lcoh, grid=0:20*5, col="black", xaxs="i", yaxs="i", xlim=c(1960,2010), ylim=c(0,50), lwd=3, las=1 ) points( Lcoh, pch=c(NA,16)[Lcoh$lex.Xst+1], col="red", cex=1.5 ) # Split time along two time-axes ( x2 <- splitLexis( Lcoh, breaks = seq(1900,2000,5), time.scale="per") ) ( x2 <- splitLexis( x2, breaks = seq(0,80,5), time.scale="age" ) ) str( x2 ) # Tabulate the cases and the person-years summary( x2 ) tapply( status(x2,"exit")==1, list( timeBand(x2,"age","left"), timeBand(x2,"per","left") ), sum ) tapply( dur(x2), list( timeBand(x2,"age","left"), timeBand(x2,"per","left") ), sum ) } \seealso{\code{\link{timeBand}}, \code{\link{cutLexis}}, \code{\link{summary.Lexis}}} \keyword{manip} Epi/man/cal.yr.Rd0000644000175100001440000000555112531361107013244 0ustar hornikusers\name{cal.yr} \alias{cal.yr} \alias{as.Date.cal.yr} \title{ Functions to convert character, factor and various date objects into a number, and vice versa. } \description{ Dates are converted to a numerical value, giving the calendar year as a fractional number. 1 January 1970 is converted to 1970.0, and other dates are converted by assuming that years are all 365.25 days long, so inaccuracies may arise, for example, 1 Jan 2000 is converted to 1999.999. Differences between converted values will be 1/365.25 of the difference between corresponding \code{\link{Date}} objects. } \usage{ cal.yr( x, format="\%Y-\%m-\%d", wh=NULL ) \method{as.Date}{cal.yr}( x, ... ) } \arguments{ \item{x}{A factor or character vector, representing a date in format \code{format}, or an object of class \code{\link{Date}}, \code{\link{POSIXlt}}, \code{\link{POSIXct}}, \code{\link{date}}, \code{dates} or \code{chron} (the latter two requires the \code{chron} package). If \code{x} is a data frame, all variables in the data-frame which are of one the classes mentioned are converted to class \code{cal.yr}. See arguemt \code{wh}, though.} \item{format}{Format of the date values if \code{x} is factor or character. If this argument is supplied and \code{x} is a datafame, all character variables are converted to class \code{cal.yr}. Factors in the dataframe will be ignored.} \item{wh}{Indices of the variables to convert if \code{x} is a data frame. Can be either a numerical or character vector.} \item{...}{Arguments passed on from other methods.} } \value{ \code{cal.yr} returns a numerical vector of the same length as \code{x}, of class \code{c("cal.yr","numeric")}. If \code{x} is a data frame a dataframe with some of the columns converted to class \code{"cal.yr"} is returned. \code{as.Date.cal.yr} returns a \code{\link{Date}} object. } \author{ Bendix Carstensen, Steno Diabetes Center \& Dept. of Biostatistics, University of Copenhagen, \email{bxc@steno.dk}, \url{http://BendixCarstensen.com} } \seealso{ \code{\link{DateTimeClasses}}, \code{\link{Date}} } \examples{ # Character vector of dates: birth <- c("14/07/1852","01/04/1954","10/06/1987","16/05/1990", "12/11/1980","01/01/1997","01/01/1998","01/01/1999") # Proper conversion to class "Date": birth.dat <- as.Date( birth, format="\%d/\%m/\%Y" ) # Converson of character to class "cal.yr" bt.yr <- cal.yr( birth, format="\%d/\%m/\%Y" ) # Back to class "Date": bt.dat <- as.Date( bt.yr ) # Numerical calculation of days since 1.1.1970: days <- Days <- (bt.yr-1970)*365.25 # Blunt assignment of class: class( Days ) <- "Date" # Then data.frame() to get readable output of results: data.frame( birth, birth.dat, bt.yr, bt.dat, days, Days, round(Days) ) } \keyword{manip} \keyword{chron} Epi/man/nickel.Rd0000644000175100001440000000233112531361107013312 0ustar hornikusers\name{nickel} \alias{nickel} \docType{data} \title{A Cohort of Nickel Smelters in South Wales} \description{ The \code{nickel} data frame has 679 rows and 7 columns. The data concern a cohort of nickel smelting workers in South Wales and are taken from Breslow and Day, Volume 2. For comparison purposes, England and Wales mortality rates (per 1,000,000 per annum) from lung cancer (ICDs 162 and 163), nasal cancer (ICD 160), and all causes, by age group and calendar period, are supplied in the dataset \code{\link{ewrates}}. } \format{ This data frame contains the following columns: \tabular{rl}{ \code{id}: \tab Subject identifier (numeric) \cr \code{icd}: \tab ICD cause of death if dead, 0 otherwise (numeric) \cr \code{exposure}: \tab Exposure index for workplace (numeric) \cr \code{dob}: \tab Date of birth (numeric) \cr \code{age1st}: \tab Age at first exposure (numeric) \cr \code{agein}: \tab Age at start of follow-up (numeric) \cr \code{ageout}: \tab Age at end of follow-up (numeric) \cr } } \source{ Breslow NE, and Day N, Statistical Methods in Cancer Research. Volume II: The Design and Analysis of Cohort Studies. IARC Scientific Publications, IARC:Lyon, 1987. } \examples{ data(nickel) str(nickel) } \keyword{datasets} Epi/man/time.band.Rd0000644000175100001440000000450112531361107013707 0ustar hornikusers\name{timeBand} \alias{timeBand} \alias{breaks} \title{Extract time band data from a split Lexis object} \description{ The break points of a \code{Lexis} object (created by a call to \code{splitLexis}) divide the follow-up intervals into time bands along a given time scale. The \code{breaks} function returns the break points, for a given time scale, and the \code{timeBand} classifies each row (=follow-up interval) into one of the time bands. } \usage{ timeBand(lex, time.scale, type="integer") breaks(lex, time.scale) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{lex}{an object of class \code{Lexis}} \item{time.scale}{a character or integer vector of length 1 identifying the time scale of interest} \item{type}{a string that determines how the time bands are labelled. See Details below} } \details{ Time bands may be labelled in various ways according to the \code{type} argument. The permitted values of the \code{type} argument, and the corresponding return values are: \describe{ \item{"integer"}{a numeric vector with integer codes starting from 0.} \item{"factor"}{a factor (unordered) with labels "(left,right]"} \item{"left"}{the left-hand limit of the time band} \item{"middle"}{the midpoint of the time band} \item{"right"}{the right-hand limit of the time band} } } \value{ The \code{breaks} function returns a vector of break points for the \code{Lexis} object, or NULL if no break points have been defined by a call to \code{splitLexis}. The \code{timeBand} function returns a numeric vector or factor, depending on the value of the \code{type} argument. } \author{Martyn Plummer} \note{ A newly created \code{Lexis} object has no break points defined. In this case, \code{breaks} will return NULL, and \code{timeBand} will a vector of zeros. } \examples{ data(diet) diet <- cal.yr(diet) diet.lex <- Lexis(entry=list(period=doe), exit=list(period=dox, age=dox-dob), exit.status=chd, data=diet) diet.split <- splitLexis(diet.lex, breaks=seq(40,70,5), "age" ) age.left <- timeBand(diet.split, "age", "left") table(age.left) age.fact <- timeBand(diet.split, "age", "factor") table(age.fact) age.mid <- timeBand(diet.split, "age", "mid") table(age.mid) } \seealso{\code{\link{Lexis}}} \keyword{attribute} Epi/man/Termplot.Rd0000644000175100001440000000643612562641471013676 0ustar hornikusers\name{Termplot} \alias{Termplot} \title{ A wrapper for \code{termplot} that optionally (but by default) exponentiates terms, and plot them on a common log-scale. Also scales x-axes to the same physical scale. } \description{ The function uses \code{\link{termplot}} to extract terms from a model with, say, spline, terms, including the standard errors, computes confidence intervals and transform these to the rate / rate-ratio scale. Thus the default use is for models on the log-scale such as Poisson-regression models. The function produces a plot with panels side-by-side, one panel per term, and returns the } \usage{ Termplot( obj, plot = TRUE, xlab = NULL, ylab = NULL, xeq = TRUE, yshr = 1, alpha = 0.05, terms = NULL, max.pt = NULL ) } \arguments{ \item{obj}{An object with a \code{terms}-method --- for details the the documentation for \code{\link{termplot}}. } \item{plot}{Should a plot be produced?} \item{xlab}{Labels for the \code{x}-axes. Defaults to the names of the terms.} \item{ylab}{Labels for the \code{x}-axes. Defaults to blank.} \item{xeq}{Should the units all all plots have the same physical scale for the \code{x}-axes).} \item{yshr}{Shrinking of \code{y}-axis. By default, the \code{y}-axes have an extent that accommodates the entire range of confidence intervals. This is a shrinking parameter for the \code{y}-axes, setting it to less than 1 will lose a bit of the confidence limits on some of the panels.} \item{alpha}{1 minus the confidence level for computing confidence intervals} \item{terms}{Which terms should be reported. Passed on to \code{\link{termplot}} and eventually \code{\link{predict}}.} \item{max.pt}{The maximal number of points in which to report the terms. If \code{NULL} all unique points from the analysis dataset are reported for each term (this is a feature of \code{\link{termplot}}).} } \value{ A list with one component per term in the model object \code{obj}, each component is a 4-column matrix with $x$ as the first column, and 3 columns with estimae and lower and upper confidence limit. } \author{ Bendix Cartensen } \seealso{ \code{\link{Ns}}, \code{termplot} } \examples{ # Get the diabetes data and set up as Lexis object data(DMlate) DMlate <- DMlate[sample(1:nrow(DMlate),500),] dml <- Lexis( entry = list(Per=dodm, Age=dodm-dobth, DMdur=0 ), exit = list(Per=dox), exit.status = factor(!is.na(dodth),labels=c("DM","Dead")), data = DMlate ) # Split in 1-year age intervals dms <- splitLexis( dml, time.scale="Age", breaks=0:100 ) # Model with 6 knots for both age and period n.kn <- 6 # Model age-specific rates with period referenced to 2004 ( a.kn <- with( subset(dms,lex.Xst=="Dead"), quantile( Age+lex.dur, probs=(1:n.kn-0.5)/n.kn ) ) ) ( p.kn <- with( subset(dms,lex.Xst=="Dead"), quantile( Per+lex.dur, probs=(1:n.kn-0.5)/n.kn ) ) ) m2 <- glm( lex.Xst=="Dead" ~ -1 + Ns( Age, kn=a.kn, intercept=TRUE ) + Ns( Per, kn=p.kn, ref=2004 ), offset = log( lex.dur ), family=poisson, data=dms ) # Finally we can plot the two effects: Termplot( m2, yshr=0.9 ) } \keyword{hplot} Epi/man/diet.Rd0000644000175100001440000000474512531361107013005 0ustar hornikusers\name{diet} \alias{diet} \docType{data} \title{Diet and heart data} \description{ The \code{diet} data frame has 337 rows and 14 columns. The data concern a subsample of subjects drawn from larger cohort studies of the incidence of coronary heart disease (CHD). These subjects had all completed a 7-day weighed dietary survey while taking part in validation studies of dietary questionnaire methods. Upon the closure of the MRC Social Medicine Unit, from where these studies were directed, it was found that 46 CHD events had occurred in this group, thus allowing a serendipitous study of the relationship between diet and the incidence of CHD. } \format{ This data frame contains the following columns: \tabular{rl}{ \code{id}: \tab subject identifier, a numeric vector. \cr \code{doe}: \tab date of entry into follow-up study, a \code{\link{Date}} variable. \cr \code{dox}: \tab date of exit from the follow-up study, a \code{\link{Date}} variable. \cr \code{dob}: \tab date of birth, a \code{\link{Date}} variable. \cr \code{y}: \tab - number of years at risk, a numeric vector. \cr \code{fail}: \tab status on exit, a numeric vector (codes 1, 3, 11, and 13 represent CHD events) \cr \code{job}: \tab occupation, a factor with levels \code{Driver} \code{Conductor} \code{Bank worker} \cr \code{month}: \tab month of dietary survey, a numeric vector \cr \code{energy}: \tab total energy intake (KCal per day/100), a numeric vector \cr \code{height}: \tab (cm), a numeric vector \cr \code{weight}: \tab (kg), a numeric vector \cr \code{fat}: \tab fat intake (g/day), a numeric vector \cr \code{fibre}: \tab dietary fibre intake (g/day), a numeric vector \cr \code{energy.grp}: \tab high daily energy intake, a factor with levels \code{<=2750 KCal} \code{>2750 KCal} \cr \code{chd}: \tab CHD event, a numeric vector (1=CHD event, 0=no event) \cr } } \source{ The data are described and used extensively by Clayton and Hills, Statistical Models in Epidemiology, Oxford University Press, Oxford:1993. They were rescued from destruction by David Clayton and reentered from paper printouts. } \examples{ data(diet) # Illustrate the follow-up in a Lexis diagram Lexis.diagram( age=c(30,75), date=c(1965,1990), entry.date=cal.yr(doe), exit.date=cal.yr(dox), birth.date=cal.yr(dob), fail=(fail>0), pch.fail=c(NA,16), col.fail=c(NA,"red"), cex.fail=1.0, data=diet ) } \keyword{datasets} Epi/man/ci.pd.Rd0000644000175100001440000000476312531361107013055 0ustar hornikusers\name{ci.pd} \alias{ci.pd} \title{ Compute confidence limits for a difference of two independent proportions. } \description{ The usual formula for the c.i. of at difference of proportions is inaccurate. Newcombe has compared 11 methods and method 10 in his paper looks like a winner. It is implemented here. } \usage{ ci.pd(aa, bb=NULL, cc=NULL, dd=NULL, method = "Nc", alpha = 0.05, conf.level=0.95, digits = 3, print = TRUE, detail.labs = FALSE ) } \arguments{ \item{aa}{Numeric vector of successes in sample 1. Can also be a matrix or array (see details).} \item{bb}{Successes in sample 2.} \item{cc}{Failures in sample 1.} \item{dd}{Failures in sample 2.} \item{method}{Method to use for calculation of confidence interval, see "Details".} \item{alpha}{Significance level} \item{conf.level}{Confidence level} \item{print}{Should an account of the two by two table be printed.} \item{digits}{How many digits should the result be rounded to if printed.} \item{detail.labs}{Should the computing of probability differences be reported in the labels.} } \details{ Implements method 10 from Newcombe(1998) (method="Nc") or from Agresti & Caffo(2000) (method="AC"). \code{aa}, \code{bb}, \code{cc} and \code{dd} can be vectors. If \code{aa} is a matrix, the elements \code{[1:2,1:2]} are used, with successes \code{aa[,1:2]}. If \code{aa} is a three-way table or array, the elements \code{aa[1:2,1:2,]} are used. } \value{ A matrix with three columns: probability difference, lower and upper limit. The number of rows equals the length of the vectors \code{aa}, \code{bb}, \code{cc} and \code{dd} or, if \code{aa} is a 3-way matrix, \code{dim(aa)[3]}. } \references{ RG Newcombe: Interval estimation for the difference between independent proportions. Comparison of eleven methods. Statistics in Medicine, 17, pp. 873-890, 1998. A Agresti & B Caffo: Simple and effective confidence intervals for proportions and differences of proportions result from adding two successes and two failures. The American Statistician, 54(4), pp. 280-288, 2000. } \author{ Bendix Carstensen, Esa Laara. \url{http://BendixCarstensen.com} } \seealso{ \code{\link{twoby2}}, \code{\link{binom.test}} } \examples{ ( a <- matrix( sample( 10:40, 4 ), 2, 2 ) ) ci.pd( a ) twoby2( t(a) ) prop.test( t(a) ) ( A <- array( sample( 10:40, 20 ), dim=c(2,2,5) ) ) ci.pd( A ) ci.pd( A, detail.labs=TRUE, digits=3 ) } \keyword{distribution} \keyword{htest} Epi/man/subset.Lexis.Rd0000644000175100001440000000173112531361107014440 0ustar hornikusers\name{subset.Lexis} \alias{subset.Lexis} \alias{subset.stacked.Lexis} \title{Subsetting Lexis (and stacked.Lexis) objects} \description{ Return subsets of Lexis objects which meet conditions } \usage{ \method{subset}{Lexis}(x, ...) \method{subset}{stacked.Lexis}(x, ...) } \arguments{ \item{x}{an object of class \code{Lexis}} \item{\dots}{additional arguments to be passed to \code{subset.data.frame}. This will normally be some logical expression selecting a subset of the rows. For details see \code{\link{subset.data.frame}}.} } \details{ The subset method for \code{Lexis} objects works exactly as the method for data frames. The method for \code{stacked.Lexis} objects also shrinks the set of levels for \code{lex.Cst} and \code{lex.Xst} to those actually occurring in the data frame. } \value{ A \code{Lexis} object with selected rows and columns. } \author{Martyn Plummer} \seealso{\code{\link{Lexis}}, \code{\link{merge.Lexis}}} \keyword{manip} Epi/man/ewrates.Rd0000644000175100001440000000172212531361107013522 0ustar hornikusers\name{ewrates} \alias{ewrates} \docType{data} \title{Rates of lung and nasal cancer mortality, and total mortality.} \description{ England and Wales mortality rates from lung cancer, nasal cancer, and all causes 1936 - 1980. The 1936 rates are repeated as 1931 rates in order to accomodate follow up for the \code{\link{nickel}} study. } \usage{data(ewrates)} \format{ A data frame with 150 observations on the following 5 variables: \tabular{rl}{ \code{id}: \tab Subject identifier (numeric) \cr \code{year} \tab Calendar period, 1931: 1931--35, 1936: 1936--40, \ldots \cr \code{age} \tab Age class: 10: 10--14, 15:15--19, \ldots \cr \code{lung} \tab Lung cancer mortality rate per 1,000,000 py. \cr \code{nasal} \tab Nasal cancer mortality rate per 1,000,000 py. \cr \code{other} \tab All cause mortality rate per 1,000,000 py. } } \source{ From Breslow and Day, Vol II, Appendix IX. } \examples{ data(ewrates) str(ewrates) } \keyword{datasets} Epi/man/start.Lexis.Rd0000644000175100001440000000257312531361107014275 0ustar hornikusers\name{start.Lexis} \alias{entry} \alias{exit} \alias{status} \alias{dur} \title{Time series methods for Lexis objects} \description{ Extract the entry time, exit time, status or duration of follow-up from a \code{Lexis} object. } \usage{ entry(x, time.scale = NULL, by.id=FALSE) exit(x, time.scale = NULL, by.id=FALSE) status(x, at="exit" , by.id=FALSE) dur(x, by.id=FALSE) } \arguments{ \item{x}{an object of class \code{Lexis}.} \item{time.scale}{a string or integer indicating the time scale. If omitted, all times scales are used.} \item{by.id}{Logical, if \code{TRUE}, only one record per unique value of \code{lex.id} is returned; either the first, the last or for \code{dur}, the sum of \code{lex.dur}. If \code{TRUE}, the returned object have the \code{lex.id} as (row)nmes attribute.} \item{at}{string indicating the time point(s) at which status is to be measured.} } \value{ The \code{entry} and \code{exit} functions return a vector of entry times and exit times, respectively, on the requested time scale. If multiple time scales are requested, then a matrix is returned. The \code{status} function returns a vector giving the status at entry or exit and \code{dur} returns a vector with the lengths of the follow-up intervals. } \author{Martyn Plummer} \seealso{\code{\link{Lexis}}} \keyword{survival} \keyword{ts} Epi/man/merge.Lexis.Rd0000644000175100001440000000244612531361107014236 0ustar hornikusers\name{merge.Lexis} \alias{merge.Lexis} \title{Merge a Lexis object with a data frame} \description{ Merge additional variables from a data frame into a Lexis object. } \usage{ \method{merge}{Lexis}(x, y, id, by, ...) } \arguments{ \item{x}{an object of class \code{Lexis}} \item{y}{a data frame} \item{id}{the name of the variable in \code{y} to use for matching against the variable \code{lex.id} in \code{x}. } \item{by}{if matching is not done by id, a vector of variable names common to both \code{x} and \code{y}} \item{...}{optional arguments to be passed to \code{merge.data.frame}} } \details{ A \code{Lexis} object can be considered as an augmented data frame in which some variables are time-dependent variables representing follow-up. The \code{Lexis} function produces a minimal object containing only these time-dependent variables. Additional variables may be added to a \code{Lexis} object using the \code{merge} method. } \value{ A \code{Lexis} object with additional columns taken from the merged data frame. } \author{Martyn Plummer} \note{ The variable given as the \code{by.y} argument must not contain any duplicate values in the data frame \code{y}. } \seealso{\code{\link{merge.data.frame}}, \code{\link{subset.Lexis}}} \keyword{array} \keyword{manip} Epi/man/simLexis.Rd0000644000175100001440000003224112556664146013665 0ustar hornikusers\name{simLexis} \alias{simLexis} \alias{nState} \alias{pState} \alias{plot.pState} \alias{lines.pState} \title{Simulate a Lexis object representing follow-up in a multistate model.} \description{Based on a (pre-)\code{Lexis} object representing persons at given states and times, and full specification of transition intensities between states in the form of models for the transition rates, this function simulates transition times and -types for persons and returns a \code{Lexis} object representing the simulated cohort. The simulation scheme accommodates multiple timescales, including time since entry into an intermediate state, and accepts fitted Poisson models, Cox-models or just a function as specification of rates.} \usage{ simLexis( Tr, init, N = 1, lex.id, t.range = 20, n.int = 101, time.pts = seq(0,t.range,length.out=n.int) ) nState( obj, at, from, time.scale = 1 ) pState( nSt, perm = 1:ncol(nSt) ) \method{plot}{pState}( x, col = rainbow(ncol(x)), border = "transparent", xlab = "Time", ylim = 0:1, ylab = "Probability", ... ) \method{lines}{pState}( x, col = rainbow(ncol(x)), border = "transparent", ... ) } \arguments{ \item{Tr}{A named list of named lists. The names of the list are names of the transient states in the model. Each list element is again a named list. The names of the elements of this inner list are the names of the states reachable from the state with name equal to the list. Elements of the intter lists represent transitions. See details.} \item{init}{A (pre-)\code{\link{Lexis}} object representing the initial state of the persons whose trajectories through the multiple states we want to simulate. Must have an attribute "time.since" --- see details. Duplicate values of \code{lex.id} are not sensible and not accepted.} \item{N}{Numeric. How many persons should be simulated. \code{N} persons with covariate configuration of each row in \code{init} will be simulated. Either a scalar or a vector of length \code{nrow(init)}.} \item{lex.id}{Vector of ids of the simulated persons. Useful when simulating in chunks.} \item{t.range}{Numerical scalar. The range of time over which to compute the cumulative rates when simulating. Simulted times beyond this will result in an obervation censored at \code{t.range} after entry.} \item{n.int}{Number of intervals to use when computing (cumulative) rates.} \item{time.pts}{Numerical vector of times since start. Cumulative rates for transitions are computed at these times after stater and entry state. Simulation is only done till time \code{max(time.pts)} after start, where persons are censored. Must start with 0.} \item{obj}{A \code{Lexis} object.} \item{from}{The point on the time scale \code{time.scale} from which we start counting.} \item{time.scale}{The timescale to which \code{from} refer.