multilevel/0000755000176200001440000000000014211511442012427 5ustar liggesusersmultilevel/NAMESPACE0000644000176200001440000000051712744702147013666 0ustar liggesusersimport("stats") import("MASS") import("nlme") importFrom("graphics","lines","plot") exportPattern("^[^\\.]") S3method(summary, rgr.agree) S3method(summary, rgr.waba) S3method(quantile, rgr.waba) S3method(summary, agree.sim) S3method(quantile, agree.sim) S3method(summary, disagree.sim) S3method(quantile, disagree.sim) multilevel/data/0000755000176200001440000000000014211505004013335 5ustar liggesusersmultilevel/data/paneldat.rda0000644000176200001440000007500414211125555015634 0ustar liggesusers 8Um8n2V&͚FYhF4 T晈d(<d(eTz׻Ͼ׽u}Σ^o)'3 #3+ lr,_Dbd`a1V !Ŗ6v= %4B$ NZ$p @8 @H`&$V:P!mBT8I^$FOI>&N# XE$`J$p"I H HF3 TN3I` &]F$pΑ- %H If$Hb$dI` "kp&$pHG+3 p8 ,'U$MH`/ #g$/%!L$0"XEI` (* h l!* ؓ-H 'jh%NcHI @ Ē@& @H3H`* ,&5$ l!$@A$p#xJ$~$pp"XEI` PH@ I`/ Xq8Cq$K/IF4EHH@I@v>p"$pIIHI`p$!IXJ+I` h- !'8NII c$0~nAy-q9Kk+Kklmc8)8_\@B2(2Tk1"*kbJ60XS v9f򜉲 iײ [SfL473kS -,,SI4βyG7 ATkq}v&?tDY;S ?eŌbni#.ccCWXPPͩ& qEs+ZE YѐBhAπ_6SXbeBqZW117rk {%4&.G1"l_)f?ȟ>WB213Xt9O&T=qޡq;km*Q^,}MQTkFD䌩V_`cK5!H˙XX Y-R<~93*-oX[ZsSL*IhN&amǚbn#N0$)rTZk`g+;ĵ&i\rVk6QҞ7R)fƴdMCgؙQ-*X[~-fXۚ,е6clKT<47X[YJ&oc7XBU|%o`Gƿ`LmH6?@v}Oѭm͆|k!tdI ِ@t~v=Җ$ѩi\ҪwU-ZZ0qF;s8-mYv,)O"ql ;3[ZMr͙6.ԩ4*19WΜ=CLHo:T+;}3ZoZci3^I6FiЋb&W =J!{_D7˘X>eP=Bʴ oT:XXRI_"&洞{Oq>eK+rSU,Ć~& Pl] k9U1(B1~m14~D*JrtimbC*2CHDjM kK oS'$ r4Sml"OR5c&Ǐouvf&F?uoQU)~pɏ)O&E?i\X ?eSEzeV{ ՗6vUE7OWRi7 qQ,omӸ7h jvZ& u3CqM*vciu-Z+%)c}'m#?r~E3Z4cr'搀h;#3hYUf&F&ک5(C_%yoLԠR- Ŀ{̘aB54e87P_\La(00 q̵S_8z0[Ǫ j }k{607~à~8?j|5ՆV+_W6`bK?f8IHbhbeL10%;-M4{Xg|H)|)&k`͟8IHs 3Cw,~8`M4MPǂO§ii`@jA|i mG1X[#Z*߁]9(E0jMk?c2r߾8&jQY ֬2co4-=C?Fwl cVHnSh3i#z\Rf$-kK зFW)Z_<d$%ŵMHxQ-K ;&kYјT%[v6#~īeK5ta-[k?: [sX@뫅_h9u8-Z`eC?E:~_OѦ%MZ 1~$6|S9̫M *]Hzb3Z]ãCcK;omli>ThRF;hR'n_ײ2G'j[jb`miCR)3&aCپ#-%&C үASٚSGeidbC?!7Mc1e?Y}a;痉z@~ePvT' n4˅G5,g,*㼊΢NJ{2ԟA@inJSV#M. ֧zqAV(-J.<%W }3VM] w^k.zce|n!Cqf(p t0 \Wʶ8v4rj;Z_Wkjى B_n83G{S;πS Xk'Mٍ&zoZ4}F*J4J'wއ17 3!yѭW@3fgsZ!vWEr#bEB} ux8lXsxA+o2N|?n{WMϑODÕM|/9Z4 <^39 Wf@$ctINϔ?"n^G.Bˑ F6 Fe p?LP#'-6< IsjBd6fW=lkp֬\ A2^7+AϘm>Pi  +T(wno .(^miVΌK\>mycBHCOnv;۲B"UhSHQSd 蕨mgxl^M57gw ؒ?ı"ο*S+಴KWpzDSHfv/1isC ( pWAs:7@qeԹo e&4;+'Zl=4n:9*Q\n< u@?keS ]$!'I,Yr\ǧ)[gUNsE2\]Y+rWM^>^?5Ԕq#xh%~~^شwp$&j#ۃ`%&\}\+,º럈Z*o)@(43愝c>Vr\ցr !HY6n$]GY }B+!U6bE=e':3Oy)KvU!HfLܺ=#"?]*7lKq @[}Y7R B,p?6u7&@ӶD&o@ _Ap"t.9vqŸr}j7'6Ͼ9ٳTB4c!dQXGpQd<7tJ? n{.x!`ܡuj +XXW$}wk|px^l)0l]n ._Ce u_?<@my's1H#([({-vM@]Д[!1Y|ꎛWОz.MZ%d] 3$Y\cumKAk'mУɌ>gaG{7AU+smUjངVqe:NRܲK^Ƒ욑1go}^oݓC\yUIf"rIu3^AV@%D9>2"i}#nN*r>~o7g\:.V+bpq=5i0J1-QuC33dണw++k uU ]R. 9B^,Ok=]!{ٵ%`$u;Ga,MqSݞL|߄);i8=_x(+ءFr,#"YLBt4+&D(Z6I&Ig^y&p ҨnxqNr W㊲gvG hOBقGv v BlUL|bߤ%2,w=qMwiל\+|ރwsIWLO: m>{n˯8INK@ҬuʲvEثTgor~Q]m..RОV nJk|M _RuObJmJ$zж%k5 [рηuA2 hNw'hxߝ" ܸjށ1S )pYrѠ a~kl>V20x+RA1; Kw-lw{Gv~* F}a]"=TyOhB_ܕyVYۯኁ u ݗזA`92G W;< xb͒ 7kl: ONCB*60-t3e3ɧLiFAg˶8_hv[>[jғ@U& [CC_ %HAdXy{ʙ4DR@P] ۞qycϭBsN%BDeh̜ZR/;*g(=[Ua%-(<N)l@^z)~ },G !ʆ[b Xva+ZXg{,9fp]f MmԹ^myµ/ pM5,Cs!L ߩ_VyE=^<''~m 3;ۖs5 TdThYQ~s>\IS7NyTТB]U^֖֔%5!\GsʥI\sk?ywDWDity[ٴPKTvOjiJz@u=DBbhVjmà4\H-L%m߆=j96 fA/i pej4HX3 8ˮ p.ZdPȽՔ-j>E^ù g-( 0(H,_"!yhd2Jރm{|QۥL}:yn-hh}p U:yC{H4~pJGXVFZ}v.ANFm@[qۯN9r?gFWSE7[Z^‘c(T޺Fb -iܟ綘{k]b-va˂.J)N0:cYQxl{- {d5ᚷ஧.ܵiHķC WAh8?EKD]QfY{{c]q NI,+ZOےPNMRv'dgX_D !+b7ܩ LJ@p. 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}Ƭs'@_h{a%'qNߜ Shgx'cB!S2=;!6=vszBN8:^2qcF1c+ k/ŤKc|3=>L=p< $d!2~O0yH: }"qvM9׿[kn뮠z}q/Kb.e5]kӐy_ c>蛙3>rc s/ꅬ'SC-N';MͿOQGjfgCDOCnMJ?Bw&0V=xM@u&3gklozNqkc0k8J`#gO`ݠkyYti!yA8|PY3sq3 iюqy+p~[5 /dڄ,^38eĵcǢ}C#h[=9c0&gȱΚ˝}rMJ_(6>-Џ !kօ>ż)sO{Žd Fq}kƀ8i|uƭc|6~;9tYGM@8ͯcepι\8Yȇw}q| a|X4scޥ-r=S~]@ -׹a~n|\ɼyvs-7>s:ڛ1~/ss>f-y'kIe͇A cg;ٻ5E?Z xO`j >ײh[oYY mP;M <2 u?ے{e?7 8gņ gw>q,sL-'rˠMgGϏo>g'3>5 ʸ|/m6`9 6A?J2ofMap/U@&}Mоg7_?0pwzE1e Srq}5{.1 "[b2+< >;c73+^j|a|NЁe|̜ų)&Ǭ@F2.a,;,R&țZy:bz8xa7̡Zwb;_"?;sY*_v5A'C3) ȋuN%Xo \ l/63 .ffwhqhg\s<xr3&,;6wo֗xsOk1~ʶZ45kn[Wsy?g ZGAGϱg[7c_̧CC0Nd}Nz!|児ow`Yk h6/%ڃo6ue|mc]I}Z_G1wt|_}%~9z'cًE~ȸ1כ>4k֥=NȯIhyqxVCfZav1x^KqXxc+NڋV_x1nՋo31} } \author{ Paul Bliese \email{pdbliese@gmail.com} } \references{ Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability: Implications for data aggregation and analysis. In K. J. Klein & S. W. Kozlowski (Eds.), Multilevel Theory, Research, and Methods in Organizations (pp. 349-381). San Francisco, CA: Jossey-Bass, Inc. Bliese, P. D., Maltarich, M. A., Hendricks, J. L., Hofmann, D. A., & Adler, A. B. (2019). Improving the measurement of group-level constructs by optimizing between-group differentiation. Journal of Applied Psychology, 104, 293-302. } \seealso{ \code{\link{ICC1}} \code{\link{sim.mlcor}} } \examples{ \dontrun{ set.seed(1535324) ICC.SIM<-sim.icc(gsize=10,ngrp=100,icc1=.15) ICC1(aov(VAR1~as.factor(GRP), ICC.SIM)) # 4 items with no level-1 correlation set.seed(15324) #items with no level-1 (within) correlation ICC.SIM<-sim.icc(gsize=10,ngrp=100,icc1=.15,nitems=4) mult.icc(ICC.SIM[,2:5],ICC.SIM$GRP) with(ICC.SIM,waba(VAR1,VAR2,GRP))$Cov.Theorem #Examine CorrW # 4 items with a level-1 (within) correlation of .30 set.seed(15324) ICC.SIM<-sim.icc(gsize=10,ngrp=100,icc1=.15,nitems=4, item.cor=.3) mult.icc(ICC.SIM[,2:5],ICC.SIM$GRP) with(ICC.SIM,waba(VAR1,VAR2,GRP))$Cov.Theorem #Examine CorrW } } \keyword{datagen}multilevel/man/sobel.Rd0000644000176200001440000000360214211431563014603 0ustar liggesusers\name{sobel} \alias{sobel} \title{Estimate Sobel's (1982) Test for Mediation} \description{Estimate Sobel's (1982) indirect test for mediation. The function provides an estimate of the magnitude of the indirect effect, Sobel's first-order estimate of the standard error associated with the indirect effect, and the corresponding z-value. The estimates are based upon three models as detailed on page 84 of MacKinnon, Lockwood, Hoffman, West and Sheets (2002).} \usage{ sobel(pred,med,out) } \arguments{ \item{pred}{The predictor or independent variable (X).} \item{med}{The mediating variable (M).} \item{out}{The outcome or dependent variable (Y).} } \value{ \item{Mod1: Y~X}{A summary of coefficients from Model 1 of MacKinnon et al., (2002).} \item{Mod2: Y~X+M}{A summary of coefficients from Model 2 of MacKinnon et al., (2002).} \item{Mod3: M~X}{A summary of coefficients from Model 3 of MacKinnon et al., (2002).} \item{Indirect.Effect}{The estimate of the indirect mediating effect.} \item{SE}{Sobel's (1982) Standard Error estimate.} \item{z.value}{The estimated z-value.} \item{N}{The number of observations used in model estimation.} } \references{MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104. Sobel, M. E., (1982). Asymptotic confidence intervals for indirect effects in structural equation models. In S. Leinhardt (Ed.), Sociological Methodology 1982 (pp. 290-312). Washington, DC: American Sociological Association.} \author{ Paul Bliese \email{pdbliese@gmail.com}} \examples{ data(bh1996) #A small but significant indirect effect indicates leadership mediates #the relationship between work hours and well-being. sobel(pred=bh1996$HRS,med=bh1996$LEAD,out=bh1996$WBEING) } \keyword{htest} multilevel/man/rgr.ols.Rd0000644000176200001440000000244214211437403015065 0ustar liggesusers\name{rgr.ols} \alias{rgr.ols} \title{Random Group Resampling OLS Regression} \description{Uses Random Group Resampling (RGR) within an Ordinary Least Square (OLS) framework to contrast actual group results with pseudo group results. This specific function performs an RGR on an OLS hierarchical OLS model with two predictors as in Bliese & Halverson (2002). To run this analysis on data with more predictors, the function would have to be modified.} \usage{ rgr.ols(xdat1,xdat2,ydata,grpid,nreps) } \arguments{ \item{xdat1}{The first predictor.} \item{xdat2}{The second predictor.} \item{ydata}{The outcome.} \item{grpid}{The group identifier.} \item{nreps}{The number of pseudo groups to create.} } \value{A matrix containing mean squares. Each row provides mean square values for a single pseudo group iteration} \references{Bliese, P. D., & Halverson, R. R. (2002). Using random group resampling in multilevel research. Leadership Quarterly, 13, 53-68.} \author{Paul Bliese \email{pdbliese@gmail.com}} \seealso{\code{\link{mix.data}}} \examples{ data(lq2002) RGROUT<-rgr.ols(lq2002$LEAD,lq2002$TSIG,lq2002$HOSTILE,lq2002$COMPID,100) #Compare values to those reported on p.62 in Bliese & Halverson (2002) summary(RGROUT) } \keyword{attribute} multilevel/man/chen2005.Rd0000644000176200001440000000342014211434746014727 0ustar liggesusers\name{chen2005} \docType{data} \alias{chen2005} \title{Data from Chen (2005)} \description{Contains the complete data used in Chen (2005). Chen (2005) examined newcomer adaptation in 65 project teams. The level of analysis was the team. In the study, team leaders assessed the initial team performance (TMPRF) at time 1 and then assessed newcomer performance over three additional time points (NCPRF.T1, NCPRF.T2, NCPRF.T3). Initial team expectations (TMEXP) and initial newcomer empowerment (NCEMP) were also assessed and modeled, but were not analyzed as repeated measures. To specify Table 2 model in Chen (2005), these data need to be converted to univariate or stacked form (see the make.univ function). Using the default values of make.univ and creating a dataframe called chen2005.univ, the specific lme model for Table 2 in Chen (2005) is: lme(MULTDV~NCEMP*TIME+TMEXP*TIME+TMPRF*TIME,random=~TIME|ID,chen2005.univ) } \usage{data(chen2005)} \format{A data frame with 7 columns and 65 team-level observations \tabular{llll}{ [,1] \tab ID \tab numeric \tab Team Identifier\cr [,2] \tab TMPRF \tab numeric \tab Initial Team Performance (time 1 in article)\cr [,3] \tab TMEXP \tab numeric \tab Team Expectations (time 1 in article)\cr [,4] \tab NCEMP \tab numeric \tab Initial Newcomer Empowerment(time 2 in article)\cr [,5] \tab NCPRF.T1 \tab numeric \tab Newcomer Performance Time 1 (time 2 in article)\cr [,6] \tab NCPRF.T2 \tab numeric \tab Newcomer Performance Time 2 (time 3 in article)\cr [,7] \tab NCPRF.T3 \tab numeric \tab Newcomer Performance Time 3 (time 4 in article)\cr } } \references{ Chen, G.