} \item{at}{Time points (after \code{from}) where the number of persons in each state is to be computed.} \item{nSt}{A table obtained by \code{nState}.} \item{perm}{A permutation of columns used before cumulating row-wise and taking percentages.} \item{x}{An object of class \code{pState}, e.g. created by \code{pState}.} \item{col}{Colors for filling the areas between curves.} \item{border}{Colors for outline of the areas between curves.} \item{xlab}{Label on x-axis} \item{ylim}{Limits on y-axis} \item{ylab}{Label on y-axis} \item{...}{Further arguments passed on to \code{plot}.} } \details{The simulation command \code{simLexis} is not defined as a method for \code{Lexis} objects, because the input is not a \code{Lexis} object, the \code{Lexis}-like object is merely representing a prevalent population and a specification of which variables that are timescales. The variables \code{lex.dur} and \code{lex.Xst} are ignored (and overwritten) if present. The core input is the list \code{Tr} giving the transitions. The components of \code{Tr} represents the transition intensities between states. The transition from state \code{A} to \code{B}, say, is assumed stored in \code{Tr$A$B}. Thus names of the elements of \code{Tr} are names of transient states, and the names of the elements of each these are the names of states reachable from the corresponding transient state. The transition intensities are assumed modelled by either a glm with Poisson family or a Cox-model. In both cases the timescale(s) in the model must be using the names fo the timescales in a Lexis object representng the follow-up in a cohort, and the risk time must be taken from the variable \code{lex.dur} --- see the example. Alternatively, an element in \code{Tr} could be a function that from a data frame produces transition rates, or specifically cumulative transition rates over intervals of length \code{lex.dur}. The pre-\code{Lexis} object \code{init} must contain values of all variables used in any of the objects in \code{Tr}, as well as all timescales - even those not used in the models. Moreover, the attributes \code{time.scales} and \code{time.since} must be present. The attribute \code{time.since} is a character vector of the same length as \code{time.scales} and an element has value \code{"A"} if the corresponding time scale is defined as "time since entry into state \code{A}", otherwise the value is \code{""}. If not present it will be set to a vector of \code{""}s, which is only OK if no time scales are defined as time since entry to a state. Note that the variables pre-\code{Lexis} object \code{init} must have the same mode and class as in the dataset used for fitting the models --- hence the indexing of rows by brackets in the assignment of values used in the example below - this way the variables have their attributes preserved; using \code{init[,"var"] <-} or \code{init$var <-} replaces the variable, whereas \code{init[1:4,"var"] <-} or \code{init$var[1:4] <-} replaces values only and prevents you from entering non-existing factor levels etc. The function \code{\link{Lexis}} automatically generates an attribute \code{time.since}, and \code{\link{cutLexis}} updates it when new time scales are defined. Hence, the simplest way of defining a initial pre-\code{Lexis} object representing a current state of a (set of) persons to be followed through a multistate model is to take \code{NULL} rows of an existing Lexis object (normally the one used for estimation), and so ensuring that all relevant attributes and state levels are properly defined. See the example code. The prevalence function \code{nState} computes the distribution of individuals in different states at prespecified times. Only sensible for a simulated \code{Lexis} object. The function \code{pState} takes a matrix as output by \code{nState} and computes the row-wise cumulative probabilities across states, and leaves an object of class \code{pState}, suitable for plotting. } \value{\code{simLexis} returns a \code{\link{Lexis}} object representing the experience of a population starting as \code{init} followed through the states according to the transitions in \code{Tr}. The function \code{nState} returns a table of persons classified by states at each of the times in \code{at}. Note that this function can easily produce meaningless results, for example if applied to a \code{Lexis} object not created by simulation. If you apply it to a \code{Lexis} object generated by \code{simLexis}, you must make sure that you start (\code{from}) the point where you started the simulation on the correct timescale, and you will get funny results if you try to tabulate beyond the censoring time for the simulation. The resulting object has class \code{"table"}. The result from using \code{pState} on the result from \code{nState} has class \code{c("pState","matrix")}. } \author{Bendix Carstensen, \url{BendixCarstensen.com}.} \seealso{ \code{\link{Lexis}}, \code{\link{cutLexis}}, \code{\link{splitLexis}} } \examples{ data(DMlate) dml <- Lexis( entry = list(Per=dodm, Age=dodm-dobth, DMdur=0 ), exit = list(Per=dox), exit.status = factor(!is.na(dodth),labels=c("DM","Dead")), data = DMlate[runif(nrow(DMlate))<0.1,] ) # Split follow-up at insulin, introduce a new timescale, # and split non-precursor states dmi <- cutLexis( dml, cut = dml$doins, pre = "DM", new.state = "Ins", new.scale = "t.Ins", split.states = TRUE ) # Split the follow in 1-year intervals for modelling Si <- splitLexis( dmi, 0:30/2, "DMdur" ) # Define knots nk <- 4 ( ai.kn <- with( subset(Si,lex.Xst=="Ins"), quantile( Age+lex.dur, probs=(1:nk-0.5)/nk ) ) ) ( ad.kn <- with( subset(Si,lex.Xst=="Dead"), quantile( Age+lex.dur, probs=(1:nk-0.5)/nk ) ) ) ( di.kn <- with( subset(Si,lex.Xst=="Ins"), quantile( DMdur+lex.dur, probs=(1:nk-0.5)/nk ) ) ) ( dd.kn <- with( subset(Si,lex.Xst=="Dead"), quantile( DMdur+lex.dur, probs=(1:nk-0.5)/nk ) ) ) ( td.kn <- with( subset(Si,lex.Xst=="Dead(Ins)"), quantile( t.Ins+lex.dur, probs=(1:nk-0.5)/nk ) ) ) # Fit Poisson models to transition rates library( splines ) DM.Ins <- glm( (lex.Xst=="Ins") ~ Ns( Age , knots=ai.kn ) + Ns( DMdur, knots=di.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="DM") ) DM.Dead <- glm( (lex.Xst=="Dead") ~ Ns( Age , knots=ad.kn ) + Ns( DMdur, knots=dd.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="DM") ) Ins.Dead <- glm( (lex.Xst=="Dead(Ins)") ~ Ns( Age , knots=ad.kn ) + Ns( DMdur, knots=dd.kn ) + Ns( t.Ins, knots=td.kn ) + I(Per-2000) + sex, family=poisson, offset=log(lex.dur), data = subset(Si,lex.Cst=="Ins") ) # Stuff the models into an object representing the transitions Tr <- list( "DM" = list( "Ins" = DM.Ins, "Dead" = DM.Dead ), "Ins" = list( "Dead(Ins)" = Ins.Dead ) ) lapply( Tr, names ) # Define an initial object - note the combination of "select=" and NULL # which ensures that the relevant attributes from the Lexis object 'Si' # are carried over to 'ini' ( Si[NULL,1:9] will lose essential # attributes ): ini <- subset(Si,select=1:9)[NULL,] ini[1:2,"lex.Cst"] <- "DM" ini[1:2,"Per"] <- 1995 ini[1:2,"Age"] <- 60 ini[1:2,"DMdur"] <- 5 ini[1:2,"sex"] <- c("M","F") str(ini) # Simulate 200 of each sex using the estimated models in Tr simL <- simLexis( Tr, ini, time.pts=seq(0,11,0.5), N=200 ) summary( simL ) # Find the number of persons in each state at a set of times. # Note that the times are shirter than the time-span simulated. nSt <- nState( subset(simL,sex=="M"), at=seq(0,10,0.1), from=1995, time.scale="Per" ) nSt # Show the cumulative prevalences in a different order than that of the # state-level ordering and plot them using all defaults pp <- pState( nSt, perm=c(1,2,4,3) ) head( pp ) plot( pp ) # A more useful set-up of the graph clr <- c("orange2","forestgreen") par( las=1 ) plot( pp, col=clr[c(2,1,1,2)] ) lines( as.numeric(rownames(pp)), pp[,2], lwd=2 ) mtext( "60 year old male, diagnosed 1995", side=3, line=2.5, adj=0 ) mtext( "Survival curve", side=3, line=1.5, adj=0 ) mtext( "DM, no insulin DM, Insulin", side=3, line=0.5, adj=0, col=clr[1] ) mtext( "DM, no insulin", side=3, line=0.5, adj=0, col=clr[2] ) axis( side=4 ) # Using a Cox-model for the mortality rates assuming the two mortality # rates to be proportional: # When we fit a Cox-model, lex.dur must be used in the Surv() function, # and the I() constrction must be used when specifying intermediate # states as covariates, since factors with levels not present in the # data will create NAs in the parameter vector returned by coxph, which # in return will crash the simulation machinery. library( survival ) Cox.Dead <- coxph( Surv( DMdur, DMdur+lex.dur, lex.Xst \%in\% c("Dead(Ins)","Dead")) ~ Ns( Age-DMdur, knots=ad.kn ) + I(lex.Cst=="Ins") + I(Per-2000) + sex, data = Si ) Cr <- list( "DM" = list( "Ins" = DM.Ins, "Dead" = Cox.Dead ), "Ins" = list( "Dead(Ins)" = Cox.Dead ) ) simL <- simLexis( Cr, ini, time.pts=seq(0,11,0.2), N=200 ) summary( simL ) nSt <- nState( subset(simL,sex=="M"), at=seq(0,10,0.2), from=1995, time.scale="Per" ) pp <- pState( nSt, perm=c(1,2,4,3) ) plot( pp ) } \keyword{survival} Epi/man/stattable.funs.Rd0000644000175100001440000000332412531361107015005 0ustar hornikusers\name{stattable.funs} \alias{count} \alias{percent} \alias{ratio} \title{Special functions for use in stat.table} \description{ These functions may be used as \code{contents} arguments to the function \code{stat.table}. They are defined internally in \code{stat.table} and have no independent existence. } \usage{ count(id) ratio(d,y,scale=1, na.rm=TRUE) percent(...) } \arguments{ \item{id}{numeric vector in which identical values identify the same individual.} \item{d, y}{numeric vectors of equal length (\code{d} for Deaths, \code{y} for person-Years)} \item{scale}{a scalar giving a value by which the ratio should be multiplied} \item{na.rm}{a logical value indicating whether \code{NA} values should be stripped before computation proceeds.} \item{...}{a list of variables taken from the \code{index} argument to \code{\link{stat.table}}} } \value{ When used as a \code{contents} argument to \code{stat.table}, these functions create the following tables: \item{\code{count}}{If given without argument (\code{count()}) it returns a contingency table of counts. If given an \code{id} argument it returns a table of the number of different values of \code{id} in each cell, i.e. how many persons contribute in each cell.} \item{\code{ratio}}{returns a table of values \code{scale * sum(d)/sum(y)}} \item{\code{percent}}{returns a table of percentages of the classifying variables. Variables that are in the \code{index} argument to \code{stat.table} but not in the call to \code{percent} are used to define strata, within which the percentages add up to 100.} } \author{Martyn Plummer} \seealso{\code{\link{stat.table}}} \keyword{iteration} \keyword{category} Epi/man/thoro.Rd0000644000175100001440000000401112531361107013175 0ustar hornikusers\name{thoro} \alias{thoro} \docType{data} \title{Thorotrast Study} \description{ The \code{thoro} data frame has 2470 rows and 14 columns. Each row represents one patient that have had cerebral angiography (X-ray of the brain) with an injected contrast medium, either Thorotrast or another one (the controls). } \format{ This data frame contains the following columns: \describe{ \item{\code{id}}{Identification of person.} \item{\code{sex}}{Sex, 1: male / 2: female.} \item{\code{birthdat}}{Date of birth, \code{Date} variable.} \item{\code{contrast}}{Group, 1: Thorotrast / 2: Control.} \item{\code{injecdat}}{Date of contrast injection, \code{Date} variable.} \item{\code{volume}}{Injected volume of Thorotrast in ml. Control patients have a 0 in this variable.} \item{\code{exitdat}}{Date of exit from the study, \code{Date} variable.} \item{\code{exitstat}}{Status at exit, 1: dead / 2: alive, censored at closing of study, 20 February 1992 / 3: censored alive at some earlier date.} \item{\code{cause}}{Cause of death. See causes in the helpfile for \code{\link{gmortDK}}.} \item{\code{liverdat}}{Date of liver cancer diagnosis, \code{Date} variable.} \item{\code{liver}}{Indicator of liver cancer diagnosis. Not all livercancers are histologically verified, hence \code{liver >= hepcc + chola + hmang}} \item{\code{hepcc}}{Hepatocellular carcinoma at \code{liverdat}.} \item{\code{chola}}{Cholangiocellular carcinoma at \code{liverdat}.} \item{\code{hmang}}{Haemangisarcoma carcinoma at \code{liverdat}.} } } \source{ M Andersson, M Vyberg, J Visfeldt, B Carstensen & HH Storm: Primary liver tumours among Danish patients exposed to Thorotrast. Radiation Research, 137, pp. 262--273, 1994. M Andersson, B Carstensen HH Storm: Mortality and cancer incidence after cerebral angiography. Radiation Research, 142, pp. 305--320, 1995. } \examples{ data(thoro) str(thoro) } \seealso{\code{\link{mortDK}}, \code{\link{gmortDK}}} \keyword{datasets} Epi/man/lungDK.Rd0000644000175100001440000000361412531361107013236 0ustar hornikusers\name{lungDK} \alias{lungDK} \docType{data} \title{Male lung cancer incidence in Denmark} \description{ Male lung cancer cases and population riks time in Denmark, for the period 1943--1992 in ages 40--89. } \usage{data(lungDK)} \format{ A data frame with 220 observations on the following 9 variables. \tabular{rl}{ \code{A5}: \tab Left end point of the age interval, a numeric vector. \cr \code{P5}: \tab Left enpoint of the period interval, a numeric vector. \cr \code{C5}: \tab Left enpoint of the birth cohort interval, a numeric vector. \cr \code{up}: \tab Indicator of upper trianges of each age by period rectangle in the Lexis diagram. (\code{up=(P5-A5-C5)/5}). \cr \code{Ax}: \tab The mean age of diagnois (at risk) in the triangle. \cr \code{Px}: \tab The mean date of diagnosis (at risk) in the triangle. \cr \code{Cx}: \tab The mean date of birth in the triangle, a numeric vector. \cr \code{D}: \tab Number of diagnosed cases of male lung cancer. \cr \code{Y}: \tab Risk time in the male population, person-years. \cr } } \details{ Cases and person-years are tabulated by age and date of diagnosis (period) as well as date of birth (cohort) in 5-year classes. Each observation in the dataframe correponds to a triangle in a Lexis diagram. Triangles are classified by age and date of diagnosis, period of diagnosis and date of birth, all in 5-year groupings. } \source{The Danish Cancer Registry and Statistics Denmark. } \references{ For a more thorough exposition of statistical inference in the Lexis diagram, see: B. Carstensen: Age-Period-Cohort models for the Lexis diagram. Statistics in Medicine, 26: 3018-3045, 2007. } \examples{ data( lungDK ) # Draw a Lexis diagram and show the number of cases in it. attach( lungDK ) Lexis.diagram( age=c(40,90), date=c(1943,1993), coh.grid=TRUE ) text( Px, Ax, paste( D ), cex=0.7 ) } \keyword{datasets} Epi/man/births.Rd0000644000175100001440000000171712531361107013347 0ustar hornikusers\name{births} \alias{births} \docType{data} \title{Births in a London Hospital} \description{ Data from 500 singleton births in a London Hospital } \usage{data(births)} \format{ A data frame with 500 observations on the following 8 variables. \tabular{rl}{ \code{id}: \tab Identity number for mother and baby. \cr \code{bweight}: \tab Birth weight of baby. \cr \code{lowbw}: \tab Indicator for birth weight less than 2500 g. \cr \code{gestwks}: \tab Gestation period. \cr \code{preterm}: \tab Indicator for gestation period less than 37 weeks. \cr \code{matage}: \tab Maternal age. \cr \code{hyp}: \tab Indicator for maternal hypertension. \cr \code{sex}: \tab Sex of baby: 1:Male, 2:Female. \cr } } \source{ Anonymous } \references{ Michael Hills and Bianca De Stavola (2002). A Short Introduction to Stata 8 for Biostatistics, Timberlake Consultants Ltd \url{http://www.timberlake.co.uk} } \examples{ data(births) } \keyword{datasets} Epi/man/transform.Lexis.Rd0000644000175100001440000000621612531361107015151 0ustar hornikusers\name{transform.Lexis} \alias{transform.Lexis} \alias{Relevel.Lexis} \alias{transform.stacked.Lexis} \alias{factorize} \alias{factorize.Lexis} \alias{levels.Lexis} \title{Transform a Lexis (or stacked.Lexis) objects} \description{ Modify a Lexis object. } \usage{ %transform(`_data`, \dots) \method{transform}{Lexis}( `_data`, \dots) \method{Relevel}{Lexis}( x, states, print = TRUE, \dots ) \method{levels}{Lexis}( x ) \method{factorize}{Lexis}( x, states, print = TRUE, \dots ) \method{transform}{stacked.Lexis}( `_data`, \dots) } \arguments{ \item{_data}{an object of class \code{Lexis}.} \item{x}{an object of class \code{Lexis}.} \item{states}{Names of the factor levels (states) for \code{lex.Cst} and \code{lex.Xst}. Can be a list, in which case some levels are collapsed, see the documentation for \code{\link{Relevel}}. No sanity check for the latter operation is undertaken.} \item{print}{Should a conversion between old and new levels be printed?} \item{\dots}{Additional arguments to be passed to \code{\link{transform.data.frame}} or \code{\link{Relevel}}.} } \details{ The transform method for \code{Lexis} objects works exactly as the method for data frames. \code{factorize} transforms the variables \code{lex.Cst} and \code{lex.Xst} to factors with identical set of levels, optionally with names given in \code{states}, and optionally collapsing states. \code{Relevel} is merely an alias for \code{factorize}, since the function does the same as \code{\link{Relevel}}, but for both the factors \code{lex.Cst} and \code{lex.Xst}. A default sideeffect is to produce a table of old states versus new states if \code{states} is a list. If \code{states} is \code{NULL}, as when for example the argument is not passed to the function, the returned object have levels of \code{lex.Cst}, \code{lex.Xst} (and for \code{stacked.Lexis} objects \code{lex.Tr}) shaved down to the actually occurring values. } \value{ A transformed \code{Lexis} object. The function \code{levels} returns the names of the states (levels of the factors \code{lex.Cst} and \code{lex.Xst}. } \author{Martyn Plummer, Bendix Carstensen} \seealso{\code{\link{Lexis}}, \code{\link{merge.Lexis}}, \code{\link{subset.Lexis}}, \code{\link{subset.stacked.Lexis}}, \code{\link{Relevel}}} \examples{ data( nickel ) nic <- Lexis( data = nickel, id = id, entry = list(age=agein), exit = list(age=ageout,cal=ageout+dob,tfh=ageout-age1st), ## Lung cancer deaths are coded 2 and other deaths are coded 1 exit.status = ( (icd > 0) + (icd \%in\% c(162,163)) ) ) str( nic ) levels( nic ) nit <- transform( nic, cumex = exposure*(agein-age1st) ) str( nit ) ## It is still a Lexis object! summary( nic ) nix <- factorize.Lexis( nic, c("Alive","Lung","Dead")) niw <- factorize.Lexis( nix, c("Alive","Pulm","Mort")) niz <- factorize.Lexis( niw, states=list("Alive",c("Pulm","Mort")), coll=" \n& ") boxes( niw, boxpos=TRUE ) par( new=TRUE ) boxes( niz, boxpos=TRUE ) siw <- stack( niw ) str( siw ) } \keyword{manip} Epi/man/B.dk.Rd0000644000175100001440000000421112531361107012622 0ustar hornikusers\name{B.dk} \alias{B.dk} \docType{data} \title{Births in Denmark by year and month of birth and sex} \description{ The number of live births as entered from printed publications from Statistics Denmark. } \usage{data(B.dk)} \format{ A data frame with 1248 observations on the following 4 variables. \describe{ \item{\code{year}}{Year of birth} \item{\code{month}}{Month of birth} \item{\code{m}}{Number of male births} \item{\code{f}}{Number of female births} } } \details{ Division of births by month and sex is only avaialble for the years 1957--69 and 2002ff. For the remaining period, the total no. births in each month is divided between the sexes so that the fraction of boys is equal to the overall fraction for the years where the sex information is available. There is a break in the series at 1920, when Sonderjylland was joined to Denmark. } \source{ Statistiske Undersogelser nr. 19: Befolkningsudvikling og sundhedsforhold 1901-60, Copenhagen 1966. Befolkningens bevaegelser 1957. Befolkningens bevaegelser 1958. ... Befolkningens bevaegelser 2003. Befolkningens bevaegelser 2004. Vital Statistics 2005. Vital Statistics 2006. } \examples{ data( B.dk ) str( B.dk ) attach( B.dk ) # Plot the no of births and the M/F-ratio par( las=1, mar=c(4,4,2,4) ) matplot( year+(month-0.5)/12, cbind( m, f ), bty="n", col=c("blue","red"), lty=1, lwd=1, type="l", ylim=c(0,5000), xlab="Date of birth", ylab="" ) usr <- par()$usr mtext( "Monthly no. births in Denmark", side=3, adj=0, at=usr[1], line=1/1.6 ) text( usr[1:2] \%*\% cbind(c(19,1),c(19,1))/20, usr[3:4] \%*\% cbind(c(1,19),c(2,18))/20, c("Boys","Girls"), col=c("blue","red"), adj=0 ) lines( year+(month-0.5)/12, (m/(m+f)-0.5)*30000, lwd=1 ) axis( side=4, at=(seq(0.505,0.525,0.005)-0.5)*30000, labels=c("","","","",""), tcl=-0.3 ) axis( side=4, at=(50:53/100-0.5)*30000, labels=50:53, tcl=-0.5 ) axis( side=4, at=(0.54-0.5)*30000, labels="\% boys", tick=FALSE, mgp=c(3,0.1,0) ) abline( v=1920, col=gray(0.8) ) } \keyword{datasets} Epi/man/crr.Lexis.rd0000644000175100001440000000737012531361107013766 0ustar hornikusers\name{crr.Lexis} \alias{crr.Lexis} \title{Fit a competing risks regression model (Fine-Gray model) using a Lexis object) } \description{ Fits a competing risks regression model using a \code{\link{Lexis}} object assuming that every person enters at time 0 and exits at time \code{lex.dur}. Thus is only meaningful for Lexis objects with one record per person, (so far). } \usage{ crr.Lexis( obj, mod, quiet=FALSE, ...) } \arguments{ \item{obj}{A Lexis object; variables in \code{mod} are taken from this.} \item{mod}{Formula, with the l.h.s. a character constant equal to a level of \code{obj$lex.Xst}, and the r.h.s. a model formula interpreted in \code{obj}.} \item{quiet}{Logical indicating whether a brief summary should be printed.} \item{\dots}{Further arguments passed on to \code{\link[cmprsk:crr]{crr}}.