(2005). Newcomer adaptation in teams: Multilevel antecedents and outcomes. Academy of Management Journal, 48, 101-116. } \keyword{datasets}multilevel/man/rgr.waba.Rd0000644000176200001440000000521114211440327015176 0ustar liggesusers\name{rgr.waba} \alias{rgr.waba} \title{Random Group Resampling of Covariance Theorem Decomposition} \description{Performs the covariance theorem decomposition of a raw correlation in situations where lower-level entities (individuals) are nested in higher-level groups (see Dansereau, Alutto & Yammarino, 1984; Robinson, 1950). Builds upon previous work by incorporating Random Group Resampling or RGR. RGR is used to randomly assign individuals to pseudo groups and create a sampling distributions of the covariance theorem components. The sampling distribution provides a way to contrast actual group covariance components to pseudo group covariance components. Note that rgr.waba is computationally intensive. } \usage{ rgr.waba(x, y, grpid, nrep) } \arguments{ \item{x}{A vector representing one variable for the correlation.} \item{y}{A vector representing the other variable for the correlation.} \item{grpid}{A vector identifying the groups from which X and Y originated.} \item{nrep}{The number of times that the entire data set is reassigned to pseudo groups} } \value{ Returns an object of class rgr.waba. The object is a list containing each random run for each component of the covariance theorem. } \author{ Paul Bliese \email{pdbliese@gmail.com} } \references{ Bliese, P. D. & Halverson, R. R. (1996). Individual and nomothetic models of job stress: An examination of work hours, cohesion, and well- being. Journal of Applied Social Psychology, 26, 1171-1189. Bliese, P. D., & Halverson, R. R. (2002). Using random group resampling in multilevel research. Leadership Quarterly, 13, 53-68. Dansereau, F., Alutto, J. A., & Yammarino, F. J. (1984). Theory testing in organizational behavior: The varient approach. Englewood Cliffs, NJ: Prentice-Hall. Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15, 351-357. } \seealso{ \code{\link{waba}} } \examples{ # This example is from Bliese & Halverson (1996). Notice that all of the # values from the RGR analysis differ from the values based on actual # group membership. Confidence intervals for individual components can # be estimated using the quantile command. In practice, the nrep option # should be more than 100 data(bh1996) #estimate the actual group model waba(bh1996$HRS,bh1996$WBEING,bh1996$GRP) #create 100 pseudo group runs and summarize the model RWABA<-rgr.waba(bh1996$HRS,bh1996$WBEING,bh1996$GRP,nrep=100) summary(RWABA) #Estimate 95th percentile confidence intervals (p=.05) quantile(RWABA,c(.025,.975)) } \keyword{attribute}multilevel/man/paneldat.Rd0000644000176200001440000000405614211433567015301 0ustar liggesusers\name{paneldat} \docType{data} \alias{paneldat} \title{Firm-Level Panel Data from Donald J. (DJ) Schepker} \description{ Contains a random sample of firm-level data from 196 firms for the years 2007 to 2011 based on data from COMPUSTAT and MSCI. The data are balanced in that each firm provided five years of data. The data contains a time variable and time varying covariates for the discontinuous growth model along with within-firm predictors related to the composition of boards of directors. The discontinuity was indexed to 2009 as the first year following the great recession. In addition to illustrating variants of growth models, the panel data is useful for contrasting econometric fixed-effect models and mixed-effect models (Bliese et al., 2020). } \usage{data(paneldat)} \format{A data frame with 14 columns and 960 observations \tabular{llll}{ [,1] \tab companyid \tab numeric \tab Firm ID\cr [,2] \tab companyname \tab numeric \tab Firm Name\cr [,3] \tab year \tab numeric \tab Calendar Year\cr [,4] \tab time \tab numeric \tab Calendar Year minus 2007\cr [,5] \tab trans \tab numeric \tab Dummy Coded Variable Indexed to Transition\cr [,6] \tab post \tab numeric \tab Time Varying Covariate for Post Transition Slope\cr [,8] \tab roani \tab numeric \tab Return on Assets\cr [,9] \tab boardindprop \tab numeric \tab Yearly Proportion of Independent Members of the Firm's Board\cr [,10] \tab dirageavg \tab numeric \tab Yearly Average Age of the Firm's Directors\cr [,11] \tab femaledirsprop \tab numeric \tab Yearly Female Board Members Proportion\cr [,12] \tab debtassets \tab numeric \tab Yearly Debt to Assets Ratio\cr [,13] \tab lnemp \tab numeric \tab Yearly Natural Log of Number of Employees\cr [,14] \tab ceotenure \tab numeric \tab Yearly CEO Tenure\cr } } \references{ Bliese, P. D., Schepker, D. J., Essman, S. M., & Ployhart, R. E. (2020). Bridging methodological divides between macro- and microresearch: Endogeneity and methods for panel data. Journal of Management, 46, 70-99. } \keyword{datasets}multilevel/man/tankdat.Rd0000644000176200001440000000304414211432626015126 0ustar liggesusers\name{tankdat} \docType{data} \alias{tankdat} \title{Tank data from Bliese and Lang (2016)} \description{ A partial sample of data collected by Lang and reported in Lang and Bliese (2009). The tankdat sub-sample was used as an example of discontinuous growth modeling in Bliese and Lang (2016). The data set is in long (univariate) format, and contains performance data from 184 participants over 12 repeated measures on a complex tank simulation task. In the research paradigm, the task was unexpectedly changed after the first six performance episodes. Discontinuous growth models were used to examine participants' reactions to the unexpected change. The data set contains the person-level predictor of conscientiousness. } \usage{data(tankdat)} \format{A dataframe with 4 columns and 2208 observations \tabular{llll}{ [,1] \tab ID \tab numeric \tab Participant ID\cr [,2] \tab CONSC \tab numeric \tab Participant Conscientiousness\cr [,3] \tab TIME \tab numeric \tab Time\cr [,4] \tab SCORE \tab numeric \tab Task Performance\cr } } \references{ Bliese, P. D., & Lang, J. W. B. (2016). Understanding relative and absolute change in discontinuous growth models: Coding alternatives and implications for hypothesis testing. Organizational Research Methods, 19, 562-592. Lang, J. W. B., & Bliese, P. D. (2009). General mental ability and two types of adaptation to unforeseen change: Applying discontinuous growth models to the task-change paradigm. Journal of Applied Psychology, 92, 411-428. } \keyword{datasets}multilevel/man/waba.Rd0000644000176200001440000000610414211440417014407 0ustar liggesusers\name{waba} \alias{waba} \title{Covariance Theoreom Decomposition of Bivariate Two-Level Correlation} \description{Performs the covariance theorem decomposition of a raw correlation in situations where lower-level entities (individuals) are nested in higher-level groups (see Robinson, 1950). Dansereau, Alutto and Yammarino (1984) refer to the variance decomposition as "Within-And-Between-Analysis II" or "WABA II". The waba function decomposes a raw correlation from a two-level nested design into 6 components. These components are (1) eta-between value for X, (2) eta-between value for Y, (3) the group-size weighted group-mean correlation, (4) the within-eta value for X, (5) the within-eta value for Y, and (6) the within-group correlation between X and Y. The last value represents the correlation between X and Y after each variable has been group-mean centered (demeaned). The program is designed to automatically perform listwise deletion on missing values; consequently, users should pay attention to the diagnostic information (Number of Groups and Number of Observations) provided as part of the output. Note that Within-And-Between-Analysis proposed by Dansereau et al. involves more than covariance theorem decomposition of correlations. Specifically, WABA involves decision rules based on eta-values. These are not replicated in the R multilevel library because the eta based decision rules have been shown to be highly related to group size (Bliese, 2000; Bliese & Halverson, 1998), a factor not accounted for in the complete Within-And-Between-Analysis methodology. } \usage{ waba(x, y, grpid) } \arguments{ \item{x}{A vector representing one variable in the correlation.} \item{y}{A vector representing the other variable in the correlation.} \item{grpid}{A vector identifying the groups from which x and y originated.} } \value{ Returns a list with three elements. \item{Cov.Theorem}{A 1 row dataframe with all of the elements of the covariance theorem.} \item{n.obs}{The number of observations used to calculate the covariance theorem.} \item{n.grps}{The number of groups in the data set.} } \author{ Paul Bliese \email{pdbliese@gmail.com} } \references{ Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability: Implications for data aggregation and Analysis. In K. J. Klein & S. W. Kozlowski (Eds.), Multilevel Theory, Research, and Methods in Organizations (pp. 349-381). San Francisco, CA: Jossey-Bass, Inc. Bliese, P. D., & Halverson, R. R. (1998). Group size and measures of group-level properties: An examination of eta-squared and ICC values. Journal of Management, 24, 157-172. Dansereau, F., Alutto, J. A., & Yammarino, F. J. (1984). Theory testing in organizational behavior: The varient approach. Englewood Cliffs, NJ: Prentice-Hall. Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15, 351-357. } \seealso{ \code{\link{rgr.waba}} } \examples{ data(bh1996) waba(bh1996$HRS,bh1996$WBEING,bh1996$GRP) } \keyword{attribute} multilevel/man/quantile.disagree.sim.Rd0000644000176200001440000000264014211421427017671 0ustar liggesusers\name{quantile.disagree.sim} \alias{quantile.disagree.sim} \title{S3 method for class 'disagree.sim'} \description{ Provides a concise quantile summary of objects created using the function ad.m.sim. The simulation functions for the average deviation of the mean (or median) return a limited number of estimated values. Consequently, the normal quantile methods are biased. The quantile methods incorporated in this function produce unbiased estimates. } \usage{ \method{quantile}{disagree.sim}(x,confint,\dots) } \arguments{ \item{x}{An object of class 'disagree.sim'.} \item{confint}{The confidence intervals to return. The values of 0.05 and 0.01 return the approximate 5 percent and 1 percent confidence intervals. Values equal to or smaller than these values are significant (p=.05, p=.01).} \item{\dots}{Optional arguments. None used.} } \value{A dataframe with two columns. The first column contains the quantile value and the second contains the estimate based on the object. } \author{ Paul Bliese \email{pdbliese@gmail.com} } \seealso{ \code{\link{ad.m.sim}} } \examples{ #Example from Dunlap et al. (2003), Table 3. The listed significance #value (p=.05) for a group of size 5 with a 7-item response format is #0.64 or less. SIMOUT<-ad.m.sim(gsize=5, nitems=1, nresp=7, itemcors=NULL, type="mean", nrep=1000) quantile(SIMOUT, c(.05,.01)) } \keyword{programming}multilevel/man/make.univ.Rd0000644000176200001440000000446414211417770015407 0ustar liggesusers\name{make.univ} \alias{make.univ} \title{Convert data from multivariate to univariate form} \description{ Longitudinal data is often stored in multivariate or wide form. In multivariate form, each row contains data from one subject, and repeated measures variables are indexed by different names (e.g., OUTCOME.T1, OUTCOME.T2, OUTCOME.T3). In repeated measures designs and growth modeling, data needs to be converted to univariate or stacked form where each row represents one of the repeated measures indexed by a TIME variable nested within subject. In univariate form, each subject has as many rows of data as there are time points. R has several functions to convert data from wide to long formats and vice versa including reshape. The code used in make.univ borrows heavily from code provided in Chambers and Hastie (1991). } \usage{ make.univ(x,dvs,tname="TIME", outname="MULTDV") } \arguments{ \item{x}{A dataframe in multivariate form.} \item{dvs}{A subset dataframe of x containing the repeated measures columns. Note that the repeated measures must be ordered from Time 1 to Time N for this function to work properly.} \item{tname}{An optional name for the new time variable. Defaults to TIME.} \item{outname}{An optional name for the outcome variable name. Defaults to MULTDV.} } \value{ Returns a dataframe in univariate (i.e., stacked) form with a TIME variable representing the repeated observations, and a variable named MULTDV representing the time-indexed variable. The TIME variable begins with 0. } \author{ Paul Bliese \email{pdbliese@gmail.com} } \references{ Bliese, P. D., & Ployhart, R. E. (2002). Growth modeling using random coefficient models: Model building, testing and illustrations. Organizational Research Methods, 5, 362-387. Chambers, J. M., & Hastie, T. J. (1991). Statistical models in S. CRC Press, Inc.. } \seealso{ \code{\link{mult.make.univ}} \code{\link{reshape}} } \examples{ data(univbct) #a dataframe in univariate form for job satisfaction TEMP<-univbct[3*1:495,c(22,1:17)] #convert back to multivariate form #Transform data to univariate form TEMP2<-make.univ(x=TEMP,dvs=TEMP[,c(10,13,16)]) #Same as above, but renaming repeated variable TEMP3<-make.univ(x=TEMP,dvs=TEMP[,c(10,13,16)],outname="JOBSAT") } \keyword{reformat}multilevel/man/rgr.agree.Rd0000644000176200001440000000455514211424315015360 0ustar liggesusers\name{rgr.agree} \alias{rgr.agree} \title{Random Group Resampling for Within-group Agreement} \description{Uses random group resampling (RGR) to estimate within group agreement. RGR agreement compares within group variances from actual groups to within group variances from pseudo groups. Evidence of significant agreement is inferred when variances from the actual groups are significantly smaller than variances from pseudo groups. RGR agreement methods are rarely reported, but provide another way to consider group level properties in data. } \usage{ rgr.agree(x, grpid, nrangrps) } \arguments{ \item{x}{A vector upon which to estimate agreement.} \item{grpid}{A vector identifying the groups from which x originated (actual group membership).} \item{nrangrps}{A number representing the number of random groups to generate. Note that the number of random groups created must be directly divisible by the number of actual groups to ensure that group sizes of pseudo groups and actual groups are identical. The rgr.agree routine will generate the number of pseudo groups that most closely approximates nrangrps given the group size characteristics of the data.} } \value{An object of class 'rgr.agree' with the following components: \item{NRanGrp}{The number of random groups created.} \item{AvRGRVar}{The average within-group variance of the random groups.} \item{SDRGRVar}{Standard deviation of random group variances used in the z-score estimate.} \item{zvalue}{Z-score difference between the actual group and random group variances.} \item{RGRVARS}{The random group variances.