} } \details{ This function is a simple wrapper for \code{crr}, allowing a formula-specification of the model (which allows specifications of covariates on the fly), and utilizing the structure of Lexis objects to simplify specification of the outcome. Prints a summary of the levels used as event, competing events and censoring. By the structure of the \code{\link{Lexis}} object it is not necessary to indicate what the censoring code or competing events are, that is automatically derived from the \code{Lexis} object. Currently only one state is allowed as l.h.s. (response) in \code{mod}. } \value{ A \code{\link[cmprsk:crr]{crr}} object (which is a list), with two extra elements in the list, \code{model.Lexis} - the model formula supplied, and \code{transitions} - a table of transitions and censorings showing which transition was analysed and which were taken as competing events. } \author{ Bendix Carstensen, \url{BendixCarstensen.com} } \seealso{ \code{\link[cmprsk:crr]{crr}}, \code{\link{Lexis}} } \examples{ # Thorotrats patients, different histological types of liver cancer # Load thorotrast data, and restrict to exposed data(thoro) tht <- thoro[thoro$contrast==1,] # Define exitdate as the date of livercancer tht$dox <- pmin( tht$liverdat, tht$exitdat, na.rm=TRUE ) tht <- subset( tht, dox > injecdat ) # Convert to calendar years in dates tht <- cal.yr( tht ) # Set up a Lexis object with three subtypes of liver cancer and death tht.L <- Lexis( entry = list( per = injecdat, tfi = 0 ), exit = list( per = dox ), exit.status = factor( 1*hepcc+2*chola+3*hmang+ 4*(hepcc+chola+hmang==0 & exitstat==1), labels=c("No cancer","hepcc","chola","hmang","Dead") ), data = tht ) summary( tht.L ) # Show the transitions boxes( tht.L, boxpos=list(x=c(20,rep(80,3),30), y=c(60,90,60,30,10) ), show.BE=TRUE, scale.R=1000 ) # Fit a model for the Hepatocellular Carcinoma as outcome # - note that you can create a variable on the fly: library( cmprsk ) hepcc <- crr.Lexis( tht.L, "hepcc" ~ volume + I(injecdat-1940) ) hepcc$model.Lexis hepcc$transitions # Models for the three other outcomes: chola <- crr.Lexis( tht.L, "chola" ~ volume + I(injecdat-1940) ) hmang <- crr.Lexis( tht.L, "hmang" ~ volume + I(injecdat-1940) ) dead <- crr.Lexis( tht.L, "Dead" ~ volume + I(injecdat-1940) ) # Compare the effects # NOTE: This is not necessarily a joint model for all transitions. zz <- rbind( ci.exp(hepcc), ci.exp(chola), ci.exp(hmang), ci.exp(dead) ) zz <- cbind( zz[c(1,3,5,7) ,], zz[c(1,3,5,7)+1,] ) rownames( zz ) <- c("hepcc","chola","hmang","dead") colnames( zz )[c(1,4)] <- rownames( ci.exp(chola) ) round( zz, 3 ) } \keyword{survival} Epi/man/nice.Rd0000644000175100001440000000142012531361107012761 0ustar hornikusers\name{nice} \alias{nice} \title{Nice breakpoints} \description{The function calls \code{\link{pretty}} for linear scale. For a log-scale nice are computed using a set of specified number in a decade. } \usage{ nice(x, log = F, lpos = c(1, 2, 5), ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{Numerical vector to} \item{log}{Logical. Is the scale logartimic?} \item{lpos}{Numeric. Numbers between 1 and 10 giving the desired breakpoints in this interval.} \item{\dots}{Arguments passed on to \code{pretty} if \code{log}=FALSE} } \value{A vector of breakpoints.} \author{Bendix Carstensen, \email{bxc@steno.dk}, \url{http://BendixCarstensen.com}} \seealso{pretty} \examples{ nice( exp( rnorm( 100 ) ), log=TRUE ) } \keyword{manip} Epi/man/occup.Rd0000644000175100001440000000333312531361107013161 0ustar hornikusers\name{occup} \alias{occup} \docType{data} \title{ A small occupational cohort } \description{This is the data that is behind the illustrative Lexis diagram in Breslow & Day's book on case-control studies.} \usage{data(occup)} \format{ A data frame with 13 observations on the following 4 variables. \describe{ \item{\code{AoE}}{a numeric vector, Age at Entry} \item{\code{DoE}}{a numeric vector, Date of entry} \item{\code{DoX}}{a numeric vector, Date of eXit} \item{\code{Xst}}{eXit status \code{D}-event, \code{W}-withdrawal, \code{X}-censoring} } } \references{ Breslow & Day: Statistical Methods in Cancer Research, vol 1: The analysis of case-control studies, figure 2.2, p. 48.} \examples{ data(occup) lx <- Lexis( entry = list( per=DoE, age=AoE ), exit = list( per=DoX ), entry.status = "W", exit.status = Xst, data = occup ) plot( lx ) # Split follow-up in 5-year classes sx <- splitLexis( lx, seq(1940,1960,5), "per" ) sx <- splitLexis( sx, seq( 40, 60,5), "age" ) plot( sx ) # Plot with a bit more paraphernalia and a device to get # the years on the same physical scale on both axes ypi <- 2.5 # Years per inch dev.new( height=15/ypi+1, width=20/ypi+1 ) # add an inch in each direction for par( mai=c(3,3,1,1)/4, mgp=c(3,1,0)/1.6 ) # the margins set in inches by mai= plot(sx,las=1,col="black",lty.grid=1,lwd=2,type="l", xlim=c(1940,1960),ylim=c(40,55),xaxs="i",yaxs="i",yaxt="n", xlab="Calendar year", ylab="Age (years)") axis( side=2, at=seq(40,55,5), las=1 ) points(sx,pch=c(NA,16)[(sx$lex.Xst=="D")+1] ) box() # Annotation with the person-years PY.ann.Lexis( sx, cex=0.8 ) } \keyword{datasets} Epi/man/projection.ip.rd0000644000175100001440000000171612562641052014702 0ustar hornikusers\name{projection.ip} \alias{projection.ip} \title{ Projection of columns of a matrix. } \description{ Projects the columns of the matrix \code{M} on the space spanned by the columns of the matrix \code{X}, with respect to the inner product defined by \code{weight}: \code{=sum(x*w*y)}. } \usage{ projection.ip(X, M, orth = FALSE, weight = rep(1, nrow(X))) } \arguments{ \item{X}{ Matrix defining the space to project onto. } \item{M}{ Matrix of columns to be projected. Must have the same number of rows as \code{X}. } \item{orth}{ Should the projection be on the orthogonal complement to \code{span(X)}? } \item{weight}{ Weights defining the inner product. Numerical vector of length \code{nrow(X)}. } } \value{ A matrix of full rank with columns in \code{span(X)} } \author{ Bendix Carstensen, Steno Diabetes Center, \url{http://BendixCarstensen.com}, with help from Peter Dalgaard. } \seealso{ \code{\link{detrend}} } \keyword{array} Epi/man/S.typh.Rd0000644000175100001440000000421612531361107013236 0ustar hornikusers\name{S.typh} \alias{S.typh} \docType{data} \title{Salmonella Typhimurium outbreak 1996 in Denmark.} \description{ Matched case-control study of food poisoning. } \format{ A data frame with 136 observations on the following 15 variables: \tabular{rl}{ \code{id}: \tab Person identification \cr \code{set}: \tab Matched set indicator \cr \code{case}: \tab Case-control status (1:case, 0:control \cr \code{age}: \tab Age of individual \cr \code{sex}: \tab Sex of individual (1:male, 2:female) \cr \code{abroad}: \tab Within the last two weeks visited abroad (1:yes, 0:no) \cr \code{beef}: \tab Within the last two weeks eaten beef \cr \code{pork}: \tab Within the last two weeks eaten pork \cr \code{veal}: \tab Within the last two weeks eaten veal \cr \code{poultry}: \tab Within the last two weeks eaten poultry \cr \code{liverp}: \tab Within the last two weeks eaten liverpaste \cr \code{veg}: \tab Within the last two weeks eaten vegetables \cr \code{fruit}: \tab Within the last two weeks eaten fruit \cr \code{egg}: \tab Within the last two weeks eaten eggs \cr \code{plant7}: \tab Within the last two weeks eaten meat from plant no. 7 \cr } } \details{ In the fall of 1996 an unusually large number of Salmonella Typhimurium cases were recorded in Fyn county in Denmark. The Danish Zoonosis Centre set up a matched case-control study to find the sources. Cases and two age-, sex- and residency-matched controls were telephone interviewed about their food intake during the last two weeks. The participants were asked at which retailer(s) they had purchased meat. Retailers were independently of this linked to meat processing plants, and thus participants were linked to meat processing plants. This way persons could be linked to (amongst other) plant no 7.} \source{ Tine Hald. } \references{ Molbak K and Hald T: Salmonella Typhimurium outbreak in late summer 1996. A Case-control study. (In Danish: Salmonella typhimurium udbrud paa Fyn sensommeren 1996. En case-kontrol undersogelse.) Ugeskrift for Laeger., 159(36):5372-7, 1997. } \examples{ data(S.typh) } \keyword{datasets} Epi/man/pctab.Rd0000644000175100001440000000222412531361107013137 0ustar hornikusers\name{pctab} \alias{pctab} \title{Create percentages in a table} \description{ Computes percentages and a margin of totals along a given margin of a table. } \usage{ pctab(TT, margin = length(dim(TT)), dec=1) } \arguments{ \item{TT}{A table or array object} \item{margin}{Which margin should be the the total?} \item{dec}{How many decimals should be printed? If 0 or \code{FALSE} nothing is printed} } \value{ A table of percentages, where all dimensions except the one specified \code{margin} has two extra levels named "All" (where all entries are 100) and "N". The function prints the table with \code{dec} decimals. } \author{ Bendix Carstensen, Steno Diabetes Center, \url{http://BendixCarstensen.com}. } \seealso{ \code{\link{addmargins}} } \examples{ Aye <- sample( c("Yes","Si","Oui"), 177, replace=TRUE ) Bee <- sample( c("Hum","Buzz"), 177, replace=TRUE ) Sea <- sample( c("White","Black","Red","Dead"), 177, replace=TRUE ) A <- table( Aye, Bee, Sea ) A ftable( pctab( A ) ) ftable( pctab( addmargins( A, 1 ), 3 ) ) round( ftable( pctab( addmargins( A, 1 ), 3 ), row.vars=3 ), 1) } \keyword{ manip } \keyword{ methods } \keyword{ array } Epi/man/effx.match.Rd0000644000175100001440000000474412531361107014102 0ustar hornikusers\name{effx.match} \alias{effx.match} \title{Function to calculate effects for individually matched case-control studies} \description{ The function calculates the effects of an exposure on a response, possibly stratified by a stratifying variable, and/or controlled for one or more confounding variables. } \usage{ effx.match(response, exposure, match, strata=NULL, control=NULL, base=1, digits=3, alpha=0.05, data=NULL) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{response}{The \code{response} variable - must be numeric} \item{exposure}{The \code{exposure} variable can be numeric or a factor} \item{match}{The variable which identifies the matched sets} \item{strata}{The \code{strata} stratifying variable - must be a factor} \item{control}{ The \code{control} variable(s). These are passed as a list if there are more than one of them.} \item{base}{Baseline for the effects of a categorical exposure, default 1} \item{digits}{Number of significant digits for the effects, default 3} \item{alpha}{1 - confidence level} \item{data}{\code{data} refers to the data used to evaluate the function} } \details{Effects are calculated odds ratios. The function is a wrapper for clogit, from the survival package. The k-1 effects for a categorical exposure with k levels are relative to a baseline which, by default, is the first level. The effect of a metric (quantitative) exposure is calculated per unit of exposure. The exposure variable can be numeric or a factor, but if it is an ordered factor the order will be ignored. } \value{ % ~Describe the value returned % If it is a LIST, use \item{comp1 }{Effects of exposure} \item{comp2 }{Tests of significance} % ... } \references{ www.mhills.pwp.blueyonder.co.uk } \author{Michael Hills} %\note{ ~~further notes~~ } %\seealso{ ~~objects to See Also as \code{\link{~~fun~~}}, ~~~ } \examples{ library(Epi) library(survival) data(bdendo) # d is the case-control variable, set is the matching variable. # The variable est is a factor and refers to estrogen use (no,yes) # The variable hyp is a factor with 2 levels and refers to hypertension (no, yes) # effect of est on the odds of being a case effx.match(d,exposure=est,match=set,data=bdendo) # effect of est on the odds of being a case, stratified by hyp effx.match(d,exposure=est,match=set,strata=hyp,data=bdendo) # effect of est on the odds of being a case, controlled for hyp effx.match(d,exposure=est,match=set,control=hyp,data=bdendo) } \keyword{ models } \keyword{ regression } Epi/man/stattable.Rd0000644000175100001440000001135312531361107014034 0ustar hornikusers\name{stat.table} \alias{stat.table} \alias{print.stat.table} \title{Tables of summary statistics} \description{ \code{stat.table} creates tabular summaries of the data, using a limited set of functions. A list of index variables is used to cross-classify summary statistics. It does NOT work inside \code{with()}! } \usage{ stat.table(index, contents = count(), data, margins = FALSE) \method{print}{stat.table}(x, width=7, digits,...) } \arguments{ \item{index}{A factor, or list of factors, used for cross-classification. If the list is named, then the names will be used when printing the table. This feature can be used to give informative labels to the variables.} \item{contents}{A function call, or list of function calls. Only a limited set of functions may be called (See Details below). If the list is named, then the names will be used when printing the table.} \item{data}{an optional data frame containing the variables to be tabulated. If this is omitted, the variables will be searched for in the calling environment.} \item{margins}{a logical scalar or vector indicating which marginal tables are to be calculated. If a vector, it should be the same length as the \code{index} argument: values corresponding to \code{TRUE} will be retained in marginal tables.} \item{x}{an object of class \code{stat.table}.} \item{width}{a scalar giving the minimum column width when printing.} \item{digits}{a scalar, or named vector, giving the number of digits to print after the decimal point. If a named vector is used, the names should correspond to one of the permitted functions (See Details below) and all results obtained with that function will be printed with the same precision.} \item{...}{further arguments passed to other print methods.} } \details{ This function is similar to \code{tapply}, with some enhancements: multiple summaries of multiple variables may be mixed in the same table; marginal tables may be calculated; columns and rows may be given informative labels; pretty printing may be controlled by the associated print method. This function is not a replacement for \code{tapply} as it also has some limitations. The only functions that may be used in the \code{contents} argument are: \code{\link{count}}, \code{\link{mean}}, \code{\link{weighted.mean}}, \code{\link{sum}}, \code{\link{quantile}}, \code{\link{median}}, \code{\link{IQR}}, \code{\link{max}}, \code{\link{min}}, \code{\link{ratio}}, \code{\link{percent}}, and \code{\link{sd}}. The \code{count()} function, which is the default, simply creates a contingency table of counts. The other functions are applied to each cell created by combinations of the \code{index} variables. } \value{ An object of class \code{stat.table}, which is a multi-dimensional array. A print method is available to create formatted one-way and two-way tables. } \author{Martyn Plummer} \note{ The permitted functions in the contents list %are overloaded functions that are defined inside \code{stat.table}. They have the same interface as the functions callable from the command line, except for two differences. If there is an argument \code{na.rm} then its default value is always \code{TRUE}. A second difference is that the \code{quantile} function can only produce a single quantile in each call. } \seealso{\code{\link{table}}, \code{\link{tapply}}, \code{\link{mean}}, \code{\link{weighted.mean}}, \code{\link{sum}}, \code{\link{quantile}}, \code{\link{median}}, \code{\link{IQR}}, \code{\link{max}}, \code{\link{min}}, \code{\link{ratio}}, \code{\link{percent}}, \code{\link{count}}, \code{\link{sd}}.} \examples{ data(warpbreaks) # A one-way table stat.table(tension,list(count(),mean(breaks)),data=warpbreaks) # The same table with informative labels stat.table(index=list("Tension level"=tension),list(N=count(), "mean number of breaks"=mean(breaks)),data=warpbreaks) # A two-way table stat.table(index=list(tension,wool),mean(breaks),data=warpbreaks) # The same table with margins over tension, but not wool stat.table(index=list(tension,wool),mean(breaks),data=warpbreaks, margins=c(TRUE, FALSE)) # A table of column percentages stat.table(list(tension,wool), percent(tension), data=warpbreaks) # Cell percentages, with margins stat.table(list(tension,wool),percent(tension,wool), margin=TRUE, data=warpbreaks) # A table with multiple statistics # Note how each statistic has its own default precision a <- stat.table(index=list(wool,tension), contents=list(count(),mean(breaks),percent (wool)), data=warpbreaks) print(a) # Print the percentages rounded to the nearest integer print(a, digits=c(percent=0)) } \keyword{iteration} \keyword{category} Epi/man/brv.Rd0000644000175100001440000000363212531361107012643 0ustar hornikusers\name{brv} \alias{brv} \docType{data} \title{Bereavement in an elderly cohort} \description{ The \code{brv} data frame has 399 rows and 11 columns. The data concern the possible effect of marital bereavement on subsequent mortality. They arose from a survey of the physical and mental health of a cohort of 75-year-olds in one large general practice. These data concern mortality up to 1 January, 1990 (although further follow-up has now taken place). Subjects included all lived with a living spouse when they entered the study. There are three distinct groups of such subjects: (1) those in which both members of the couple were over 75 and therefore included in the cohort, (2) those whose spouse was below 75 (and was not, therefore, part of the main cohort study), and (3) those living in larger households (that is, not just with their spouse). } \format{ This data frame contains the following columns: \describe{ \item{\code{id}}{subject identifier, a numeric vector} \item{\code{couple}}{couple identifier, a numeric vector} \item{\code{dob}}{date of birth, a date} \item{\code{doe}}{date of entry into follow-up study, a date} \item{\code{dox}}{date of exit from follow-up study, a date} \item{\code{dosp}}{date of death of spouse, a date (if the spouse was still alive at the end of follow-up,this was coded to January 1, 2000)} \item{\code{fail}}{status at end of follow-up, a numeric vector (0=alive,1=dead)} \item{\code{group}}{see Description, a numeric vector} \item{\code{disab}}{disability score, a numeric vector} \item{\code{health}}{perceived health status score, a numeric vector} \item{\code{sex}}{a factor with levels \code{Male} and \code{Female} } } } \source{ Jagger C, and Sutton CJ, Death after Marital Bereavement. Statistics in Medicine, 10:395-404, 1991. (Data supplied by Carol Jagger). } \examples{ data(brv) } \keyword{datasets} Epi/man/detrend.Rd0000644000175100001440000000207012531361107013472 0ustar hornikusers\name{detrend} \alias{detrend} \title{ Projection of a model matrix on to the orthogonal complement of a trend. } \description{ The columns of the model matrix \code{M} is projected on the orthogonal complement to the matrix \code{(1,t)}. Orthogonality is defined w.r.t. an inner product defined by the weights \code{weight}. } \usage{ detrend( M, t, weight = rep(1, nrow(M)) ) } \arguments{ \item{M}{ A model matrix. } \item{t}{ The trend defining a subspace. A numerical vector of length \code{nrow(M)} } \item{weight}{ Weights defining the inner product of vectors \code{x} and \code{y} as \code{sum(x*w*y)}. A numerical vector of length \code{nrow(M)}, defaults to a vector of \code{1}s.} } \details{ The functions is intended to be used in parametrization of age-period-cohort models. } \value{ A full-rank matrix with columns orthogonal to \code{(1,t)}. } \author{ Bendix Carstensen, Steno Diabetes Center, \url{http://BendixCarstensen.com}, with help from Peter Dalgaard. } \seealso{ \code{\link{projection.ip}} } \keyword{array} Epi/man/bdendo11.Rd0000644000175100001440000000100312531361107013435 0ustar hornikusers\name{bdendo11} \alias{bdendo11} \docType{data} \title{A 1:1 subset of the endometrial cancer case-control study} \description{ The \code{bdendo11} data frame has 126 rows and 13 columns. This is a subset of the dataset \code{\link{bdendo}} in which each case was matched with a single control. } \source{ Breslow NE, and Day N, Statistical Methods in Cancer Research. Volume I: The Analysis of Case-Control Studies. IARC Scientific Publications, IARC:Lyon, 1980. } \examples{ data(bdendo11) } \keyword{datasets} Epi/man/ftrend.Rd0000644000175100001440000000476612531361107013345 0ustar hornikusers\name{ftrend} \alias{ftrend} \title{Fit a floating trend to a factor in generalized linear model} \description{ Fits a "floating trend" model to the given factor in a glm in a generalized linear model by centering covariates. } \usage{ ftrend(object, ...) } \arguments{ \item{object}{fitted \code{lm} or \code{glm} object. The model must not have an intercept term} \item{...}{arguments to the \code{nlm} function} } \details{ \code{ftrend()} calculates "floating trend" estimates for factors in generalized linear models. This is an alternative to treatment contrasts suggested by Greenland et al. (1999). If a regression model is fitted with no intercept term, then contrasts are not used for the first factor in the model. Instead, there is one parameter for each level of this factor. However, the interpretation of these parameters, and their variance-covariance matrix, depends on the numerical coding used for the covariates. If an arbitrary constant is added to the covariate values, then the variance matrix is changed. The \code{ftrend()} function takes the fitted model and works out an optimal constant to add to the covariate values so that the covariance matrix is approximately diagonal. The parameter estimates can then be treated as approximately independent, thus simplifying their presentation. This is particularly useful for graphical display of dose-response relationships (hence the name). Greenland et al. (1999) originally suggested centring the covariates so that their weighted mean, using the fitted weights from the model, is zero. This heuristic criterion is improved upon by \code{ftrend()} which uses the same minimum information divergence criterion as used by Plummer (2003) for floating variance calculations. \code{ftrend()} calls \code{nlm()} to do the minimization and will pass optional arguments to control it. } \note{ The "floating trend" method is an alternative to the "floating absolute risk" method, which is implemented in the function \code{float()}. } \value{ A list with the following components \item{coef}{coefficients for model with adjusted covariates.} \item{vcov}{Variance-covariance matrix of adjusted coefficients.} } \references{ Greenland S, Michels KB, Robins JM, Poole C and Willet WC (1999) Presenting statistical uncertainty in trends and dose-response relations, \emph{American Journal of Epidemiology}, \bold{149}, 1077-1086. } \author{Martyn Plummer} \seealso{\code{\link{float}}} \keyword{regression} Epi/man/ROC.Rd0000644000175100001440000000747712531361107012510 0ustar hornikusers\name{ROC} \alias{ROC} %- Also NEED an `\alias' for EACH other topic documented here. \title{Function to compute and draw ROC-curves.} \description{ Computes sensitivity, specificity and positive and negative predictive values for a test based on dichotomizing along the variable \code{test}, for prediction of \code{stat}. Plots curves of these and a ROC-curve. } \usage{ ROC( test = NULL, stat = NULL, form = NULL, plot = c("sp", "ROC"), PS = is.null(test), PV = TRUE, MX = TRUE, MI = TRUE, AUC = TRUE, grid = seq(0,100,10), col.grid = gray( 0.9 ), cuts = NULL, lwd = 2, data = parent.frame(), ... ) } \arguments{ \item{test}{ Numerical variable used for prediction. } \item{stat}{ Logical variable of true status. } \item{form}{ Formula used in a logistic regression. If this is given, \code{test} and \code{stat} are ignored. If not given then both \code{test} and \code{stat} must be supplied. } \item{plot}{ Character variable. If "sp", the a plot of sensitivity, specificity and predictive values against test is produced, if "ROC" a ROC-curve is plotted. Both may be given.} \item{PS}{logical, if TRUE the x-axis in the plot "ps"-plot is the the predicted probability for \code{stat}==TRUE, otherwise it is the scale of \code{test} if this is given otherwise the scale of the linear predictor from the logistic regression.} \item{PV}{Should sensitivity, specificity and predictive values at the optimal cutpoint be given on the ROC plot? } \item{MX}{Should the ``optimal cutpoint'' (i.e. where sens+spec is maximal) be indicated on the ROC curve?} \item{MI}{Should model summary from the logistic regression model be printed in the plot?} \item{AUC}{Should the area under the curve (AUC) be printed in the ROC plot?} \item{grid}{Numeric or logical. If FALSE no background grid is drawn. Otherwise a grid is drawn on both axes at \code{grid} percent.} \item{col.grid}{Colour of the grid lines drawn.} \item{cuts}{Points on the test-scale to be annotated on the ROC-curve. } \item{lwd}{Thickness of the curves} \item{data}{Data frame in which to interpret the variables.} \item{\dots}{Additional arguments for the plotting of the ROC-curve. Passed on to \code{plot}} } \details{ As an alternative to a \code{test} and a \code{status} variable, a model formula may given, in which case the the linear predictor is the test variable and the response is taken as the true status variable. The test used to derive sensitivity, specificity, PV+ and PV- as a function of \eqn{x} is \code{test}\eqn{\geq x}{>=x} as a predictor of \code{stat}=TRUE. } \value{ A list with two components: \item{res}{dataframe with variables \code{sens}, \code{spec}, \code{pvp}, \code{pvn} and name of the test variable. The latter is the unique values of test or linear predictor from the logistic regression in ascending order with -Inf prepended. Since the sensitivity is defined as \eqn{P(test>x)|status=TRUE}, the first row has \code{sens} equal to 1 and \code{spec} equal to 0, corresponding to drawing the ROC curve from the upper right to the lower left corner.} \item{lr}{glm object with the logistic regression result used for construction of the ROC curve} 0, 1 or 2 plots are produced according to the setting of \code{plot}. } \author{Bendix Carstensen, Steno Diabetes Center \& University of Copenhagen, \url{http://BendixCarstensen.com} } \examples{ x <- rnorm( 100 ) z <- rnorm( 100 ) w <- rnorm( 100 ) tigol <- function( x ) 1 - ( 1 + exp( x ) )^(-1) y <- rbinom( 100, 1, tigol( 0.3 + 3*x + 5*z + 7*w ) ) ROC( form = y ~ x + z, plot="ROC" ) } \keyword{manip} \keyword{htest} %\keyword{ROC-curves} %\keyword{sensitivity} %\keyword{specificity} %\keyword{predictive values} Epi/man/Icens.Rd0000644000175100001440000000672412531361107013120 0ustar hornikusers\name{Icens} \alias{Icens} \alias{print.Icens} \title{ Fits a regression model to interval censored data. } \description{ The models fitted assumes a piecewise constant baseline rate in intervals specified by the argument \code{breaks}, and for the covariates either a multiplicative relative risk function (default) or an additive excess risk function. } \usage{ Icens( first.well, last.well, first.ill, formula, model.type=c("MRR","AER"), breaks, boot=FALSE, alpha=0.05, keep.sample=FALSE, data ) } \arguments{ \item{first.well}{Time of entry to the study, i.e. the time first seen without event. Numerical vector.} \item{last.well}{Time last seen without event. Numerical vector.} \item{first.ill}{Time first seen with event. Numerical vector.} \item{formula}{Model formula for the log relative risk.} \item{model.type}{Which model should be fitted.} \item{breaks}{Breakpoints between intervals in which the underlying timescale is assumed constant. Any observation outside the range of \code{breaks} is discarded.} \item{boot}{Should bootstrap be performed to produce confidence intervals for parameters. If a number is given this will be the number of bootsrap samples. The default is 1000.} \item{alpha}{1 minus the confidence level.} \item{keep.sample}{Should the bootstrap sample of the parameter values be returned?} \item{data}{Data frame in which the times and formula are interpreted.} } \details{ The model is fitted by calling either \code{\link{fit.mult}} or \code{\link{fit.add}}. } \value{ An object of class \code{"Icens"}: a list with three components: \item{rates}{A glm object from a binomial model with log-link, estimating the baseline rates, and the excess risk if \code{"AER"} is specfied.} \item{cov}{A glm object from a binomial model with complementary log-log link, estimating the log-rate-ratios. Only if \code{"MRR"} is specfied.} \item{niter}{Nuber of iterations, a scalar} \item{boot.ci}{If \code{boot=TRUE}, a 3-column matrix with estimates and 1-\code{alpha} confidence intervals for the parameters in the model.} \item{sample}{A matrix of the parameterestimates from the bootstrapping. Rows refer to parameters, columns to bootstrap samples.} } \references{ B Carstensen: Regression models for interval censored survival data: application to HIV infection in Danish homosexual men. Statistics in Medicine, 15(20):2177-2189, 1996. CP Farrington: Interval censored survival data: a generalized linear modelling approach. Statistics in Medicine, 15(3):283-292, 1996. } \author{ Martyn Plummer, \email{plummer@iarc.fr}, Bendix Carstensen, \email{bxc@steno.dk} } \seealso{ \code{\link{fit.add}} \code{\link{fit.mult}} } \examples{ data( hivDK ) # Convert the dates to fractional years so that rates are # expressed in cases per year for( i in 2:4 ) hivDK[,i] <- cal.yr( hivDK[,i] ) m.RR <- Icens( entry, well, ill, model="MRR", formula=~pyr+us, breaks=seq(1980,1990,5), data=hivDK) # Currently the MRR model returns a list with 2 glm objects. round( ci.lin( m.RR$rates ), 4 ) round( ci.lin( m.RR$cov, Exp=TRUE ), 4 ) # There is actually a print method: print( m.RR ) m.ER <- Icens( entry, well, ill, model="AER", formula=~pyr+us, breaks=seq(1980,1990,5), data=hivDK) # There is actually a print method: print( m.ER ) } \keyword{ models } \keyword{ regression } \keyword{ survival } Epi/man/Lexis.diagram.Rd0000644000175100001440000001160212531361107014535 0ustar hornikusers\name{Lexis.diagram} \alias{Lexis.diagram} \title{Plot a Lexis diagram} \description{ Draws a Lexis diagram, optionally with life lines from a cohort, and with lifelines of a cohort if supplied. Intended for presentation purposes. } \usage{ Lexis.diagram( age = c( 0, 60), alab = "Age", date = c( 1940, 2000 ), dlab = "Calendar time", int = 5, lab.int = 2*int, col.life = "black", lwd.life = 2, age.grid = TRUE, date.grid = TRUE, coh.grid = FALSE, col.grid = gray(0.7), lwd.grid = 1, las = 1, entry.date = NA, entry.age = NA, exit.date = NA, exit.age = NA, risk.time = NA, birth.date = NA, fail = NA, cex.fail = 1.1, pch.fail = c(NA,16), col.fail = rep( col.life, 2 ), data = NULL, ... ) } \arguments{ \item{age}{Numerical vector of length 2, giving the age-range for the diagram} \item{alab}{Label on the age-axis.} \item{date}{Numerical vector of length 2, giving the calendar time-range for the diagram} \item{dlab}{label on the calendar time axis.} \item{int}{The interval between grid lines in the diagram. If a vector of length two is given, the first value will be used for spacing of age-grid and the second for spacing of the date grid.} \item{lab.int}{The interval between labelling of the grids.} \item{col.life}{Colour of the life lines.} \item{lwd.life}{Width of the life lines.} \item{age.grid}{Should grid lines be drawn for age?} \item{date.grid}{Should grid lines be drawn for date?} \item{coh.grid}{Should grid lines be drawn for birth cohorts (diagonals)?} \item{col.grid}{Colour of the grid lines.} \item{lwd.grid}{Width of the grid lines.} \item{las}{How are the axis labels plotted?} \item{entry.date, entry.age, exit.date, exit.age, risk.time, birth.date}{Numerical vectors defining lifelines to be plotted in the diagram. At least three must be given to produce lines. Not all subsets of three will suffice, the given subset has to define life lines. If insufficient data is given, no life lines are produced.} \item{fail}{Logical of event status at exit for the persons whose life lines are plotted.} \item{pch.fail}{Symbols at the end of the life lines for censorings (\code{fail==0}) and failures (\code{fail != 0}).} \item{cex.fail}{Expansion of the status marks at the end of life lines.} \item{col.fail}{Character vector of length 2 giving the colour of the failure marks for censorings and failures respectively.} \item{data}{Dataframe in which to interpret the arguments.} \item{...}{Arguments to be passed on to the initial call to plot.} } \value{ If sufficient information on lifelines is given, a data frame with one row per person and columns with entry ages and dates, birth date, risk time and status filled in. Side effect: a plot of a Lexis diagram is produced with the life lines in it is produced. This will be the main reason for using the function. If the primary aim is to illustrate follow-up of a cohort, then it is better to represent the follow-up in a \code{\link{Lexis}} object, and use the generic \code{\link{plot.Lexis}} function. } \details{ The default unit for supplied variables are (calendar) years. If any of the variables \code{entry.date}, \code{exit.date} or \code{birth.date} are of class "\code{Date}" or if any of the variables \code{entry.age}, \code{exit.age} or \code{risk.time} are of class "\code{difftime}", they will be converted to calendar years, and plotted correctly in the diagram. The returned dataframe will then have colums of classes "\code{Date}" and "\code{difftime}". } \author{ Bendix Carstensen, \url{http://BendixCarstensen.com} } \examples{ Lexis.diagram( entry.age = c(3,30,45), risk.time = c(25,5,14), birth.date = c(1970,1931,1925.7), fail = c(TRUE,TRUE,FALSE) ) LL <- Lexis.diagram( entry.age = sample( 0:50, 17, replace=TRUE ), risk.time = sample( 5:40, 17, r=TRUE), birth.date = sample( 1910:1980, 17, r=TRUE ), fail = sample( 0:1, 17, r=TRUE ), cex.fail = 1.1, lwd.life = 2 ) # Identify the persons' entry and exits text( LL$exit.date, LL$exit.age, paste(1:nrow(LL)), col="red", font=2, adj=c(0,1) ) text( LL$entry.date, LL$entry.age, paste(1:nrow(LL)), col="blue", font=2, adj=c(1,0) ) data( nickel ) attach( nickel ) LL <- Lexis.diagram( age=c(10,100), date=c(1900,1990), entry.age=age1st, exit.age=ageout, birth.date=dob, fail=(icd \%in\% c(162,163)), lwd.life=1, cex.fail=0.8, col.fail=c("green","red") ) abline( v=1934, col="blue" ) nickel[1:10,] LL[1:10,] } \keyword{hplot} \keyword{dplot} \seealso{ \code{\link{Life.lines}}, \code{\link{Lexis.lines}} } Epi/man/fit.add.Rd0000644000175100001440000000264212531361107013363 0ustar hornikusers\name{fit.add} \alias{fit.add} \title{ Fit an addive excess risk model to interval censored data. } \description{ Utility function. The model fitted assumes a piecewise constant intensity for the baseline, and that the covariates act additively on the rate scale. } \usage{ fit.add( y, rates.frame, cov.frame, start ) } \arguments{ \item{y}{Binary vector of outcomes} \item{rates.frame}{Dataframe expanded from the original data by \code{\link{expand.data}}, cooresponding to covariates for the rate parameters.} \item{cov.frame}{ do., but covariates corresponding to the \code{formula} argument of \code{\link{Icens}}} \item{start}{Starting values for the rate parameters. If not supplied, then starting values are generated.} } \value{ A list with one component: \item{rates}{A glm object from a binomial model with log-link function.} } \references{ B Carstensen: Regression models for interval censored survival data: application to HIV infection in Danish homosexual men. Statistics in Medicine, 15(20):2177-2189, 1996. CP Farrington: Interval censored survival data: a generalized linear modelling approach. Statistics in Medicine, 15(3):283-292, 1996. } \author{ Martyn Plummer, \email{plummer@iarc.fr} } \seealso{ \code{\link{Icens}} \code{\link{fit.mult}} } \examples{ data( HIV.dk ) } \keyword{ models } \keyword{ regression } \keyword{ survival } Epi/man/N2Y.Rd0000644000175100001440000000427412531361107012465 0ustar hornikusers\name{N2Y} \alias{N2Y} \title{ Create risk time (Person-Years) in Lexis triangles from population data. } \description{ Data on population size at equidistant dates and age-classes are used to estimate person-time at risk in Lexis-triangles, i.e. classes classified by age, period AND cohort. } \usage{ N2Y( A, P, N, data = NULL, return.dfr = TRUE) } \arguments{ \item{A}{Name of the age-variable, which should be numeric, corresponding to the left endpoints of the age intervals.} \item{P}{Name of the period-variable, which should be numeric, corresponding to the date of population count.} \item{N}{The population size at date \code{P} in age class \code{A}.} \item{data}{A data frame in which arguments are interpreted.} \item{return.dfr}{Logical. Should the results be returned as a data frame (default \code{TRUE}) or as a table.} } \details{The calculation of the risk time from the population figures is done as described in: B. Carstensen: Age-Period-Cohort models for the Lexis diagram. Statistics in Medicine, 26: 3018-3045, 2007. } \value{A data frame with variables \code{A}, \code{P} and \code{Y}, representing the mean age and period in the Lexis triangles and the person-time in them. If \code{res.dfr=FALSE} a three-way table classified by the left end point of the age-classes and the periods and a factor \code{wh} taking the values \code{up} and \code{lo} corresponding to upper (early cohort) and lower (late cohort) Lexis triangles. } \references{ B. Carstensen: Age-Period-Cohort models for the Lexis diagram. Statistics in Medicine, 26: 3018-3045, 2007. } \author{ Bendix Carstensen, \url{BendixCarstensen.com} } \seealso{ \code{\link{splitLexis}}, \code{\link{apc.fit}} } \examples{ # Danish population at 1 Jan each year by sex and age data( N.dk ) # An illustrative subset ( Nx <- subset( N.dk, sex==1 & A<5 & P<1975 ) ) # Show the data in tabular form xtabs( N ~ A + P, data=Nx ) # Lexis triangles as data frame Nt <- N2Y( data=Nx, return.dfr=TRUE ) xtabs( Y ~ round(A,2) + round(P,2), data=Nt ) # Lexis triangles as a 3-dim array ftable( N2Y( data=Nx, return.dfr=FALSE ) ) } \keyword{Data} Epi/man/plotevent.rd0000644000175100001440000000216412531361107014131 0ustar hornikusers\name{plotevent} \alias{plotevent} \title{ Plot Equivalence Classes } \description{ For interval censored data, segments of times between last.well and first.ill are plotted for each conversion in the data. It also plots the equivalence classes. } \usage{ plotevent(last.well, first.ill, data) } \arguments{ \item{last.well}{ Time at which the individuals are last seen negative for the event } \item{first.ill}{ Time at which the individuals are first seen positive for the event } \item{data}{ Data with a transversal shape } } \details{ last.well and first.ill should be written as character in the function. } \value{ Graph } \references{ Carstensen B. Regression models for interval censored survival data: application to HIV infection in Danish homosexual men.Stat Med. 1996 Oct 30;15(20):2177-89. Lindsey JC, Ryan LM. Tutorial in biostatistics methods for interval-censored data.Stat Med. 1998 Jan 30;17(2):219-38. } \author{ Delphine Maucort-Boulch, Bendix Carstensen, Martyn Plummer } \seealso{ \code{\link{Icens}} } % \examples{ % } \keyword{ models } \keyword{ regression } \keyword{ survival } Epi/man/Life.lines.Rd0000644000175100001440000000404012531361107014034 0ustar hornikusers\name{Life.lines} \alias{Life.lines} \title{ Compute dates/ages for life lines in a Lexis diagram } \description{ Fills out the missing information for follow up of persons in a Lexis diagram if sufficient information is given. } \usage{ Life.lines( entry.date = NA, exit.date = NA, birth.date = NA, entry.age = NA, exit.age = NA, risk.time = NA ) } \arguments{ \item{entry.date, exit.date,birth.date, entry.age, exit.age, risk.time}{Vectors defining lifelines to be plotted in the diagram. At least three must be given to produce a result. Not all subsets of three will suffice, the given subset has to define life lines. If insufficient data is given, nothing is returned and a warning is given.} } \value{ Data frame with variables \code{entry.date}, \code{entry.age}, \code{exit.date}, \code{exit.age}, \code{risk.time}, \code{birth.date}, with all entries computed for each person. If any of \code{entry.date}, \code{exit.date} or \code{birth.date} are of class \code{Date} or if any of \code{entry.age}, \code{exit.age} or \code{risk.time} are of class \code{difftime} the date variables will be of class \code{Date} and the other three of class \code{difftime}. } \examples{ ( Life.lines( entry.age = c(3,30,45), risk.time = c(25,5,14), birth.date = c(1970,1931,1925.7) ) ) # Draw a Lexis diagram Lexis.diagram() # Compute entry and exit age and date. ( LL <- Life.lines( entry.age = c(3,30,45), risk.time = c(25,5,14), birth.date = c(1970,1931,1925.7) ) ) segments( LL[,1], LL[,2], LL[,3], LL[,4] ) # Plot the life lines. # Compute entry and exit age and date, supplying a date variable bd <- ( c(1970,1931,1925.7) - 1970 ) * 365.25 class( bd ) <- "Date" ( Life.lines( entry.age = c(3,30,45), risk.time = c(25,5,14), birth.date = bd ) ) } \seealso{ \code{\link{Lexis.diagram}}, \code{\link{Lexis.lines}} } \keyword{ manip } \keyword{ dplot } Epi/man/float.Rd0000644000175100001440000000726412531361107013164 0ustar hornikusers\name{float} \alias{float} \alias{print.floated} \title{Calculate floated variances} \description{ Given a fitted model object, the \code{float()} function calculates floating variances (a.k.a. quasi-variances) for a given factor in the model. } \usage{ float(object, factor, iter.max=50) } \arguments{ \item{object}{a fitted model object} \item{factor}{character string giving the name of the factor of interest. If this is not given, the first factor in the model is used.} \item{iter.max}{Maximum number of iterations for EM algorithm} } \details{ The \code{float()} function implements the "floating absolute risk" proposal of Easton, Peto and Babiker (1992). This is an alternative way of presenting parameter estimates for factors in regression models, which avoids some of the difficulties of treatment contrasts. It was originally designed for epidemiological studies of relative risk, but the idea is widely applicable. Treatment contrasts are not orthogonal. Consequently, the variances of treatment contrast estimates may be inflated by a poor choice of reference level, and the correlations between them may also be high. The \code{float()} function associates each level of the factor with a "floating" variance (or quasi-variance), including the reference level. Floating variances are not real variances, but they can be used to calculate the variance error of contrast by treating each level as independent. Plummer (2003) showed that floating variances can be derived from a covariance structure model applied to the variance-covariance matrix of the contrast estimates. This model can be fitted by minimizing the Kullback-Leibler information divergence between the true distribution of the parameter estimates and the simplified distribution given by the covariance structure model. Fitting is done using the EM algorithm. In order to check the goodness-of-fit of the floating variance model, the \code{float()} function compares the standard errors predicted by the model with the standard errors derived from the true variance-covariance matrix of the parameter contrasts. The maximum and minimum ratios between true and model-based standard errors are calculated over all possible contrasts. These should be within 5 percent, or the use of the floating variances may lead to invalid confidence intervals. } \value{ An object of class \code{floated}. This is a list with the following components \item{coef}{A vector of coefficients. These are the same as the treatment contrasts but the reference level is present with coefficient 0.} \item{var}{A vector of floating (or quasi-) variances} \item{limits}{The bounds on the accuracy of standard errors over all possible contrasts} } \note{ Menezes(1999) and Firth and Menezes (2004) take a slightly different approach to this problem, using a pseudo-likelihood approach to fit the quasi-variance model. Their work is implemented in the package qvcalc. } \references{ Easton DF, Peto J and Babiker GAG (1991) Floating absolute risk: An alternative to relative risk in survival and case control analysis avoiding an arbitrary reference group. \emph{Statistics in Medicine}, \bold{10}, 1025-1035. Firth D and Mezezes RX (2004) Quasi-variances. \emph{Biometrika} \bold{91}, 65-80. Menezes RX(1999) More useful standard errors for group and factor effects in generalized linear models. \emph{D.Phil. Thesis}, Department of Statistics, University of Oxford. Plummer M (2003) Improved estimates of floating absolute risk, \emph{Statistics in Medicine}, \bold{23}, 93-104. } \author{Martyn Plummer} \seealso{\code{\link{ftrend}}, \code{qvcalc}} \keyword{regression} Epi/man/gmortDK.Rd0000644000175100001440000000303112531361107013412 0ustar hornikusers\name{gmortDK} \alias{gmortDK} \docType{data} \title{Population mortality rates for Denmark in 5-years age groups.} \description{ The \code{gmortDK} data frame has 418 rows and 21 columns. } \format{ This data frame contains the following columns: \tabular{rl}{ \code{agr}: \tab Age group, 0:0--4, 5:5--9,..., 90:90+. \cr \code{per}: \tab Calendar period, 38: 1938--42, 43: 1943--47, ..., 88:1988-92. \cr \code{sex}: \tab Sex, 1: male, 2: female. \cr \code{risk}: \tab Number of person-years in the Danish population. \cr \code{dt}: \tab Number of deaths. \cr \code{rt}: \tab Overall mortality rate in cases per 1000 person-years, i.e. \code{rt=1000*dt/risk} \cr \tab Cause-specific mortality rates in cases per 1000 person-years: \cr \code{r1}: \tab Infections \cr \code{r2}: \tab Cancer. \cr \code{r3}: \tab Tumors, benign, unspecific nature. \cr \code{r4}: \tab Endocrine, metabolic. \cr \code{r5}: \tab Blood. \cr \code{r6}: \tab Nervous system, psychiatric. \cr \code{r7}: \tab Cerebrovascular. \cr \code{r8}: \tab Cardiac. \cr \code{r9}: \tab Respiratory diseases, excl. cancer. \cr \code{r10}: \tab Liver, excl. cancer. \cr \code{r11}: \tab Digestive, other. \cr \code{r12}: \tab Genitourinary. \cr \code{r13}: \tab Ill-defined symptoms. \cr \code{r14}: \tab All other, natural. \cr \code{r15}: \tab Violent. \cr } } \source{ Statistics Denmark, National board of health provided original data. Michael Andersson grouped the causes of death. } \examples{ data(gmortDK) } \seealso{\code{\link{thoro}}, \code{\link{mortDK}}} \keyword{datasets} Epi/man/M.dk.Rd0000644000175100001440000000315112531361107012637 0ustar hornikusers\name{M.dk} \alias{M.dk} \docType{data} \title{Mortality in Denmark 1974 ff.