} } \author{ Paul Bliese \email{pdbliese@gmail.com} } \references{ Bliese, P. D., & Halverson, R. R. (2002). Using random group resampling in multilevel research. Leadership Quarterly, 13, 53-68. Bliese, P.D., Halverson, R. R., & Rothberg, J. (2000). Using random group resampling (RGR) to estimate within-group agreement with examples using the statistical language R. Walter Reed Army Institute of Research. Ludtke, O. & Robitzsch, A. (2009). Assessing within-group agreement: A critical examination of a random-group resampling approach. Organizational Research Methods, 12, 461-487. } \seealso{ \code{\link{rwg}} \code{\link{rwg.j}} } \examples{ data(bh1996) RGROUT<-rgr.agree(bh1996$HRS,bh1996$GRP,1000) summary(RGROUT) } \keyword{attribute}multilevel/man/summary.disagree.sim.Rd0000644000176200001440000000155714211432136017551 0ustar liggesusers\name{summary.disagree.sim} \alias{summary.disagree.sim} \title{S3 method for class 'disagree.sim'} \description{Provides a concise summary of objects created using the function ad.m.sim. } \usage{ \method{summary}{disagree.sim}(object,\dots) } \arguments{ \item{object}{An object of class 'disagree.sim'.} \item{\dots}{Optional additional arguments. None used.} } \value{A summary of all the output elements in the disagree.sim class object.} \author{ Paul Bliese \email{pdbliese@gmail.com} } \seealso{ \code{\link{ad.m.sim}} } \examples{ #Example from Dunlap et al. (2003), Table 3. The listed significance #value for a group of size 5 with a 7-item response format is 0.64 or less. #Increase nrep in actual use SIMOUT<-ad.m.sim(gsize=5, nitems=1, nresp=7, itemcors=NULL, type="mean", nrep=500) summary(SIMOUT) } \keyword{programming}multilevel/man/ran.group.Rd0000644000176200001440000000204114211424122015377 0ustar liggesusers\name{ran.group} \alias{ran.group} \title{Randomly mix grouped data and return function results} \description{Called by rgr.agree (and potentially other functions). The ran.group function randomly mixes data and applies a function to the pseudo groups. Pseudo group IDs match real group IDs in terms of size.} \usage{ ran.group(x,grpid,fun,...) } \arguments{ \item{x}{A matrix or vector containing data to be randomly sorted.} \item{grpid}{A vector containing a group identifier.} \item{fun}{A function to be applied to the observations within each random group.} \item{...}{Additional arguments to fun.} } \value{A vector containing the results of applying the function to each random group.} \references{Bliese, P. D., & Halverson, R. R. (2002). Using random group resampling in multilevel research. Leadership Quarterly, 13, 53-68.} \author{ Paul Bliese \email{pdbliese@gmail.com}} \seealso{\code{\link{rgr.agree}}} \examples{ data(bh1996) ran.group(bh1996$HRS,bh1996$GRP,mean) } \keyword{programming} multilevel/man/quantile.agree.sim.Rd0000644000176200001440000000234514211421340017165 0ustar liggesusers\name{quantile.agree.sim} \alias{quantile.agree.sim} \title{S3 method for class 'agree.sim'} \description{ Provides a concise quantile summary of objects created using the functions rwg.sim and rwg.j.sim. The simulation functions for rwg and rwg.j return a limited number of estimated values. Consequently, the normal quantile methods are biased. The quantile methods incorporated in this function produce unbiased estimates. } \usage{ \method{quantile}{agree.sim}(x,confint,\dots) } \arguments{ \item{x}{An object of class 'agree.sim'.} \item{confint}{The confidence intervals to return. The values of 0.95 and 0.99 return the approximate 95th and 99th percentile confidence intervals (p=.05 and p=.01).} \item{\dots}{Optional arguments. None used.} } \value{A dataframe with two columns. The first column contains the quantile value and the second contains the estimate based on the object. } \author{ Paul Bliese \email{pdbliese@gmail.com} } \seealso{ \code{\link{rwg.sim}} \code{\link{rwg.j.sim}} } \examples{ #An example from Dunlap et al. (2003). The estimate from Dunlap et al. #Table 2 is 0.53 RWG.OUT<-rwg.sim(gsize=10,nresp=5,nrep=1000) quantile(RWG.OUT, c(.95,.99)) } \keyword{programming}multilevel/man/cordif.dep.Rd0000644000176200001440000000212114211413603015502 0ustar liggesusers\name{cordif.dep} \alias{cordif.dep} \title{Estimate whether two dependent correlations differ} \description{ Tests for statistical differences between two dependent correlations using the formula provided on page 56 of Cohen & Cohen (1983). The function returns a t-value, the DF, and the p-value. } \usage{ cordif.dep(r.x1y,r.x2y,r.x1x2,n) } \arguments{ \item{r.x1y}{The correlation between x1 and y where y is typically the outcome variable.} \item{r.x2y}{The correlation between x2 and y where y is typically the outcome variable.} \item{r.x1x2}{The correlation between x1 and x2 (the correlation between the two predictors).} \item{n}{The sample size.