} \description{ Mortality in one-year classes of age (0-98,99+) and period (1974 ff.) in Denmark. } \usage{data(M.dk)} \format{ A data frame with 6400 observations on the following 6 variables. \describe{ \item{\code{A}}{Age-class, 0-98, 99:99+} \item{\code{sex}}{Sex. 1:males, 2:females} \item{\code{P}}{Period (year) of death} \item{\code{D}}{Number of deaths} \item{\code{Y}}{Number of person-years} \item{\code{rate}}{Mortality rate per 1000 person-years} } } \details{ Deaths in ages over 100 are in the class labelled 99. Risk time is computed by tabulation of the risk time in \code{\link{Y.dk}}, except for the class 99+ where the average of the population size in ages 99+ at the first and last date of the year is used.} \source{ \url{http://www.statistikbanken.dk/statbank5a/SelectTable/omrade0.asp?SubjectCode=02&PLanguage=1&ShowNews=OFF} } \examples{ data(M.dk) str(M.dk) zz <- xtabs( rate ~ sex+A+P, data=M.dk ) zz[zz==0] <- NA # 0s makes log-scale plots crash par(mfrow=c(1,2), mar=c(0,0,0,0), oma=c(3,3,1,1), mgp=c(3,1,0)/1.6 ) for( i in 1:2 ) { matplot( dimnames(zz)[[2]], zz[i,,], lty=1, lwd=1, col=rev(heat.colors(37)), log="y", type="l", ylim=range(zz,na.rm=TRUE), ylab="", xlab="", yaxt="n" ) text( 0, max(zz,na.rm=TRUE), c("M","F")[i], font=2, adj=0:1, cex=2, col="gray" ) if( i==1 ) axis( side=2, las=1 ) } mtext( side=1, "Age", line=2, outer=TRUE ) mtext( side=2, "Mortality rate", line=2, outer=TRUE ) } \keyword{datasets} Epi/man/apc.fit.Rd0000644000175100001440000002331712531361107013400 0ustar hornikusers\name{apc.fit} \alias{apc.fit} \title{ Fit an Age-Period-Cohort model to tabular data. } \description{ Fits the classical five models to tabulated rate data (cases, person-years) classified by two of age, period, cohort: Age, Age-drift, Age-Period, Age-Cohort and Age-period. There are no assumptions about the age, period or cohort classes being of the same length, or that tabulation should be only by two of the variables. Only requires that mean age and period for each tabulation unit is given. } \usage{ apc.fit( data, A, P, D, Y, ref.c, ref.p, dist = c("poisson","binomial"), model = c("ns","bs","ls","factor"), dr.extr = c("weighted","Holford"), parm = c("ACP","APC","AdCP","AdPC","Ad-P-C","Ad-C-P","AC-P","AP-C"), npar = c( A=5, P=5, C=5 ), scale = 1, alpha = 0.05, print.AOV = TRUE ) } \arguments{ \item{data}{Data frame with (at least) variables, \code{A} (age), \code{P} (period), \code{D} (cases, deaths) and \code{Y} (person-years). Cohort (date of birth) is computed as \code{P-A}. If thsi argument is given the arguments \code{A}, \code{P}, \code{D} and \code{Y} are ignored.} \item{A}{Age; numerical vector with mean age at diagnosis for each unit.} \item{P}{Period; numerical vector with mean date of diagnosis for each unit.} \item{D}{Cases, deaths; numerical vector.} \item{Y}{Person-years; numerical vector. Also used as denominator for binomial data, see the \code{dist} argument.} \item{ref.c}{Reference cohort, numerical. Defaults to median date of birth among cases. If used with \code{parm="AdCP"} or \code{parm="AdPC"}, the resdiual cohort effects will be 1 at \code{ref.c}} \item{ref.p}{Reference period, numerical. Defaults to median date of diagnosis among cases.} \item{dist}{Distribution (or more precisely: Likelihood) used for modelling. if a binomial model us ised, \code{Y} is assuemd to be the denominator; \code{"binomial"} gives a binomial model with logit link.} \item{model}{Type of model fitted: \itemize{ \item \code{ns} fits a model with natural splines for each of the terms, with \code{npar} parameters for the terms. \item \code{bs} fits a model with B-splines for each of the terms, with \code{npar} parameters for the terms. \item \code{ls} fits a model with linear splines. \item \code{factor} fits a factor model with one parameter per value of \code{A}, \code{P} and \code{C}. \code{npar} is ignored in this case. } } \item{dr.extr}{Character. How the drift parameter should be extracted from the age-period-cohort model. \code{"weighted"} (default) lets the weighted average (by marginal no. cases, \code{D}) of the estimated period and cohort effects have 0 slope. \code{"Holford"} uses the naive average over all values for the estimated effects, disregarding the no. cases.} \item{parm}{Character. Indicates the parametrization of the effects. The first four refer to the ML-fit of the Age-Period-Cohort model, the last four give Age-effects from a smaller model and residuals relative to this. If one of the latter is chosen, the argument \code{dr.extr} is ignored. Possible values for \code{parm} are: \itemize{ \item \code{"ACP"}: ML-estimates. Age-effects as rates for the reference cohort. Cohort effects as RR relative to the reference cohort. Period effects constrained to be 0 on average with 0 slope. \item \code{"APC"}: ML-estimates. Age-effects as rates for the reference period. Period effects as RR relative to the reference period. Cohort effects constrained to be 0 on average with 0 slope. \item \code{"AdCP"}: ML-estimates. Age-effects as rates for the reference cohort. Cohort and period effects constrained to be 0 on average with 0 slope. These effects do not multiply to the fitted rates, the drift is missing and needs to be included to produce the fitted values. \item \code{"AdPC"}: ML-estimates. Age-effects as rates for the reference period. Cohort and period effects constrained to be 0 on average with 0 slope. These effects do not multiply to the fitted rates, the drift is missing and needs to be included to produce the fitted values. \item \code{"Ad-C-P"}: Age effects are rates for the reference cohort in the Age-drift model (cohort drift). Cohort effects are from the model with cohort alone, using log(fitted values) from the Age-drift model as offset. Period effects are from the model with period alone using log(fitted values) from the cohort model as offset. \item \code{"Ad-P-C"}: Age effects are rates for the reference period in the Age-drift model (period drift). Period effects are from the model with period alone, using log(fitted values) from the Age-drift model as offset. Cohort effects are from the model with cohort alone using log(fitted values) from the period model as offset. \item \code{"AC-P"}: Age effects are rates for the reference cohort in the Age-Cohort model, cohort effects are RR relative to the reference cohort. Period effects are from the model with period alone, using log(fitted values) from the Age-Cohort model as offset. \item \code{"AP-C"}: Age effects are rates for the reference period in the Age-Period model, period effects are RR relative to the reference period. Cohort effects are from the model with cohort alone, using log(fitted values) from the Age-Period model as offset. } } \item{npar}{The number of parameters/knots to use for each of the terms in the model. If it is vector of length 3, the numbers are taken as the no. of knots for Age, Period and Cohort, respctively. Unless it has a names attribute with vales "A", "P" and "C" in which case these will be used. The knots chosen are the quantiles \code{(1:nk+0.1)/(nk+0.2)} of the events (i.e. of \code{rep(A,D)}) \code{npar} may also be a named list of three numerical vectors with names "A", "P" and "C", in which case these taken as the knots for the age, period and cohort effect, the first and last element in each vector are used as the boundary knots.} \item{alpha}{The significance level. Estimates are given with (1-\code{alpha}) confidence limits.} \item{scale}{numeric(1), factor multiplied to the rate estimates before output.} \item{print.AOV}{Should the analysis of deviance table for the models be printed?} } \value{ An object of class "apc" (recognized by \code{\link{apc.plot}} and \code{\link{apc.lines}}) --- a list with components: \item{Type}{Text describing the model and parametrization returned} \item{Model}{The model object(s) on which the parametrization is based.} \item{Age}{Matrix with 4 colums: \code{A.pt} with the ages (equals \code{unique(A)}) and three columns giving the estimated rates with c.i.s.} \item{Per}{Matrix with 4 colums: \code{P.pt} with the dates of diagnosis (equals \code{unique(P)}) and three columns giving the estimated RRs with c.i.s.} \item{Coh}{Matrix with 4 colums: \code{C.pt} with the dates of birth (equals \code{unique(P-A)}) and three columns giving the estimated RRs with c.i.s.} \item{Drift}{A 3 column matrix with drift-estimates and c.i.s: The first row is the ML-estimate of the drift (as defined by \code{drift}), the second row is the estimate from the Age-drift model. For the sequential parametrizations, only the latter is given.} \item{Ref}{Numerical vector of length 2 with reference period and cohort. If ref.p or ref.c was not supplied the corresponding element is NA.} \item{AOV}{Analysis of deviance table comparing the five classical models.} \item{Type}{Character string explaining the model and the parametrization.} \item{Knots}{If \code{model} is one of \code{"ns"} or \code{"bs"}, a list with three components: \code{Age}, \code{Per}, \code{Coh}, each one a vector of knots. The max and the min are the boundary knots.} } \references{ The considerations behind the parametrizations used in this function are given in details in a preprint from Department of Biostatistics in Copenhagen: "Demography and epidemiology: Age-Period-Cohort models in the computer age", \url{http://biostat.ku.dk/reports/2006/ResearchReport06-1.pdf/}, later published as: B. Carstensen: Age-period-cohort models for the Lexis diagram. Statistics in Medicine, 10; 26(15):3018-45, 2007. } \author{ Bendix Carstensen, \url{http://BendixCarstensen.com} } \seealso{ \code{\link{apc.frame}}, \code{\link{apc.lines}}, \code{\link{apc.plot}}. } \examples{ library( Epi ) data(lungDK) # Taylor a dataframe that meets the requirements exd <- lungDK[,c("Ax","Px","D","Y")] names(exd)[1:2] <- c("A","P") # Two different ways of parametrizing the APC-model, ML ex.H <- apc.fit( exd, npar=7, model="ns", dr.extr="Holford", parm="ACP", scale=10^5 ) ex.W <- apc.fit( exd, npar=7, model="ns", dr.extr="weighted", parm="ACP", scale=10^5 ) # Sequential fit, first AC, then P given AC. ex.S <- apc.fit( exd, npar=7, model="ns", parm="AC-P", scale=10^5 ) # Show the estimated drifts ex.H[["Drift"]] ex.W[["Drift"]] ex.S[["Drift"]] # Plot the effects fp <- apc.plot( ex.H ) apc.lines( ex.W, frame.par=fp, col="red" ) apc.lines( ex.S, frame.par=fp, col="blue" ) } \keyword{models} \keyword{regression}Epi/man/twoby2.Rd0000644000175100001440000000352412531361107013300 0ustar hornikusers\name{twoby2} \alias{twoby2} \title{Analysis of a two by two table} \description{ Computes the usual measures of association in a 2 by 2 table with confidence intervals. Also produces asymtotic and exact tests. Assumes that comparison of probability of the first column level between levels of the row variable is of interest. Output requires that the input matrix has meaningful row and column labels. } \usage{ twoby2(exposure, outcome, alpha = 0.05, print = TRUE, dec = 4, conf.level = 1-alpha, F.lim = 10000) } \arguments{ \item{exposure}{If a table the analysis is based on the first two rows and first two columns of this. If a variable, this variable is tabulated against} \item{outcome}{as the second variable} \item{alpha}{Significance level} \item{print}{Should the results be printed?} \item{dec}{Number of decimals in the printout.} \item{conf.level}{1-\code{alpha}} \item{F.lim}{If the table total exceeds \code{F.lim}, Fisher's exact test is not computed} } \value{A list with elements: \item{table}{The analysed 2 x 2 table augmented with probabilities and confidence intervals. The confidence intervals for the probabilities are computed using the normal approximation to the log-odds. Confidence intervals for the difference of proportions are computed using method 10 from Newcombe, Stat.Med. 1998, 17, pp.873 ff.} \item{measures}{A table of Odds-ratios and relative risk with confidence intervals.} \item{p.value}{Exact p-value for the null hypothesis of OR=1} } \author{ Mark Myatt. Modified by Bendix Carstensen. } \examples{ Treat <- sample(c("A","B"), 50, rep=TRUE ) Resp <- c("Yes","No")[1+rbinom(50,1,0.3+0.2*(Treat=="A"))] twoby2( Treat, Resp ) twoby2( table( Treat, Resp )[,2:1] ) # Comparison the other way round } \keyword{univar} \keyword{htest} Epi/man/Ns.Rd0000644000175100001440000001006712562623763012447 0ustar hornikusers\name{Ns} \alias{Ns} \title{ Natural splines - (cubic splines linear beyond outermost knots) with convenient specification of knots and possibility of centering and detrending. } \description{ This function is partly for convenient specification of natural splines in practical modelling. The convention used is to take the smallest and the largest of the supplied knots as boundary knots. It also has the option of centering the effects provided at a chosen reference point as well as projecting the columns on the orthogonal space to that spanned by the intercept and the linear effect of the variable. } \usage{ Ns( x, ref = NULL, df = NULL, knots = NULL, intercept = FALSE, Boundary.knots = NULL, detrend = FALSE ) } \arguments{ \item{x}{A variable.} \item{ref}{Scalar. Reference point on the \code{x}-scale, where the resuting effect will be 0.} \item{df}{degrees of freedom.} \item{knots}{knots to be used both as boundary and internal knots. If \code{Boundary.knots} are given, this will be taken as the set of internal knots.} \item{intercept}{Should the intercept be included in the resulting basis? Ignored if any of \code{ref} or \code{detrend} is given.} \item{Boundary.knots}{The boundary knots beyond which the spline is linear.} \item{detrend}{If \code{TRUE}, the columns of the spline basis will be projected to the orthogonal of \code{cbind(1,x)}. Optionally \code{detrend} can be given as a vector of non-negative numbers used to define an inner product as \code{diag(detrend)} for projection on the orthogonal to \code{cbind(1,x)}. The default is projection w.r.t. the inner product defined by the identity matrix.} } \value{ A matrix of dimension c(length(x),df) where either \code{df} was supplied or if \code{knots} were supplied, \code{df = length(knots) - intercept}. \code{Ns} returns a spline basis which is centered at \code{ref}, without the \code{ref=} argument \code{Ns} behaves as \code{Ns}, and so the two will likely be merged. \code{Ns} returns a spline basis which is orthogonal to \code{cbind(1,x)} with respect to the inner product defined by the positive definite matrix \code{diag(weight)} (an assumption which is checked). } \author{ Bendix Carstensen } \note{ The need for this function is priarily from analysis of rates in epidemiology and demography, where the dataset are time-split records of follow-up, and the range of data therefore rarely is of any interest (let alone is meaningful). In Poisson modelling of rates based on time-split records one should aim at having the same number of \emph{events} between knots, rather than the same number of observations. } \examples{ require(splines) require(stats) require(graphics) ns( women$height, df = 3) Ns( women$height, knots=c(63,59,71,67) ) # Gives the same results as ns: summary( lm(weight ~ ns(height, df = 3), data = women) ) summary( lm(weight ~ Ns(height, df = 3), data = women) ) # Get the diabetes data and set up as Lexis object data(DMlate) DMlate <- DMlate[sample(1:nrow(DMlate),500),] dml <- Lexis( entry = list(Per=dodm, Age=dodm-dobth, DMdur=0 ), exit = list(Per=dox), exit.status = factor(!is.na(dodth),labels=c("DM","Dead")), data = DMlate ) # Split in 1-year age intervals dms <- splitLexis( dml, time.scale="Age", breaks=0:100 ) summary( dms ) # Model age-specific rates using Ns with 6 knots n.kn <- 6 ( a.kn <- with( subset(dms,lex.Xst=="Dead"), quantile( Age+lex.dur, probs=(1:n.kn-0.5)/n.kn ) ) ) m1 <- glm( lex.Xst=="Dead" ~ Ns( Age, kn=a.kn ), offset = log( lex.dur ), family=poisson, data=dms ) # Plot estimated curve and knots chosen: nd <- data.frame(Age=40:90,lex.dur=1000) matplot( nd$Age, ci.pred( m1, newdata=nd ), type="l", lwd=c(3,1,1), lty=1, col="black", log="y", ylab="Mortality rates per 1000 PY", xlab="Age (years)", las=1 ) rug( a.kn, lwd=2 ) } \keyword{regression} Epi/man/apc.frame.Rd0000644000175100001440000001135112531361107013703 0ustar hornikusers\name{apc.frame} \alias{apc.frame} \title{ Produce an empty frame for display of parameter-estimates from Age-Period-Cohort-models. } \description{ A plot is generated where both the age-scale and the cohort/period scale is on the x-axis. The left vertical axis will be a logarithmic rate scale referring to age-effects and the right a logarithmic rate-ratio scale of the same relative extent as the left referring to the cohort and period effects (rate ratios). Only an empty plot frame is generated. Curves or points must be added with \code{points}, \code{lines} or the special utility function \code{\link{apc.lines}}. } \usage{ apc.frame( a.lab, cp.lab, r.lab, rr.lab = r.lab / rr.ref, rr.ref = r.lab[length(r.lab)/2], a.tic = a.lab, cp.tic = cp.lab, r.tic = r.lab, rr.tic = r.tic / rr.ref, tic.fac = 1.3, a.txt = "Age", cp.txt = "Calendar time", r.txt = "Rate per 100,000 person-years", rr.txt = "Rate ratio", ref.line = TRUE, gap = diff(range(c(a.lab, a.tic)))/10, col.grid = gray(0.85), sides = c(1,2,4) ) } \arguments{ \item{a.lab}{Numerical vector of labels for the age-axis.} \item{cp.lab}{Numerical vector of labels for the cohort-period axis.} \item{r.lab}{Numerical vector of labels for the rate-axis (left vertical)} \item{rr.lab}{Numerical vector of labels for the RR-axis (right vertical)} \item{rr.ref}{At what level of the rate scale is the RR=1 to be.} \item{a.tic}{Location of additional tick marks on the age-scale} \item{cp.tic}{Location of additional tick marks on the cohort-period-scale} \item{r.tic}{Location of additional tick marks on the rate-scale} \item{rr.tic}{Location of additional tick marks on the RR-axis.} \item{tic.fac}{Factor with which to diminish intermediate tick marks} \item{a.txt}{Text for the age-axis (left part of horizontal axis).} \item{cp.txt}{Text for the cohort/period axis (right part of horizontal axis).} \item{r.txt}{Text for the rate axis (left vertical axis).} \item{rr.txt}{Text for the rate-ratio axis (right vertical axis)} \item{ref.line}{Logical. Should a reference line at RR=1 be drawn at the calendar time part of the plot?} \item{gap}{Gap between the age-scale and the cohort-period scale} \item{col.grid}{Colour of the grid put in the plot.} \item{sides}{Numerical vector indicating on which sides axes should be drawn and annotated. This option is aimed for multi-panel displays where axes only are put on the outer plots.} } \details{ The function produces an empty plot frame for display of results from an age-period-cohort model, with age-specific rates in the left side of the frame and cohort and period rate-ratio parameters in the right side of the frame. There is a gap of \code{gap} between the age-axis and the calendar time axis, vertical grid lines at \code{c(a.lab,a.tic,cp.lab,cp.tic)}, and horizontal grid lines at \code{c(r.lab,r.tic)}. The function returns a numerical vector of length 2, with names \code{c("cp.offset","RR.fac")}. The y-axis for the plot will be a rate scale for the age-effects, and the x-axis will be the age-scale. The cohort and period effects are plotted by subtracting the first element (named \code{"cp.offset"}) of the returned result form the cohort/period, and multiplying the rate-ratios by the second element of the returned result (named \code{"RR.fac"}). } \value{ A numerical vector of length two, with names \code{c("cp.offset","RR.fac")}. The first is the offset for the cohort period-axis, the second the multiplication factor for the rate-ratio scale. Side-effect: A plot with axes and grid lines but no points or curves. Moreover, the option \code{apc.frame.par} is given the value \code{c("cp.offset","RR.fac")}, which is recognized by \code{\link{apc.plot}} and \code{\link{apc.lines}}. } \references{ B. Carstensen: Age-Period-Cohort models for the Lexis diagram. Statistics in Medicine, 26: 3018-3045, 2007. } \author{ Bendix Carstensen, Steno Diabetes Center, \url{http://BendixCarstensen.com} } \examples{ par( mar=c(4,4,1,4) ) res <- apc.frame( a.lab=seq(30,90,20), cp.lab=seq(1880,2000,30), r.lab=c(1,2,5,10,20,50), a.tic=seq(30,90,10), cp.tic=seq(1880,2000,10), r.tic=c(1:10,1:5*10), gap=27 ) res # What are the axes actually? par(c("usr","xlog","ylog")) # How to plot in the age-part: a point at (50,10) points( 50, 10, pch=16, cex=2, col="blue" ) # How to plot in the cohort-period-part: a point at (1960,0.3) points( 1960-res[1], 0.3*res[2], pch=16, cex=2, col="red" ) } \seealso{ \code{\link{apc.lines},\link{apc.fit}} } \keyword{hplot} Epi/man/mortDK.Rd0000644000175100001440000000301212531361107013242 0ustar hornikusers\name{mortDK} \alias{mortDK} \docType{data} \title{Population mortality rates for Denmark in 1-year age-classes.} \description{ The \code{mortDK} data frame has 1820 rows and 21 columns. } \format{ This data frame contains the following columns: \tabular{rl}{ \code{age}: \tab Age class, 0--89, 90:90+. \cr \code{per}: \tab Calendar period, 38: 1938--42, 43: 1943--47, ..., 88:1988-92. \cr \code{sex}: \tab Sex, 1: male, 2: female. \cr \code{risk}: \tab Number of person-years in the Danish population. \cr \code{dt}: \tab Number of deaths. \cr \code{rt}: \tab Overall mortality rate in cases per 1000 person-years, i.e. \code{rt=1000*dt/risk} \cr \tab Cause-specific mortality rates in cases per 1000 person-years: \cr \code{r1}: \tab Infections \cr \code{r2}: \tab Cancer. \cr \code{r3}: \tab Tumors, benign, unspecific nature. \cr \code{r4}: \tab Endocrine, metabolic. \cr \code{r5}: \tab Blood. \cr \code{r6}: \tab Nervous system, psychiatric. \cr \code{r7}: \tab Cerebrovascular. \cr \code{r8}: \tab Cardiac. \cr \code{r9}: \tab Respiratory diseases, excl. cancer. \cr \code{r10}: \tab Liver, excl. cancer. \cr \code{r11}: \tab Digestive, other. \cr \code{r12}: \tab Genitourinary. \cr \code{r13}: \tab Ill-defined symptoms. \cr \code{r14}: \tab All other, natural. \cr \code{r15}: \tab Violent. \cr } } \source{ Statistics Denmark, National board of health provided original data. Michael Andersson grouped the causes of death. } \examples{ data(mortDK) } \seealso{\code{\link{thoro}}, \code{\link{gmortDK}}} \keyword{datasets} Epi/man/NArray.Rd0000644000175100001440000000207412531361107013245 0ustar hornikusers\name{NArray} \alias{NArray} \alias{ZArray} \title{Set up an array of NAs, solely from the list of dimnames } \description{Defines an array of NAs, solely from the list of dimnames } \usage{ NArray( x, cells=NA ) ZArray( x, cells=0 ) } \arguments{ \item{x}{A (possibly named) list to be used as dimnames for the resulting array} \item{cells}{Value(s) to fill the array} } \details{This is a simple useful way of defining arrays to be used for collection of results. The point is that everything is defined from the named list, so in the process of defining what you want to collect, there is only one place in the program to edit. It's just a wrapper for \code{array}. \code{ZArray} is just a wrapper for \code{NArray} with a different default. } \value{An array with \code{dimnames} attribute \code{x}, and all values equal to \code{cells}. } \author{Bendix Carstensen } \examples{ ftable( NArray( list(Aye = c("Yes", "Si", "Oui"), Bee = c("Hum", "Buzz"), Sea = c("White", "Black", "Red", "Dead") ) ) ) }Epi/man/rateplot.Rd0000644000175100001440000001723012531361107013703 0ustar hornikusers\name{rateplot} \alias{rateplot} \alias{Aplot} \alias{Pplot} \alias{Cplot} \title{ Functions to plot rates from a table classified by age and calendar time (period) } \description{ Produces plots of rates versus age, connected within period or cohort (\code{Aplot}), rates versus period connected within age-groups (\code{Pplot}) and rates and rates versus date of birth cohort (\code{Cplot}). \code{rateplot} is a wrapper for these, allowing to produce the four classical displays with a single call. } \usage{ rateplot( rates, which = c("ap","ac","pa","ca"), age = as.numeric( dimnames( rates )[[1]] ), per = as.numeric( dimnames( rates )[[2]] ), grid = FALSE, a.grid = grid, p.grid = grid, c.grid = grid, ygrid = grid, col.grid = gray( 0.9 ), a.lim = range( age, na.rm=TRUE ) + c(0,diff( range( age ) )/30), p.lim = range( per, na.rm=TRUE ) + c(0,diff( range( age ) )/30), c.lim = NULL, ylim = range( rates[rates>0], na.rm=TRUE ), at = NULL, labels = paste( at ), a.lab = "Age at diagnosis", p.lab = "Date of diagnosis", c.lab = "Date of birth", ylab = "Rates", type = "l", lwd = 2, lty = 1, log.ax = "y", las = 1, ann = FALSE, a.ann = ann, p.ann = ann, c.ann = ann, xannx = 1/20, cex.ann = 0.8, a.thin = seq( 1, length( age ), 2 ), p.thin = seq( 1, length( per ), 2 ), c.thin = seq( 2, length( age ) + length( per ) - 1, 2 ), col = par( "fg" ), a.col = col, p.col = col, c.col = col, ... ) Aplot( rates, age = as.numeric( dimnames( rates )[[1]] ), per = as.numeric( dimnames( rates )[[2]] ), grid = FALSE, a.grid = grid, ygrid = grid, col.grid = gray( 0.9 ), a.lim = range( age, na.rm=TRUE ), ylim = range( rates[rates>0], na.rm=TRUE ), at = NULL, labels = paste( at ), a.lab = names( dimnames( rates ) )[1], ylab = deparse( substitute( rates ) ), type = "l", lwd = 2, lty = 1, col = par( "fg" ), log.ax = "y", las = 1, c.col = col, p.col = col, c.ann = FALSE, p.ann = FALSE, xannx = 1/20, cex.ann = 0.8, c.thin = seq( 2, length( age ) + length( per ) - 1, 2 ), p.thin = seq( 1, length( per ), 2 ), p.lines = TRUE, c.lines = !p.lines, ... ) Pplot( rates, age = as.numeric( dimnames( rates )[[1]] ), per = as.numeric( dimnames( rates )[[2]] ), grid = FALSE, p.grid = grid, ygrid = grid, col.grid = gray( 0.9 ), p.lim = range( per, na.rm=TRUE ) + c(0,diff(range(per))/30), ylim = range( rates[rates>0], na.rm=TRUE ), p.lab = names( dimnames( rates ) )[2], ylab = deparse( substitute( rates ) ), at = NULL, labels = paste( at ), type = "l", lwd = 2, lty = 1, col = par( "fg" ), log.ax = "y", las = 1, ann = FALSE, cex.ann = 0.8, xannx = 1/20, a.thin = seq( 1, length( age ), 2 ), ... ) Cplot( rates, age = as.numeric( rownames( rates ) ), per = as.numeric( colnames( rates ) ), grid = FALSE, c.grid = grid, ygrid = grid, col.grid = gray( 0.9 ), c.lim = NULL, ylim = range( rates[rates>0], na.rm=TRUE ), at = NULL, labels = paste( at ), c.lab = names( dimnames( rates ) )[2], ylab = deparse( substitute( rates ) ), type = "l", lwd = 2, lty = 1, col = par( "fg" ), log.ax = "y", las = 1, xannx = 1/20, ann = FALSE, cex.ann = 0.8, a.thin = seq( 1, length( age ), 2 ), ... ) } \arguments{ \item{rates}{A two-dimensional table (or array) with rates to be plotted. It is assumed that the first dimension is age and the second is period.} \item{which}{A character vector with elements from \code{c("ap","ac","apc","pa","ca")}, indication which plots should be produced. One plot per element is produced. The first letter indicates the x-axis of the plot, the remaining which groups should be connected, i.e. \code{"pa"} will plot rates versus period and connect age-classes, and \code{"apc"} will plot rates versus age, and connect both periods and cohorts.} \item{age}{Numerical vector giving the means of the age-classes. Defaults to the rownames of \code{rates} as numeric.} \item{per}{Numerical vector giving the means of the periods. Defaults to the columnnames of \code{rates} as numeric.} \item{grid}{Logical indicating whether a background grid should be drawn.} \item{a.grid}{Logical indicating whether a background grid on the age-axis should be drawn. If numerical it indicates the age-coordinates of the grid.} \item{p.grid}{do. for the period.} \item{c.grid}{do. for the cohort.} \item{ygrid}{do. for the rate-dimension.} \item{col.grid}{The colour of the grid.} \item{a.lim}{Range for the age-axis.} \item{p.lim}{Range for the period-axis.} \item{c.lim}{Range for the cohort-axis.} \item{ylim}{Range for the y-axis (rates).} \item{at}{Position of labels on the y-axis (rates).} \item{labels}{Labels to put on the y-axis (rates).} \item{a.lab}{Text on the age-axis. Defaults to "Age".} \item{p.lab}{Text on the period-axis. Defaults to "Date of diagnosis".} \item{c.lab}{Text on the cohort-axis. Defaults to "Date of birth".} \item{ylab}{Text on the rate-axis. Defaults to the name of the rate-table.} \item{type}{How should the curves be plotted. Defaults to \code{"l"}.} \item{lwd}{Width of the lines. Defaults to 2.} \item{lty}{Which type of lines should be used. Defaults to 1, a solid line.} \item{log.ax}{Character with letters from \code{"apcyr"}, indicating which axes should be logarithmic. \code{"y"} and \code{"r"} both refer to the rate scale. Defaults to \code{"y"}.} \item{las}{see \code{par}.} \item{ann}{Should the curves be annotated?} \item{a.ann}{Logical indicating whether age-curves should be annotated.} \item{p.ann}{do. for period-curves.} \item{c.ann}{do. for cohort-curves.} \item{xannx}{The fraction that the x-axis is expanded when curves are annotated.} \item{cex.ann}{Expansion factor for characters annotating curves.} \item{a.thin}{Vector of integers indicating which of the age-classes should be labelled.} \item{p.thin}{do. for the periods.} \item{c.thin}{do. for the cohorts.} \item{col}{Colours for the curves.} \item{a.col}{Colours for the age-curves.} \item{p.col}{do. for the period-curves.} \item{c.col}{do. for the cohort-curves.} \item{p.lines}{Should rates from the same period be connected?} \item{c.lines}{Should rates from the same cohort be connected?} \item{...}{Additional arguments pssed on to \code{matlines} when plotting the curves.} } \details{ Zero values of the rates are ignored. They are neiter in the plot nor in the calculation of the axis ranges. } \value{ \code{NULL}. The function is used for its side-effect, the plot. } \author{ Bendix Carstensen, Steno Diabetes Center, \url{http://BendixCarstensen.com} } \seealso{ \code{\link{apc.frame}} } \examples{ data( blcaIT ) attach(blcaIT) # Table of rates: bl.rate <- tapply( D, list(age,period), sum ) / tapply( Y, list(age,period), sum ) bl.rate # The four classical plots: par( mfrow=c(2,2) ) rateplot( bl.rate*10^6 ) # The labels on the vertical axis could be nicer: rateplot( bl.rate*10^6, at=10^(-1:3), labels=c(0.1,1,10,100,1000) ) # More bells an whistles par( mfrow=c(1,3), mar=c(3,3,1,1), oma=c(0,3,0,0), mgp=c(3,1,0)/1.6 ) rateplot( bl.rate*10^6, ylab="", ann=TRUE, which=c("AC","PA","CA"), at=10^(-1:3), labels=c(0.1,1,10,100,1000), col=topo.colors(11), cex.ann=1.2 ) } \keyword{hplot} Epi/man/contr.cum.Rd0000644000175100001440000000306212531361107013757 0ustar hornikusers\name{contr.cum} \alias{contr.cum} \alias{contr.2nd} \alias{contr.diff} \alias{contr.orth} \title{ Contrast matrices } \description{ Return a matrix of contrasts for factor coding. } \usage{ contr.cum(n) contr.diff(n) contr.2nd(n) contr.orth(n) } \arguments{ \item{n}{A vector of levels for a factor, or the number of levels.} } \details{ These functions are used for creating contrast matrices for use in fitting regression models. The columns of the resulting matrices contain contrasts which can be used for coding a factor with \code{n} levels. \code{contr.cum} gives a coding corresponding to successive differences between factor levels. \code{contr.diff} gives a coding that correspond to the cumulative sum of the value for each level. This is not meaningful in a model where the intercept is included, therefore \code{n} columns ia always returned. \code{contr.2nd} gives contrasts corresponding to 2nd order differences between factor levels. Returns a matrix with \code{n-2} columns. \code{contr.orth} gives a matrix with \code{n-2} columns, which are mutually orthogonal and orthogonal to the matrix \code{cbind(1,1:n)} } \value{ A matrix with \code{n} rows and \code{k} columns, with \code{k}=\code{n} for \code{contr.diff} \code{k}=\code{n-1} for \code{contr.cum} \code{k}=\code{n-2} for \code{contr.2nd} and \code{contr.orth}. } \author{Bendix Carstensen} \seealso{ \code{\link{contr.treatment}} } \examples{ contr.cum(6) contr.2nd(6) contr.diff(6) contr.orth(6) } \keyword{design} \keyword{models} Epi/man/summary.Lexis.rd0000644000175100001440000000472712531361107014700 0ustar hornikusers\name{summary.Lexis} \alias{summary.Lexis} \alias{print.summary.Lexis} \title{ Summarize transitions and risk time from a Lexis object } \description{ A two-way table of records and transitions classified by states (\code{lex.Cst} and \code{lex.Xst}), as well the risk time in each state. } \usage{ \method{summary}{Lexis}( object, simplify=TRUE, scale=1, by=NULL, Rates=FALSE, ... ) \method{print}{summary.Lexis}( x, ..., digits=2 ) } \arguments{ \item{object}{A Lexis object.} \item{simplify}{Should rows with 0 follow-up time be dropped?} \item{scale}{Scaling factor for the rates. The calculated rates are multiplied by this number.} \item{by}{Character vector of name(s) of variable(s) in \code{object}. Used to give a separate summaries for subsets of \code{object}. If longer than than 1, the interaction between that variables is used to stratify the summary. It is also possible to supply a vector of length \code{nrow(object)}, and the distinct values of this will be used to stratify the summary.} \item{Rates}{Should a component with transition rates be returned (and printed) too?} \item{x}{A \code{summary.Lexis} object.} \item{digits}{How many digits should be used for printing?} \item{ ... }{Other parameters - ignored} } \value{ An object of class \code{summary.Lexis}, a list with two components, \code{Transitions} and \code{Rates}, each one a matrix with rows classified by states where persons spent time, and columns classified by states to which persons transit. The \code{Transitions} contains number of transitions and has 4 extra columns with number of records, total number of events, total risk time and number of person contributing attached. The \code{Rates} contains the transitions rates. If the argument \code{Rates} is FALSE (the default), then only the first component of the list is returned. } \author{Bendix Carstensen, \email{bxc@steno.dk}} \examples{ data( nickel ) # Lung cancer deaths and other deaths are coded 1 and 2 nic <- Lexis( data=nickel, entry=list(age=agein), exit=list(age=ageout,cal=ageout+dob,tfh=ageout-age1st), exit.status=factor( (icd > 0) + (icd \%in\% c(162,163)), labels=c("Alive","Other","Lung") ) ) str( nic ) head( nic ) summary( nic ) # More detailed summary, by exposure level summary( nic, by=nic$exposure>5, Rates=TRUE, scale=100 ) } \keyword{survival}Epi/man/DMconv.Rd0000644000175100001440000000217612531361107013242 0ustar hornikusers\name{DMconv} \alias{DMconv} \docType{data} \title{Conversion to diabetes} \description{ Data from a randomized intervention study ("Addition") where persons with prediabetic conditions are followed up for conversion to diabetes (DM). Conversion dates are interval censored. Original data are not published yet, so id-numbers have been changed and all dates have been randomly perturbed. } \usage{data(DMconv)} \format{ A data frame with 1519 observations on the following 6 variables. \describe{ \item{\code{id}}{Person identifier} \item{\code{doe}}{Date of entry, i.e. first visit.} \item{\code{dlw}}{Date last seen well, i.e. last visit without DM.} \item{\code{dfi}}{Date first seen ill, i.e. first visit with DM.} \item{\code{gtol}}{Glucose tolerance. Factor with levels: 1="IFG" (impaired fasting glucose), 2="IGT" (impaired glucose tolerance).} \item{\code{grp}}{Randomization. Factor with levels: 1="Intervention", 2="Control".} } } \source{ Signe Saetre Rasmussen, Steno Diabetes Center. The Addition Study. } \examples{ data(DMconv) str(DMconv) head(DMconv) } \keyword{datasets} Epi/man/merge.data.frame.Rd0000644000175100001440000000124612531361107015151 0ustar hornikusers\name{merge.data.frame} \alias{merge.data.frame} \title{Merge data frame with a Lexis object} \description{ Merge two data frames, or a data frame with a \code{Lexis} object. } \usage{ \method{merge}{data.frame}(x, y, ...) } \arguments{ \item{x, y}{data frames, or objects to be coerced into one} \item{...}{optional arguments for the merge method} } \details{ This version of \code{merge.default} masks the one in the \code{base}. It ensures that, if either \code{x} or \code{y} is a \code{Lexis} object, then \code{merge.Lexis} is called. } \value{ A merged \code{Lexis} object or data frame. } \author{Martyn Plummer} \seealso{\code{\link{Lexis}}} \keyword{ts} Epi/man/Relevel.Rd0000644000175100001440000000236312531361107013450 0ustar hornikusers\name{Relevel} \alias{Relevel} \alias{Relevel.factor} \title{Reorder and combine levels of a factor} \description{ The levels of a factor are re-ordered so that the levels specified by \code{ref} is first and the others are moved down. This is useful for \code{contr.treatment} contrasts which take the first level as the reference. Levels may also be combined. } \usage{ \method{Relevel}{factor}( x, ref, first = TRUE, collapse="+", \dots ) } \arguments{ \item{x}{An unordered factor} \item{ref}{The names or numbers of levels to be the first. If \code{ref} is a list, factor levels mentioned in each list element are combined. If the list is named the names are used as new factor levels.} \item{first}{Should the levels mentioned in ref come before those not?} \item{collapse}{String used when collapsing factor levels.} \item{\dots}{Arguments passed on to other methods.} } \value{ An unordered factor, where levels of \code{x} have been reordered and/or collapsed. } \seealso{\code{\link{Relevel.Lexis}}} \examples{ ff <- factor( sample( letters[1:5], 100, replace=TRUE ) ) table( ff, Relevel( ff, list( AB=1:2, "Dee"=4, c(3,5) ) ) ) table( ff, rr=Relevel( ff, list( 5:4, Z=c("c","a") ), coll="-und-", first=FALSE ) ) } \keyword{manip} Epi/man/ci.lin.Rd0000644000175100001440000001621112557611166013236 0ustar hornikusers\name{ci.lin} \alias{ci.lin} \alias{ci.mat} \alias{ci.exp} \alias{ci.pred} \alias{Wald} \title{ Compute linear functions of parameters with standard errors and confidence limits } \description{ For a given model object the function computes a linear function of the parameters and the corresponding standard errors, p-values and confidence intervals. } \usage{ ci.lin( obj, ctr.mat = NULL, subset = NULL, subint = NULL, diffs = FALSE, fnam = !diffs, vcov = FALSE, alpha = 0.05, df = Inf, Exp = FALSE, sample = FALSE ) ci.exp( ..., Exp = TRUE, pval=FALSE ) Wald( obj, H0=0, ... ) ci.mat( alpha = 0.05, df = Inf ) ci.pred( obj, newdata, Exp = NULL, alpha = 0.05, df = Inf ) } \arguments{ \item{obj}{A model object (of class \code{lm}, \code{glm}, \code{coxph}, \code{survreg}, \code{clogistic}, \code{cch}, \code{lme}, \code{mer}, \code{lmerMod}, \code{gls}, \code{nls}, \code{gnlm}, \code{MIresult}, \code{mipo}, \code{polr}, or \code{rq}). } \item{ctr.mat}{Contrast matrix to be multiplied to the parameter vector, i.e. the desired linear function of the parameters.} \item{subset}{The subset of the parameters to be used. If given as a character vector, the elements are in turn matched against the parameter names (using \code{grep}) to find the subset. Repeat parameters may result from using a character vector. This is considered a facility.} \item{subint}{\code{sub}set selection like for \code{subset}, except that elements of a character vector given as argument will be used to select a number of subsets of parameters and only the \code{int}ersection of these is returned.} \item{diffs}{If TRUE, all differences between parameters in the subset are computed. \code{ctr.mat} is ignored. If \code{obj} inherits from \code{lm}, and \code{subset} is given as a string \code{subset} is used to search among the factors in the model and differences of all factor levels for the first match are shown. If \code{subset} does not match any of the factors in the model, all pairwise differences between parameters matching are returned.} \item{fnam}{Should the common part of the parameter names be included with the annotation of contrasts? Ignored if \code{diffs==T}. If a sting is supplied this will be prefixed to the labels.} \item{vcov}{Should the covariance matrix of the set of parameters be returned? If this is set, \code{Exp} is ignored. See details.} \item{alpha}{Significance level for the confidence intervals.} \item{df}{Integer. Number of degrees of freedom in the t-distribution used to compute the quantiles used to construct the confidence intervals.} \item{Exp}{If \code{TRUE} columns 5:6 are replaced with exp( columns 1,5,6 ). For \code{ci.pred} it indicates whether the predictions should be exponentiated - the default is to make a prediction on the scale of the linear predictor and transform it by the inverse link function; if FALSE, the prediction on the link scale is returned.} \item{sample}{Logical or numerical. If \code{TRUE} or numerical a sample of size \code{as.numeric(sample)} is drawn from the multivariate normal with mean equal to the (\code{subet} defined) coefficients and variance equal to the estimated variance-covriance of these. These are then transformed by \code{ctr.mat} and returned.} \item{pval}{Logical. Should a column of P-values be included with the estimates and confidence intervals output by \code{ci.exp}.} \item{H0}{Numeric. The null values for the selected/transformed parameters to be tested by a Wald test. Must have the same length as the selected parameter vector.} \item{\ldots}{Parameters passed on to \code{ci.lin}.} \item{newdata}{Data frame of covariates where prediction is made.} } \value{ \code{ci.lin} returns a matrix with number of rows and row names as \code{ctr.mat}. The columns are Estimate, Std.Err, z, P, 2.5\% and 97.5\%. If \code{vcov=TRUE} a list with components \code{est}, the desired functional of the parameters and \code{vcov}, the variance covariance matrix of this, is returned but not printed. If \code{Exp==TRUE} the confidence intervals for the parameters are replaced with three columns: exp(estimate,c.i.). \code{ci.exp} returns only the exponentiated parameter estimates with confidence intervals. It is merely a wrapper for \code{ci.lin}, fishing out the last 3 columns from \code{ci.lin(...,Exp=TRUE)}. If you just want the estimates and confidence limits, but not exponentiated, use \code{ci.exp(...,Exp=FALSE)}. \code{Wald} computes a Wald test for a subset of (possibly linear combinations of) parameters being equal to the vector of null values. The selection of the subset of parameters is the same as for \code{ci.lin}. Using the \code{ctr.mat} argument makes it possible to do a Wald test for equality of parameters. \code{Wald} returns a named numerical vector of length 3, with names \code{Chisq}, \code{d.f.} and \code{P}. \code{ci.mat} returns a 2 by 3 matrix with rows \code{c(1,0,0)} and \code{c(0,-1,1)*1.96}, devised to post-multiply to a p by 2 matrix with columns of estimates and standard errors, so as to produce a p by 3 matrix of estimates and confidence limits. Used internally in \code{ci.lin} and \code{ci.cum}. The 1.96 is replaced by the appropriate quantile from the normal or t-distribution when arguments \code{alpha} and/or \code{df} are given. \code{ci.pred} returns a 3-column matrix with estimates and upper and lower confidence intervals as columns. This is just a convenience wrapper for \code{predict.glm(obj,se.fit=TRUE)} which returns a rather unhandy structure. The prediction with c.i. is made in the \code{link} scale, and by default transformed by the inverse link, since the most common use for this is for multiplicative Poisson or binomial models with either log or logit link. } \author{ Bendix Carstensen, \url{BendixCarstensen.com} & Michael Hills \url{http://www.mhills.pwp.blueyonder.co.uk/} } \seealso{See also \code{\link{ci.cum}} for a function computing cumulative sums of (functions of) parameter estimates.} \examples{ # Bogus data: f <- factor( sample( letters[1:5], 200, replace=TRUE ) ) g <- factor( sample( letters[1:3], 200, replace=TRUE ) ) x <- rnorm( 200 ) y <- 7 + as.integer( f ) * 3 + 2 * x + 1.7 * rnorm( 200 ) # Fit a simple model: mm <- lm( y ~ x + f + g ) ci.lin( mm ) ci.lin( mm, subset=3:6, diff=TRUE, fnam=FALSE ) ci.lin( mm, subset=3:6, diff=TRUE, fnam=TRUE ) ci.lin( mm, subset="f", diff=TRUE, fnam="f levels:" ) print( ci.lin( mm, subset="g", diff=TRUE, fnam="gee!:", vcov=TRUE ) ) # Use character defined subset to get ALL contrasts: ci.lin( mm, subset="f", diff=TRUE ) # A Wald test of whether the g-parameters are 0 Wald( mm, subset="g" ) # Wald test of whether the three first f-parameters are equal: ( CM <- rbind( c(1,-1,0,0), c(1,0,-1,0)) ) Wald( mm, subset="f", ctr.mat=CM ) # or alternatively ( CM <- rbind( c(1,-1,0,0), c(0,1,-1,0)) ) Wald( mm, subset="f", ctr.mat=CM ) } \keyword{models} \keyword{regression} Epi/man/fit.baseline.rd0000644000175100001440000000162012531361107014450 0ustar hornikusers\name{fit.baseline} \alias{fit.baseline} \title{ Fit a piecewise contsnt intesity model for interval censored data. } \description{ Utility function Fits a binomial model with logaritmic link, with \code{y} as outcome and covariates in \code{rates.frame} to estimate rates in the inttervals between \code{breaks}. } \usage{ fit.baseline( y, rates.frame, start ) } \arguments{ \item{y}{Binary vector of outcomes} \item{rates.frame}{Dataframe expanded from the original data by \code{\link{expand.data}}} \item{start}{Starting values for the rate parameters. If not supplied, then starting values are generated.