} } \value{ Returns three values. A t-value, DF and p-value. } \author{ Paul Bliese \email{pdbliese@gmail.com} } \references{ Cohen, J. & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd Ed.). Hillsdale, nJ: Lawrence Erlbaum Associates. } \seealso{ \code{\link{cordif}} } \examples{ cordif.dep(r.x1y=.30,r.x2y=.60,r.x1x2=.10,n=305) } \keyword{htest}multilevel/man/sherifdat.Rd0000644000176200001440000000445314211470101015444 0ustar liggesusers\name{sherifdat} \docType{data} \alias{sherifdat} \title{Sherif (1935) group data from 3 person teams} \description{ Contains estimates of movement length (in inches) of a light in a completely dark room. Eight groups of three individuals provided three estimates for a total of 72 observations. In four of the groups, participants first made estimates alone prior to providing estimates as a group. In the other four groups participants started as groups. Lang and Bliese (2019) used these data to illustrate how variance functions in mixed-effects models (lme) could be used to test whether groups displayed consensus emergence. Data were obtained from https://brocku.ca/MeadProject/Sherif/Sherif_1935a/Sherif_1935a_3.html } \usage{data(sherifdat)} \format{A dataframe with 5 columns and 72 observations \tabular{llll}{ [,1] \tab person \tab numeric \tab Participant ID within a group\cr [,2] \tab time \tab numeric \tab Measurment Occasion\cr [,3] \tab group \tab numeric \tab Group Identifier \cr [,4] \tab y \tab numeric \tab Estimate of movement length in inches \cr [,5] \tab condition \tab numeric \tab Experimental Condition (0) starting as a group, (1) starting individually\cr } } \references{ Sherif, M. (1935). A study of some social factors in perception: Chapter 3. Archives of Psychology, 27, 23- 46. https://brocku.ca/MeadProject/Sherif/Sherif_1935a/Sherif_1935a_3.html Lang, J. W. B., & Bliese, P. D., (2019). A Temporal Perspective on Emergence: Using 3-level Mixed Effects Models to Track Consensus Emergence in Groups. In S. E. Humphrey & J. M. LeBreton (Eds.), The Handbook for Multilevel Theory, Measurement, and Analysis. Washington, DC: American Psychological Association Lang, J. W. B., Bliese, P. D., & Adler, A. B. (2019). Opening the Black Box: A Multilevel Framework for Studying Group Processes. Advances in Methods and Practices in Psychological Science, 2, 271-287. Lang, J. W. B., Bliese, P. D., & de Voogt, A. (2018). Modeling Consensus Emergence in Groups Using Longitudinal Multilevel Methods. Personnel Psychology, 71, 255-281. Lang, J. W. B., Bliese, P. D., & Runge, J. M. (2021). Detecting consensus emergence in organizational multilevel data: Power simulations. Organizational Research Methods, 24(2), 319-341. } \keyword{datasets}multilevel/man/cordif.Rd0000644000176200001440000000203214211413670014740 0ustar liggesusers\name{cordif} \alias{cordif} \title{Estimate whether two independent correlations differ} \description{ Tests for statistical differences between two independent correlations using the formula provided on page 54 of Cohen & Cohen (1983). The function returns a z-score estimate. } \usage{ cordif(rvalue1,rvalue2,n1,n2) } \arguments{ \item{rvalue1}{Correlation value from first sample.} \item{rvalue2}{Correlation value from second sample.} \item{n1}{The sample size of the first correlation.} \item{n2}{The sample size of the second correlation.} } \value{ Produces a single value, the z-score for the differences between the correlations. } \author{ Paul Bliese \email{pdbliese@gmail.com} } \references{ Cohen, J. & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd Ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. } \seealso{ \code{\link{rtoz}} \code{\link{cordif.dep}} } \examples{ cordif(rvalue1=.51,rvalue2=.71,n1=123,n2=305) } \keyword{htest}multilevel/man/dgm.code.Rd0000644000176200001440000001172214211465310015156 0ustar liggesusers\name{dgm.code} \alias{dgm.code} \title{Create Coding Matrix for Discontinuous Growth Model} \description{Creates time-varying covariates for estimating a discontinuous growth model (DGM). Creating time-varying covariates requires only simple recoding of a time vector when data are balanced and the discontinuity event occurs at the same time for each group. When data are not balanced and one or more events occurs at different times for each group, coding the time-varying covariates is complex. For instance, if the event of interest was employee turnover in a store in a given month, it is likely that stores would differ on how many months of data were available and on the specific months when turnover occurred. With these irregularly-timed events and different time windows for each store, it would be challenging to create time-varying covariates for the DGM specific to each store's circumstances.} \usage{ dgm.code(grp,time,event,n.events=FALSE,first.obs=FALSE) } \arguments{ \item{grp}{A vector representing the group ID in the panel data. Each group ID is repeated n times as represented in the panel.} \item{time}{A vector from 0:n-1 where n represents the number of observations within each panel. Within each group, the time vector can vary in length.} \item{event}{A vector containing 1 for each time the event of interest for a group occurs and a 0 in all other cases.} \item{n.events}{Controls how many events for which to create time-varying covariates. In cases where some groups encounter numerous events, this argument can be used to limit the coding to a small number (e.g., 2 or 3). Default is to create as many time related covariates as occur in the group with the most events (which is often not useful).