} } \value{ A \code{\link{glm}} object, with binomial error and logaritmic link. } \author{ Martyn Plummer, \email{plummer@iarc.fr} } \seealso{ \code{\link{fit.add}} \code{\link{fit.mult}} } \keyword{ models } \keyword{ regression } \keyword{ survival } Epi/man/plot.apc.Rd0000644000175100001440000000342312531361107013570 0ustar hornikusers\name{plot.apc} \alias{plot.apc} \alias{apc.plot} \title{Plot the estimates from a fitted Age-Period-Cohort model} \description{ This function plots the estimates created by \code{\link{apc.fit}} in a single graph. It just calls \code{\link{apc.frame}} after computing some sensible values of the parameters, and subsequently plots the estimates using \code{\link{apc.lines}}. } \usage{ \method{plot}{apc}( x, r.txt="Rate", ...) apc.plot( x, r.txt="Rate", ...) } \arguments{ \item{x}{ An object of class \code{apc}. } \item{r.txt}{ The text to put on the vertical rate axis. } \item{\dots}{ Additional arguments passed on to \code{\link{apc.lines}}. } } \value{ A numerical vector of length two, with names \code{c("cp.offset","RR.fac")}. The first is the offset for the cohort period-axis, the second the multiplication factor for the rate-ratio scale. Therefore, if you want to plot at \code{(x,y)} in the right panel, use \code{(x-res["cp.offset"],y/res["RR.fac"])} \code{=(x-res[1],y/res[2])}. This vector should be supplied for the parameter \code{frame.par} to \code{\link{apc.lines}} if more sets of estimates is plotted in the same graph, however see \code{\link{cp.points}}. } \details{\code{plot.apc} is just a wrapper for \code{apc.plot}.} \author{ Bendix Carstensen, Steno Diabetes Center, \url{http://BendixCarstensen.com} } \seealso{ \code{\link{apc.lines}}, \code{\link{lines.apc}}, \code{\link{apc.frame}}, \code{\link{apc.fit}} } \examples{ data( lungDK ) attach( lungDK ) apc1 <- apc.fit( A=Ax, P=Px, D=D, Y=Y/10^5 ) fp <- apc.plot( apc1 ) apc.lines( apc1, frame.par=fp, drift=1.01, col="red" ) for( i in 1:11 ) apc.lines( apc1, frame.par=fp, drift=1+(i-6)/100, col=rainbow(12)[i] ) } \keyword{hplot} Epi/man/lep.Rd0000644000175100001440000000233012531361107012624 0ustar hornikusers\name{lep} \alias{lep} \docType{data} \title{An unmatched case-control study of leprosy incidence} \description{ The \code{lep} data frame has 1370 rows and 7 columns. This was an unmatched case-control study in which incident cases of leprosy in a region of N. Malawi were compared with population controls. } \format{ This data frame contains the following columns: \tabular{rl}{ \code{id}: \tab subject identifier: a numeric vector \cr \code{d}: \tab case/control status: a numeric vector (1=case, 0=control) \cr \code{age}: \tab a factor with levels \code{5-9} \code{10-14} \code{15-19} \code{20-24} \code{25-29} \code{30-44} \code{45+} \cr \code{sex}: \tab a factor with levels \code{male}, \code{female} \cr \code{bcg}: \tab presence of vaccine scar, a factor with levels \code{no} \code{yes} \cr \code{school}: \tab schooling, a factor with levels \code{none} \code{1-5yrs} \code{6-8yrs} \code{sec/tert} \cr \code{house}: \tab housing, a factor with levels \code{brick} \code{sunbrick} \code{wattle} \code{temp} \cr } } \source{ The study is described in more detail in Clayton and Hills, Statistical Models in Epidemiology, Oxford University Press, Oxford:1993. } \examples{ data(lep) } \keyword{datasets} Epi/man/ccwc.Rd0000644000175100001440000000413412531361107012767 0ustar hornikusers\name{ccwc} \alias{ccwc} \title{Generate a nested case-control study} \usage{ ccwc( entry=0, exit, fail, origin=0, controls=1, match=list(), include=list(), data=NULL, silent=FALSE ) } \arguments{ \item{entry}{ Time of entry to follow-up } \item{exit}{ Time of exit from follow-up } \item{fail}{ Status on exit (1=Fail, 0=Censored) } \item{origin}{ Origin of analysis time scale } \item{controls}{ The number of controls to be selected for each case } \item{match}{ List of categorical variables on which to match cases and controls } \item{include}{ List of other variables to be carried across into the case-control study } \item{data}{ Data frame in which to look for input variables } \item{silent}{ If FALSE, echos a . to the screen for each case-control set created; otherwise produces no output. } } \description{ Given the basic outcome variables for a cohort study: the time of entry to the cohort, the time of exit and the reason for exit ("failure" or "censoring"), this function computes risk sets and generates a matched case-control study in which each case is compared with a set of controls randomly sampled from the appropriate risk set. Other variables may be matched when selecting controls. } \value{ The case-control study, as a dataframe containing: \item{Set}{ case-control set number } \item{Map}{ row number of record in input dataframe } \item{Time}{ failure time of the case in this set } \item{Fail}{ failure status (1=case, 0=control) } These are followed by the matching variables, and finally by the variables in the \code{include} list } \references{ Clayton and Hills, Statistical Models in Epidemiology, Oxford University Press, Oxford:1993. } \author{ David Clayton } \seealso{ \code{\link{Lexis}} } \examples{ # # For the diet and heart dataset, create a nested case-control study # using the age scale and matching on job # data(diet) dietcc <- ccwc( doe, dox, chd, origin=dob, controls=2, data=diet, include=energy, match=job) } \keyword{datagen} Epi/man/clogistic.Rd0000644000175100001440000000656012531361107014035 0ustar hornikusers\name{clogistic} \alias{clogistic} \title{Conditional logistic regression} \description{ Estimates a logistic regression model by maximizing the conditional likelihood. The conditional likelihood calculations are exact, and scale efficiently to strata with large numbers of cases. } \usage{ clogistic(formula, strata, data, subset, na.action, init, model = TRUE, x = FALSE, y = TRUE, contrasts = NULL, iter.max=20, eps=1e-6, toler.chol = sqrt(.Machine$double.eps)) } \arguments{ \item{formula}{Model formula} \item{strata}{Factor describing membership of strata for conditioning} \item{data}{data frame containing the variables in the formula and strata arguments} \item{subset}{subset of records to use} \item{na.action}{missing value handling} \item{init}{initial values} \item{model}{ a logical value indicating whether \emph{model frame} should be included as a component of the returned value} \item{x,y}{ logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value. } \item{contrasts}{ an optional list. See the \code{contrasts.arg} of \code{model.matrix.default} } \item{iter.max}{maximum number of iterations} \item{eps}{ Convergence tolerance. Iteration continues until the relative change in the conditional log likelihood is less than \code{eps}. Must be positive. } \item{toler.chol}{ Tolerance used for detection of a singularity during a Cholesky decomposition of the variance matrix. This is used to detect redundant predictor variables. Must be less than \code{eps}. } } \value{ An object of class \code{"clogistic"}. This is a list containing the following components: \item{coefficients}{ the estimates of the log-odds ratio parameters. If the model is over-determined there will be missing values in the vector corresponding to the redundant columns in the model matrix. } \item{var}{ the variance matrix of the coefficients. Rows and columns corresponding to any missing coefficients are set to zero. } \item{loglik}{ a vector of length 2 containing the log-likelihood with the initial values and with the final values of the coefficients. } \item{iter}{ number of iterations used. } \item{n}{ number of observations used. Observations may be dropped either because they are missing, or because they belong to a homogeneous stratum. For more details on which observations were used, see \code{informative} below. } \item{informative}{ if \code{model=TRUE}, a logical vector of length equal to the number of rows in the model frame. This indicates whether an observation is informative. Strata that are homogeneous with respect to either the outcome variable or the predictor variables are uninformative, in the sense that they can be removed without modifying the estimates or standard errors. If \code{model=FALSE}, this is NULL. } The output will also contain the following, for documentation see the \code{glm} object: \code{terms}, \code{formula}, \code{call}, \code{contrasts}, \code{xlevels}, and, optionally, \code{x}, \code{y}, and/or \code{frame}. } \examples{ data(bdendo) clogistic(d ~ cest + dur, strata=set, data=bdendo) } \author{Martyn Plummer} \seealso{\code{\link{glm}}} \keyword{models} Epi/man/ncut.Rd0000644000175100001440000000304512531361107013021 0ustar hornikusers\name{ncut} \alias{ncut} \title{ Function to group a variable in intervals.} \description{ Cuts a continuous variable in intervals. As opposed to \code{cut} which returns a factor, \code{ncut} returns a numeric variable. } \usage{ ncut(x, breaks, type="left" ) } \arguments{ \item{x}{A numerical vector.} \item{breaks}{Vector of breakpoints. \code{NA} will results for values below \code{min(breaks)} if \code{type="left"}, for values above \code{max(breaks)} if \code{type="right"} and for values outside \code{range(breaks)} if \code{type="mid"}} \item{type}{Character: one of \code{c("left","right","mid")}, indicating whether the left, right or midpoint of the intervals defined in breaks is returned.} } \details{ The function uses the base function \code{findInterval}. } \value{ A numerical vector of the same length as \code{x}. } \author{ Bendix Carstensen, Steno Diabetes Center, \email{bxc@steno.dk}, \url{http://BendixCarstensen.com}, with essential input from Martyn Plummer, IARC. } \seealso{ \code{\link{cut}}, \code{\link{findInterval}} } \examples{ br <- c(-2,0,1,2.5) x <- c( rnorm( 10 ), br, -3, 3 ) cbind( x, l=ncut( x, breaks=br, type="l" ), m=ncut( x, breaks=br, type="m" ), r=ncut( x, breaks=br, type="r" ) )[order(x),] x <- rnorm( 200 ) plot( x, ncut( x, breaks=br, type="l" ), pch=16, col="blue", ylim=range(x) ) abline( 0, 1 ) abline( v=br ) points( x, ncut( x, breaks=br, type="r" ), pch=16, col="red" ) points( x, ncut( x, breaks=br, type="m" ), pch=16, col="green" ) } \keyword{manip} Epi/man/expand.data.rd0000644000175100001440000000333212531361107014276 0ustar hornikusers\name{expand.data} \alias{expand.data} \title{ Function to expand data for regression analysis of interval censored data. } \description{ This is a utility function. The original records with \code{first.well}, \code{last.well} and \code{first.ill} are expanded to multiple records; several for each interval where the person is known to be well and one where the person is known to fail. At the same time columns for the covariates needed to estimate rates and the response variable are generated. } \usage{ expand.data(fu, formula, breaks, data) } \arguments{ \item{fu}{A 3-column matrix with \code{first.well}, \code{last.well} and \code{first.ill} in each row.} \item{formula}{Model fromula, used to derive the model matrix.} \item{breaks}{Defines the intervals in which the baseline rate is assumed constant. All follow-up before the first and after the last break is discarded.} \item{data}{Datafrem in which \code{fu} and \code{formula} is interpreted.} } \value{ Returns a list with three components \item{rates.frame}{Dataframe of covariates for estimation of the baseline rates --- one per interval defined by \code{breaks}.} \item{cov.frame}{Dataframe for estimation of the covariate effects. A data-framed version of the designmatrix from \code{formula}.} \item{y}{Response vector.} } \references{ B Carstensen: Regression models for interval censored survival data: application to HIV infection in Danish homosexual men. Statistics in Medicine, 15(20):2177-2189, 1996. } \author{ Martyn Plummer, \email{plummer@iarc.fr} } \seealso{ \code{\link{Icens}} \code{\link{fit.mult}} \code{\link{fit.add}} } \keyword{ models } \keyword{ regression } \keyword{ survival } Epi/man/plot.Lexis.Rd0000644000175100001440000001160512531361107014112 0ustar hornikusers\name{plot.Lexis} \alias{plot.Lexis} \alias{points.Lexis} \alias{lines.Lexis} \alias{PY.ann} \alias{PY.ann.Lexis} \title{Lexis diagrams} \description{ The follow-up histories represented by a Lexis object can be plotted using one or two dimensions. The two dimensional plot is a Lexis diagram showing follow-up time simultaneously on two time scales. } \usage{ \method{plot}{Lexis}(x=Lexis( entry=list(Date=1900,Age=0), exit=list(Age=0) ), time.scale = NULL, type="l", breaks="lightgray", ...) \method{points}{Lexis}(x, time.scale = options()[["Lexis.time.scale"]] , ...) \method{lines}{Lexis}(x, time.scale = options()[["Lexis.time.scale"]], ...) \method{PY.ann}{Lexis}(x, time.scale = options()[["Lexis.time.scale"]], digits=1, ...) } \arguments{ \item{x}{An object of class \code{Lexis}. The default is a bogus \code{Lexis} object, so that \code{plot.Lexis} can be called without the first argument and still produce a(n empty) Lexis diagram. Unless arguments \code{xlim} and \code{ylim} are given in this case the diagram is looking pretty daft.} \item{time.scale}{A vector of length 1 or 2 giving the time scales to be plotted either by name or numerical order} \item{type}{Character indication what to draw: "n" nothing (just set up the diagram), "l" - liefelines, "p" - endpoints of follow-up, "b" - both lifelines and endpoints.} \item{breaks}{a string giving the colour of grid lines to be drawn when plotting a split Lexis object. Grid lines can be suppressed by supplying the value \code{NULL} to the \code{breaks} argument} \item{digits}{Numerical. How many digits after the demimal points should be when plotting the person-years.} \item{\dots}{Further graphical parameters to be passed to the plotting methods. Grids can be drawn (behind the life lines) using the following parameters in \code{plot}: \itemize{ \item \code{grid} If logical, a background grid is set up using the axis ticks. If a list, the first component is used as positions for the vertical lines and the last as positions for the horizontal. If a nunerical vector, grids on both axes are set up using the distance between the numbers. \item \code{col.grid="lightgray"} Color of the background grid. \item \code{lty.grid=2} Line type for the grid. \item \code{coh.grid=FALSE} Should a 45 degree grid be plotted?} } } \details{ The plot method for \code{Lexis} objects traces ``life lines'' from the start to the end of follow-up. The \code{points} method plots points at the end of the life lines. If \code{time.scale} is of length 1, the life lines are drawn horizontally, with the time scale on the X axis and the id value on the Y axis. If \code{time.scale} is of length 2, a Lexis diagram is produced, with diagonal life lines plotted against both time scales simultaneously. If \code{lex} has been split along one of the time axes by a call to \code{splitLexis}, then vertical or horizontal grid lines are plotted (on top of the life lines) at the break points. \code{PY.ann} writes the length of each (segment of) life line at the middle of the line. Not advisable to use with large cohorts. Another example is in the example file for \code{\link{occup}}. } \author{Martyn Plummer} \examples{ # A small bogus cohort xcoh <- structure( list( id = c("A", "B", "C"), birth = c("14/07/1952", "01/04/1957", "10/06/1987"), entry = c("04/08/1965", "08/09/1972", "23/12/1991"), exit = c("27/06/1997", "23/05/1995", "24/07/1998"), fail = c(1, 0, 1) ), .Names = c("id", "birth", "entry", "exit", "fail"), row.names = c("1", "2", "3"), class = "data.frame" ) # Convert the character dates into numerical variables (fractional years) xcoh$bt <- cal.yr( xcoh$birth, format="\%d/\%m/\%Y" ) xcoh$en <- cal.yr( xcoh$entry, format="\%d/\%m/\%Y" ) xcoh$ex <- cal.yr( xcoh$exit , format="\%d/\%m/\%Y" ) # See how it looks xcoh # Define as Lexis object with timescales calendar time and age Lcoh <- Lexis( entry = list( per=en ), exit = list( per=ex, age=ex-bt ), exit.status = fail, data = xcoh ) # Default plot of follow-up plot( Lcoh ) # Show follow-up time PY.ann( Lcoh ) # Show exit status plot( Lcoh, type="b" ) # Same but failures only plot( Lcoh, type="b", pch=c(NA,16)[Lcoh$fail+1] ) # With a grid and deaths as endpoints plot( Lcoh, grid=0:10*10, col="black" ) points( Lcoh, pch=c(NA,16)[Lcoh$lex.Xst+1] ) # With a lot of bells and whistles: plot( Lcoh, grid=0:20*5, col="black", xaxs="i", yaxs="i", xlim=c(1960,2010), ylim=c(0,50), lwd=3, las=1 ) points( Lcoh, pch=c(NA,16)[Lcoh$lex.Xst+1], col="red", cex=1.5 ) } \seealso{\code{\link{Lexis}}, \code{\link{splitLexis}}} \keyword{hplot} \keyword{aplot} Epi/man/gen.exp.Rd0000644000175100001440000001450112531361107013413 0ustar hornikusers\name{gen.exp} \alias{gen.exp} \title{ Generate covariates for drug-exposure follow-up from drug purchase records. } \description{ From records of drug purchase and possibly known treatment intensity, the time since first drug use and cumulative dose at prespecified times is computed. Optionally, lagged exposures are computed too, i.e. cumulative exposure a prespecified time ago. } \usage{ gen.exp(purchase, id = "id", dop = "dop", amt = "amt", dpt = "dpt", fu, doe = "doe", dox = "dox", breaks, use.dpt = ( dpt \%in\% names(purchase) ), lags = NULL, push.max = Inf, pred.win = Inf, lag.dec = 1 ) } \arguments{ \item{purchase}{Data frame with columns \code{id}-person id, \code{dop}-date of purchase, \code{amt}-amount purchased, and optionally \code{dpt}-defined daily dose, that is how much is assumed to be ingested per unit time. The time unit used here is assumed to be the same as that used in \code{dop}, so despite the name it is not necessarily measured per day.} \item{id}{Name of the id variable in the data frame.} \item{dop}{Name of the date of purchase variable in the data frame.} \item{amt}{Name of the amount purchased variable in the data frame.} \item{dpt}{Name of the dose-per-time variable in the data frame.} \item{fu}{Data frame with follow-up period for each person, the person id variable must have the same name as in the \code{purchase} data frame.} \item{doe}{Name of the date of entry variable.} \item{dox}{Name of the date of exit variable.} \item{use.dpt}{Logical, should we use information on dose per time.} \item{breaks}{Numerical vector of time points where the time since exposure and the cumulative dose are computed.} \item{lags}{Numerical vector of lag-times used in computing lagged cumulative doses.} \item{push.max}{How much can purchases maximally be pushed forward in time. See details.} \item{pred.win}{The length of the window used for constructing the average dose per time used to compute the duration of the last purchase} \item{lag.dec}{How many decimals to use in the construction of names for the lagged exposure variables} } \details{ Each purchase record is converted into a time-interval of exposure. If \code{use.dpt} is \code{TRUE} then the dose per time information is used to compute the exposure interval associated with each purchase. Exposure intervals are stacked, that is each interval is put after any previous. This means that the start of exposure to a given purchase can be pushed into the future. The parameter \code{push.max} indicates the maximally tolerated push. If this is reached by a person, the assumption is that some of the purchased drug is not counted in the exposure calculations. The \code{dpt} can either be a constant, basically translating the purchased amount into exposure time the same way for all persons, or it can be a vector with different treatment intensities for each purchase. In any case the cumulative dose is computed taking this into account. If \code{use.dpt} is \code{FALSE} then the exposure from one purchase is assumed to stretch over the time to the next purchase, so we are effectively assuming different rates of dose per time between any two adjacent purchases. Moreover, with this approach, periods of non-exposure does not exist. The intention of this function is to generate covariates for a particular drug for the entire follow-up of each person. The reason that the follow-up prior to drug purchase and post-exposure is included is that the covariates must be defined for these periods too, in order to be useful for analysis of disease outcomes. } \value{A data frame with one record per follow-up interval between \code{breaks}, with columns: \describe{ \item{\code{id}}{person id.} \item{\code{dof}}{date of follow up, i.e. start of interval. Apart from possibly the first interval for each person, this will assume values in the set of the values in \code{breaks}.} \item{\code{Y}}{the length of interval.} \item{\code{tfi}}{time from first initiation of drug.} \item{\code{tfc}}{time from latest cessation of drug.} \item{\code{cdur}}{cumulative time on the drug.} \item{\code{cdos}}{cumulative dose.} \item{\code{ldos}}{suffixed with one value per element in \code{lags}, the latter giving the cumulative doses \code{lags} before \code{dof}.} } } \author{Bendix Carstensen, \email{bxc@steno.dk}} \seealso{\code{\link{Lexis}}, \code{\link{splitLexis}}} \examples{ # Construct a simple data frame of purchases for 3 persons # The purchase units (in variable dose) correspond to n <- c( 10, 17, 8 ) dop <- c( 1995.2+cumsum(sample(1:4/10,n[1],replace=TRUE)), 1997.3+cumsum(sample(1:4/10,n[2],replace=TRUE)), 1997.3+cumsum(sample(1:4/10,n[3],replace=TRUE)) ) amt <- sample( 1:3/15, sum(n), replace=TRUE ) dpt <- sample( 15:20/25, sum(n), replace=TRUE ) dfr <- data.frame( id = rep(1:3,n), dop, amt = amt, dpt = dpt ) round( dfr, 3 ) # Construct a simple dataframe for follow-up periods for these 3 persons fu <- data.frame( id = 1:3, doe = c(1995,1997,1996)+1:3/4, dox = c(2001,2003,2002)+1:3/5 ) round( fu, 3 ) ( dpos <- gen.exp( dfr, fu = fu, breaks = seq(1990,2015,0.5), lags = 2:3/5 ) ) ( xpos <- gen.exp( dfr, fu = fu, use.dpt = FALSE, breaks = seq(1990,2015,0.5), lags = 2:3/5 ) ) # How many relevant columns nvar <- ncol(xpos)-3 clrs <- rainbow(nvar) # Show how the variables relate to the follow-up time par( mfrow=c(3,1), mar=c(3,3,1,1), mgp=c(3,1,0)/1.6, bty="n" ) for( i in unique(xpos$id) ) matplot( xpos[xpos$id==i,"dof"], xpos[xpos$id==i,-(1:3)], xlim=range(xpos$dof), ylim=range(xpos[,-(1:3)]), type="l", lwd=2, lty=1, col=clrs, ylab="", xlab="Date of follow-up" ) ytxt <- par("usr")[3:4] ytxt <- ytxt[1] + (nvar:1)*diff(ytxt)/(nvar+2) xtxt <- rep( sum(par("usr")[1:2]*c(0.98,0.02)), nvar ) text( xtxt, ytxt, colnames(xpos)[-(1:3)], font=2, col=clrs, cex=1.5, adj=0 ) } \keyword{data manipulation} Epi/CHANGES0000644000175100001440000005754112575536150012025 0ustar hornikusersChanges in 1.1.71 o Bug from calling lme4::vcov from within ci.lin fixed Changes in 1.1.70 o Bug in calling lme4::fixef from within ci.lin fixed o ci.lin with sample=TRUE now samples from the posterior of the parameter vector and then transforms this by the contrast matrix (earlier the sampling was from the posterior of the ctr.mat transformed parameters). Changes in 1.1.69 o ci.cum now has an argument ci.Exp (defaults to FALSE) that computes the ci of the cum.haz on the log-scale. This is useful if you want to transform c.i.s to the survival scale and want the c.i.s for the survival function to stay inside [0,1]. o ci.pred updated to automatically use the inverse link for transformation of results. Also now only accepts glm objects. o Ns now have arguents ref= and detrend=. ref= allows a reference value to be specified (where the Ns is 0); this is independent of the supplied data. detrend= projects the columns of Ns() on the orthogonal of the variable supplied; this is strongly dependent on the data. Changes in 1.1.68 o ci.lin has been groomed to use internally (newly, from 1.1.68) defined methods COEF and VCOV to extract coefficients and variance-covariances of these from different types of objects. Changes in 1.1.67 o The simLexis vignette has been groomed a bit o The function ci.pred (a wrapper for predict.glm) added Changes in 1.1.66 o A crr.Lexis method has been added to simplify the use of the Fine-Gray model when data are set up in a Lexis object. o ci.lin and ci.exp now recognises crr objects. o A wrapper, ci.pred, for predict.glm has been added, it returns predictions with confidence intervals. Changes in 1.1.65 o Despite the note, dropped rows from construction of a Lexis objects were not put in the "dropped" attribute. Changes in 1.1.64 o Fixed the sim-Lexis vignette Changes in 1.1.64 o Added function ZArray, which generates an array of 0s just as NArray generates an array of NAs. o Updated code to avoid "::" and ":::" as far as possible, as well as "<<-". Hence excluded records from construction of a Lexis object is now put in an attribute "dropped" of the Lexis object. Changes in 1.1.62 o Bug fix in the example code for boxes.Lexis Changes in 1.1.61 o ci.lin updated to recognize objects of class "lmerMod" from lmer. Changes in 1.1.60 o Documentation for DMlate updated. Changes in 1.1.59 o boxes.Lexis updated so that also no. of beginners and enders are properly formatted if larger than 999 Changes in 1.1.57 o boxes.Lexis updated with arguments show.BE and BE.pre, allowing annotation of boxes by the *number* of persons strating and ending in different states. Changes in 1.1.57 o simLexis substantially updated and groomed to accept Cox-models for transitions too. Example in the documentation and vignette is expanded accordingly. Changes in 1.1.56 o Typos in documentation corrected. o When records with too short follow-up are encountered by Lexis, they are dropped from the resulting object, but they will be availabe in the object drop.sh in the global environment. o The col and border arguments to plot.pState were not repeated automatically, so needed fixing. o More elaborate warning and error-messages if initiators in absorbing states are handed to simLexis. Changes in 1.1.55 o Documentation of clogistic is clarified w.r.t. likelihood contributions of matched sets. Changes in 1.1.54 o apc.fit updated to use the Ns wrapper, and automatically use knots located so that the marginal number of events is the same between knots. o plot.apc and lines.apc defined as methods for apc objects. They are just wrappers for apc.plot and apc.lines. o gen.exp example slightly modified to give better output. Changes in 1.1.53 o Population figures in N.dk, M.dk and Y.dk updated Changes in 1.1.52 o col.txt argument to plotEst was not working properly. Fixed o txt argument to plotEst can now be an expression vector allowing sub- and superscripts and mathematical expressions. o Relevel did not issue warning or error when a given level was being specified as member of more than one new level. It now stops with an error. o The convenience wrappers Ns (for ns) and NArray (for array) have been added Changes in 1.1.51 o boxes.Lexis fixed so that rates could be computed from matrix input to. Changes in 1.1.50 o Calculation of p-values in ci.lin has been modified, thyanks to Krista Fischer. Changes in 1.1.49 o Default of "border" argument to plot.pState changed to "transparent" Changes in 1.1.48 o Slight tidying of code in apc.fit. Automatic determination of reference points for period and cohort fixed to produce median correctly. Changes in 1.1.47 o Bug in simLexis (specifically in Epi:::simX) where factor levels were wrongly used, is now corrected. Versions earlier are likely to produce wrong state allocation in simulated dataset and not crash. Changes in 1.1.46 o lex.id handling in simLexis changed, allows for easier simulation in chunks which may be necessary to avoid memory problems. Changes in 1.1.45 o simLexis introduced. Allows simulation from multiple timescale mulitste models. Accompanying utilities supplied too: pr.Lexis, prev and plot.prev. Changes in 1.1.44 o A number of dead links in the documentation have been resurrected. o attach() purged from Lexis.diagram, Lexis.lines and ccwc. o Bug in computing AUC in ROC fixed, thanks to Karl Ove Hufthammer. Documentation clarified with respect to definition of test. o Bug fix to remove warning when more than one variable name was supplied in the by= argument of summary.Lexis. Changes in 1.1.43 o N2Y fixed so that local variable bindings are recognized (M. Plummer) o summary.Lexis expanded with the argument by=, allowing a summary by a factor. Also, the default behaviour is now to return only the transition summary, and only optionally the transition rates; giverned by the Rates= argument Changes in 1.1.42 o Bug that caused a crash of ROC when vaiables in the "form" argument were only in the "data" argument data frame and not in the global environmant. Now fixed thanks to Ben Barnes of the Robert Koch Institute, German Center for Cancer Registry Data Changes in 1.1.41 o boxes.Lexis now returns an object of class MS, which easily allows plotting of slightly modified multistate displays using the new command boxes.MS. The facility in boxes.Lexis to produce weedy code for the same purpose has been removed. o boxes.Lexis now allows to show *both* number of transitions *and* rates between the states. o stack.Lexis now takes the Lexis attributes "time.scales" and "breaks" across from the Lexis object to the stacked.Lexis object. Changes in 1.1.40 o subset, transform and Relevel methods have been added for objects of type stacked.Lexis. Changes in 1.1.39 o effx has been expanded with an extra argument eff=, which defaults to NULL, allows "RR" for relative risk for binary data, and "RD" for rate differences for failure data. Also logical response is admitted for binomial and failure responses. o factorize and Relevel are now synonyms. Methods for Relevel are Relevel.default, Relevel.factor, Relevel.Lexis, for factorise only the factorize.default and factorize.Lexis exist. o A mistake in estimating sequential residuals in apc.fit() has been corrected. The wrong model (adc instead of rc, in the case parm == "AD-C-P") was used for the basis of residuals. Thanks to Shih-Yung Su from Taiwan. o as.Date.cal.yr is added, even though it was removed earlier: cal.yr is a class for date variables and the conversion function should be around. o A bug in ci.pd causing calculations to go wrong if vectors were supplied as input. Correction thanks to Patrick Rymer. o A bug in factorize.Lexis is fixed. Now appropriately groups factor levels in the state factors lex.Cst and lex.Xst. Relevel.Lexis is defined as an alias for factorize.Lexis. Thus the functins Relevel() and factorize() are now identical. Changes in 1.1.36 o A bug in boxes.Lexis causing arrow-coloring to go out of sync fixed o Array problems in stat.table fixed Changes in 1.1.35 o none. Just compiled for the archive with R 2.15.0 Changes in 1.1.34 o plotEst now has an arguments col.txt and font.txt which allows the use of different colors and fonts for the annotation of the estimates. Models likely to be multiplicative incurs a logarithmic x-axis in the plot. o ci.exp introduced - a wrapper for ci.lin, getting the exponentiated parameters with CIs. o A bug causing unintended reordering of levels using boxes.Lexis is fixed. o Method etm for Lexis objects included. This just takes a Lexis object, and fishes out the relevant information to be able to call the function etm from the etm package (empirical transition matrix). This function is now physically defined in the file(s) foreign.Lexis.R(d). Changes in 1.1.32 o gen.exp was re-written and simplified. o Small cosmetic changes to the code for N2Y Changes in 1.1.31 o The extractor functions entry, exit, status and dur have now an argument by.id=FALSE. If set to TRUE, only one record per lex.id is returned and the resulting object has lex.id as (row)names attribute. Changes in 1.1.30 o DMlate expanded with the column dooad o Documentation for as.Date.cal.yr fixed o Bug in gen.exp fixed (It was assuming a data frame called dfr existed was wrong, but not spotted by the example because in the example one actually did exist!) Changes in 1.1.29 o New function gen.exp for generating time-varying exposure variables from drug purchase records. Changes in 1.1.28 o splitLexis now allows NAs in the timescale on which you split. Records with NAs are simply left untouched, but a warning is printed. o A bug in boxes.Lexis preventing rates to be printed was issued. Changes in 1.1.27 o A few typos corrected o Functions a.lines, a.points, cp.lines and cp.points added to facilitate plotting points and curves from APC-models. o apc.fit did not return the reference cohort/period if it was not supplied in a model with explicit drift. Changes in 1.1.26 o A new function N2Y added which computes person-years in Lexis triangles from population prevalence data. o Demographic example data from Denmark added: N.dk - population size at 1 Jan Y.dk - risk time in Lexis triangles M.dk - mortality data B.dk - births in Denmark 1902 ff. Changes in 1.1.25 o Added sd() function to stat.table() o tmat.Lexis has an argument Y=FALSE which if set to TRUE will return the person-years in the diagonal. o boxes() now explicitly defined with methods boxes.Lexis and boxes.matrix that explicitly call boxes.default (which is the function doing the work (almost identical to the former boxes.Lexis). Changes in 1.1.24 o countLexis did not take the "timescales" and "breaks" attribute across to the resulting Lexis object. Changes in 1.1.23 o A missing defualt value for new.scale in doCutLexis caused a crash o A missing default value for new.scale in doCutLexis caused a crash when using the count=TRUE argument to cutLexis. Changes in 1.1.23 o ci.lin and ci.cum now have a sample= argument that causes return of a sample from the normal distribution with mean equal to the estimates and variance equal to the estimated variance of the estimates. To be used to do "parametric bootstrap" of complicated functions of the parameters, such as state occupancy probabilities from multistate models. o ci.lin now supports objects of class mipo (Multiple Imputation Pooled Objects --- see the mice package). o tabplot removed --- it was a proper subset of the mosaicplot from the graphics package Changes in 1.1.22 o A bug in boxes.Lexis prevented the use of ht= and wd= arguments to set boxes to a prespecified size. The scaling of these is now also clarified in the man file for boxes.Lexis. Changes in 1.1.21 o Specifying period of cohort effects with only two parameters caused apc.fit to crash. Fixed by adding a few ",drop=FALSE" in subsetting of matrices. o Since as.Date.cal.yr was not used anywhere, it has been removed from the package. o A function Wald added to do Wald test of several parameters or linear combinations of them. It is a small extension on top of ci.lin. Changes in 1.1.20 o CITATION file added. Changes in 1.1.19 o ci.lin amended by an argument subint= allowing to select subsets of parameters matching several strings. Changes in 1.1.18 o boxes.Lexis has been made a bit more versatile for production of box-diagrams from multistate models. Changes in 1.1.17 o A comma was missing in the code-output from boxes.Lexis o mstate.Lexis function changed name to msdata.Lexis, according to the change in convention in the mstate package. Code simplified as it is now using the functionality in stack.Lexis. o A factorize.Lexis function has been added, it basically changes the variables lex.Cst and lex.Xst to factors with same set of levels. A useful facility when we want boxes.Lexis to work. Changes in 1.1.16 o boxes.Lexis now resets the graphical parameters (par()) on exit. o plot.Lexis now has a default Lexis object as argument, allowing use of the function to plot empty Lexis diagrams without setting up a Lexis object first. The bogus object has timescales c("Date","Age") but 0 follow-up time. Changes in 1.1.14 o ci.lin now has an argument df to allow for t-quantiles in ci calculations. o lls() function revised to give nicer (left justified) output. Changes in 1.1.13 o ci.lin now supports objects of class clogistic. o utility function ci.mat() added --- earlier defined inside ci.lin and ci.cum, but also useful on its own. o lls() and clear() added, to ease overview and clearing of workspace (and attachments!) o apc.frame now sets the option "apc.frame.par" with the offset and scaling of calendar time part of the apc frame. This is recognised now by apc.lines automatically. o Function pc.points, pc.lines, pc.matlines, pc.matpoints added to ease plotting the calendar time region of an apc frame; live off the option "apc.frame.par". Changes in 1.1.12 o Added function clogistic for conditional logistic regression. Changes in 1.1.9 o A function PY.ann.Lexis is added. It writes the length of (pieces of) lifelines in a Lexis digram produced by plot.Lexis. o plot.Lexis now sets an option "Lexis.time.scale" which is queried by lines.Lexis and points.Lexis, so that time.scale is only needed in plot.Lexis. Changes in 1.1.8 o apc.fit had a bug in the specification of knots when using the argument model="bs". Fixed. Changes in 1.1.7 o boxes.Lexis has been further enhanced with the facility to plot rates instead of no. transitions on the arrows if required. The code has been tidied a bit too. o The man file for boxes.Lexis and subsidiaries have been renamed to MS.boxes.Rd Changes in 1.1.5 o boxes.Lexis have been enhanced to accommodate two-way transitions between states. Annotation by number of transitions has been improved to accommodate this too by always putting the number on the left side of the arrow. Changes in 1.1.3 o ci.lin() and ci.cum() have been expanded to accept objects of class "MIresult" from the mitools package (Esa Lr). o The boxes.Lexis() now gives a more versatile piece of code, which computes the text widths and heights. Changes in 1.1.2 o cutLexis crashed if new.state=TRUE and new.scale=FALSE were specified. Fixed. Changes in 1.1.1 o Functions stack.Lexis, tmat.Lexis and mstate.Lexis have been added to facilitate practical multistate modeling. The two latter provides an interface to the mstate package. o Functions tbox, dbox, fillarr, boxarr and boxes.Lexis added to facilitate drawing of multistate box diagrams. Changes in 1.1.0 o Two new datasets DMrand and DMlate with random samples from the Danish National diabetes register. The examples from these illustrate most of the recently added multistate stuff. o Minor bug in check.time.scale was fixed (misplaced parentheses in the argument to any(), causing a warning). o cutLexis introduces a new timescale "time since event", which has missing values for any follow-up time prior to event. Hence requires that the Lexis plotting functions explicitly discards the units with missing on timescales in use. Accomplished by the new function valid.times. o cutLexis now places the new states after the precursor states and before the other ones in the factors lex.Cst and lex.Xst. o splitLexis uses the first timescale by default. Which in particular means that in the case of only one time scale it is not necessary to specify it, so this has become acceptable now. o Vignettes has been updated. o Example for ci.cum has been fixed to be compatible with the new survival package as of 2.9.0 as announced. o apc.fit fitted the wrong model when using parm="AC-P". Fixed o The axis scaling of apc.plot has been improved. o apc.frame now by default plots a reference line for RR=1, this may be switched off by the (newly introduced) parameter "ref.line=FALSE". Changes in 1.0.10 o Fixed parse errors in documentation. Changes in 1.0.9 o Thanks to Mike Murphy, Professor of Demography, Department of Social Policy, London School of Economics, a bug causing a crash of apc.fit if only one row in the model matrix corresponds to the reference level was fixed. o Also thanks to Mike Murphy, a much more efficient calculation of median period and cohort is now used. o apc.fit expanded with an argument allowing logistic regression model instead of a Poisson model only. Changes in 1.0.8 o tab.Lexis removed and replaced by summary.Lexis which gives a better summary of the transitions and transition rates. o A bug in ci.pd (confidence interval for probability difference) has been fixed. Changes in 1.0.7 o Stat.table data= argument fixed. Changes in 1.0.6 o Lexis now converts character values of entry/exit.status to factors for lex.Cst and lex.Xst. And produces a warning if the entry.state is defaulted to the first level of exit.state (i.e. when exit.state is given as charcter or factor). o splitLexis gave wrong results for factor states. cutLexis gave wrong results for character states. Fixed by letting Lexis coerce character mode entry.status and exit.status to factors for lex.Cst and lex.Xst. In split.lexis.1D was the problem with the factor states, they were coerced to numeric when stuffed into the new.Xst matrix. Now states are turned to numeric before the call to split.lexis.1D and the factor attributes re-instituted after the split. o Added transform method for Lexis objects. Changes in 1.0.5 o Typos in documentation of APC functions corrected. o cutLexis updated to handle various instances by MP. A few BxC additions to MP's code: - cutLexis2 is renamed cutLexis. BxC's old cutLexis killed. - count=FALSE as argument to cutLexis, just calls countLexis if TRUE. - cutLexis no longer returns the working column lex.cut - cutLexis was missing the attributes "time.scales" and "breaks". Added. - cut= is allowed, simplifying cut of split Lexis objects. - documentation accordingly altered. o splitLexis amended so that lex.Xst is returned as a factor if lex.Cst is a factor. splitLexis crashed if lex.Cst and lex.Xst were factors. o Lexis now allows omission of entry.status --- if exit.status is numeric/logical/factor, entry.status (and hence lex.Cst) will be set to 0/FALSE/first level. o Lexis made sure that lex.Cst and lex.Xst have the same class. If they are factors, the set of levels is taken to be the union. Changes in 1.0.1 o cutLexis now works properly - no it did not! o cutLexis now accepts a (smaller) dataframe with cutpoints and states as input. Changes in 0.9.6 o Bugfix in timeBand, crashed when type="factor" was chosen. levels was given as 0:(lengh(breaks)+1), changed to 0:lengh(breaks) Changes in 0.9.5 o The Lexis definition now assumes that entry is 0 if only one of exit or duration are given as a one-component list. o tab.Lexis is now properly working as a method for Lexis objects. Changes in 0.9.4 o The lex.-variables in Lexis objects are now called lex.dur, lex.Cst, lex.Xst, lex.id (duration, Current state, eXit state and identification) o An extra option states= added to Lexis. If used the state variables are returned as factors. o The utility function deltat.Lexis() has been renamed to dur(). o state() now returns a dataframe of both (entry,exit) states a default. The reason for this is that lex.Cst and lex.Xst may be factors (which actually would be the logical thing to have by default, but it is not enforced only allowed). o entry() and exit() now by default returns matrices with entry and exit times on all timescales. If only one timescale is requested, they return a 1-column matrix. o A minor typo in stat.table corrected: in the definition of the quantile function prob=probs changed to probs=probs. o cutLexis() bugs corrected. Now works with split data too, but requires specification of censoring states --- i.e. states that will be replaced by the new state obtained at the cut date. Changes in 0.9.3 (since 0.9.0) o New function cutLexis() to allow cutting of follow-up time at a specific date for each person, where a new state is assumed. o New function tab.Lexis() which tabulates records as well as events and person-years from a Lexis object. o splitLexis got state information wrong if breaks were not unique. Fixed. Changes in 0.9.0 o effx and effx.match updated following Tartu 2007 to avoid attaching the data, and to correct the parsing of the list of control variables. Changes in 0.8.0 o A new function Lexis() to define follow-up on multiple timescales has been added. An object of class Lexis is defined and a number of utilities for the class are available. Time-splititng is now done by splitLexis(). o The old Lexis function for time-splitting has been renamed to W.Lexis for backward compatibility. o The function epi.eff() has been replaced by effx() and effx.match(). Changes 0.7.2 to 0.7.3 o Icens is now able to handle a constant underlying rate. (A bug in expand.data was fixed). Changes 0.7.0 to 0.7.2 o Bugs in ROC fixed, and the functionality of the grid option slightly chnaged. Changes 0.6.1 to 0.7.0 o Function Icens() for estimation of rates from intervalcensored follow-up data by Martyn Plummer added. o Function epi.eff by Michael Hills is added. Estimates effects in various epidemiological study types. Changes 0.6.0 to 0.6.1 o Coding errors in thoro dataset corrected. Only concerning dates and status for livercancer diagnosis. o Lexis.lines now allows col.life, lwd.life, pch.fail, col.fail and cex.fail to have the same length as the data, i.e. to produce individualized lines and points. As Lexis.diagram calls Lexis.lines, this facility is also available through Lexis.diagram. Changes from 0.4 to 0.6 o ci.pd() amended to support the Agresti-Caffo method for confidence intervals for difference between proportions. Newcombes method 10 is still used in twoby2. o apc.fit() added. Fits age-period-cohort models with a range of possibilities for parametrizations. o Functions for time-splitting at arbitrary times and at recurrent failures have been added: isec(), icut(), fcut1(), fcut() and ex1(). Eventually they will be superseded by new facilities in Lexis. o Function apc.plot() to make a plot of an apc fit is added. It is just a wrapper for apc.frame() and apc.lines(), with suitable computation of the paramters supplied to apc.frame. o Lexis.lines(): pch.fail and col.fail expandS to vectors of length two if only one value is given. o ci.cum() aimed at computing cumulative hazard functions from parametric functions for hazards. o Problem in print.floated() with printing of objects of class "floated" fixed. o Problem in ci.lin when subset did not match any factor names and diff=T was given the function crashed. Fixed, and documentation updated. o cal.yr produces objects of class c("cal.yr","numeric"). Functions as.Date.numeric and as.Date.cal.yr added.