} \item{first.obs}{Controls what to do if the first observation is an event. If TRUE, then the first observation is changed to a zero and treated as a non-event. If FALSE, the function provides a list of the groups where the event is the first observation. The reason why the first observation is flagged is that it is not possible to estimate a discontinuous growth model if the event occurs on the first observation.} } \value{Produces a data.frame with the columns [, c("grp","time","event","trans1","post1","time.a","tot.events","event.first")] If numerous events are coded, the data.frame will contain more trans and post values (trans2, post2, etc.) corresponding to the maximum number of events experienced by a group unless n.events is set to limit the number of events. The data.frame must be merged back to the original data.frame for subsequent analyses. See examples.} \references{Bliese, P. D., & Lang, J. W. B. (2016). Understanding relative and absolute change in discontinuous growth models: Coding alternatives and implications for hypothesis testing. Organizational Research Methods, 19, 562-592. Bliese, P. D., Kautz, J., & Lang, J. W. (2020). Discontinuous growth models: Illustrations, recommendations, and an R function for generating the design matrix. In Y. Griep & S. D. Hansen (Eds.), Handbook on the Temporal Dynamics of Organizational Behavior (pp. 319-350). Northampton, MA: Edward Elgar Publishers, Inc.} \author{Paul Bliese \email{pdbliese@gmail.com}} \examples{ ########## # Example 1: Coding trans, post and time.a in data where # every event occurs at the same time for each person ########## # Read data from library data(tankdat) # Add a marker (1 or 0) indicating an event tankdat$taskchange<-0 tankdat$taskchange[tankdat$TIME==6]<-1 # Run function with defaults OUT<-with(tankdat,dgm.code(ID,TIME,taskchange)) names(OUT) names(tankdat) # Merge original data and dgm codes and reorder tankdat.dgm<-merge(tankdat,OUT,by.x=c("ID","TIME"),by.y=c("grp","time")) tankdat.dgm<-tankdat.dgm[order(tankdat.dgm$ID,tankdat.dgm$TIME),] # Examine data tankdat.dgm[1:12,] ########## # Example 2: Coding trans, post and time.a in data where every transition # event occurs at the different times for each person ########## # Read data from library data(tankdat) # Add a marker (1 or 0) indicating an event at random set.seed(343227) tankdat$taskchange<-rbinom(nrow(tankdat),1,prob=.1) tankdat[1:24,] #ID 1 had one event at TIME 10. ID 2 had 3 events # Run function with defaults \dontrun{ OUT<-with(tankdat,dgm.code(ID,TIME,taskchange))} # returns an error showing the 24 groups that started with an event. # Either drop these groups or change the first.obs option to TRUE # which changes these first events to 0 (non-events) OUT<-with(tankdat,dgm.code(ID,TIME,taskchange,first.obs=TRUE)) OUT[1:24,] OUT[OUT$grp==9,] #Notice the event.first value of 1 for group 9 indicating that the #first value was present and recoded. # In the default setting, one ID had 4 events. It may be preferable # to restrict the number of events to 3 or more and code accordingly OUT<-with(tankdat,dgm.code(ID,TIME,taskchange,n.events=3,first.obs=TRUE)) OUT[1:24,] } \keyword{growth models}multilevel/man/univbct.Rd0000644000176200001440000000364714211433000015146 0ustar liggesusers\name{univbct} \docType{data} \alias{univbct} \title{Data from Bliese and Ployhart (2002)} \description{Contains the complete data set used in Bliese and Ployhart (2002) focused on Job Satisfation. The data is in univariate (i.e., stacked) form. Data were collected at three time points. } \usage{data(univbct)} \format{A data frame with 22 columns and 1485 observations from 495 individuals \tabular{llll}{ [,1] \tab BTN \tab numeric \tab BN Id\cr [,2] \tab COMPANY \tab numeric \tab Co Id\cr [,3] \tab MARITAL \tab numeric \tab Marital Status (1 single; 2 married; 3 separated; 4 divorced; 5 other)\cr [,4] \tab GENDER \tab numeric \tab Gender (1 male; 2 female)\cr [,5] \tab HOWLONG \tab numeric \tab Time in Unit \cr [,6] \tab RANK \tab numeric \tab Rank\cr [,7] \tab EDUCATE \tab numeric \tab Education\cr [,8] \tab AGE \tab numeric \tab Age\cr [,9] \tab JOBSAT1 \tab numeric \tab JOBSAT Time 1\cr [,10] \tab COMMIT1 \tab numeric \tab Commitment Time 1\cr [,11] \tab READY1 \tab numeric \tab Readiness Time 1\cr [,12] \tab JOBSAT2 \tab numeric \tab JOBSAT Time 2\cr [,13] \tab COMMIT2 \tab numeric \tab Commitment Time 2\cr [,14] \tab READY2 \tab numeric \tab Readiness Time 2\cr [,15] \tab JOBSAT3 \tab numeric \tab JOBSAT Time 3\cr [,16] \tab COMMIT3 \tab numeric \tab Commitment Time 3\cr [,17] \tab READY3 \tab numeric \tab Readiness Time 3\cr [,18] \tab TIME \tab numeric \tab 0 to 2 time maker\cr [,19] \tab JSAT \tab numeric \tab Jobsat in univariate form \cr [,20] \tab COMMIT \tab numeric \tab Commitment in univariate form\cr [,21] \tab READY \tab numeric \tab Readiness in univariate form \cr [,22] \tab SUBNUM \tab numeric \tab Subject number } } \references{ Bliese, P. D., & Ployhart, R. E. (2002). Growth modeling using random coefficient models: Model building, testing and illustrations. Organizational Research Methods, 5, 362-387.} \keyword{datasets}multilevel/man/lq2002.Rd0000644000176200001440000000654514211417547014436 0ustar liggesusers\name{lq2002} \docType{data} \alias{lq2002} \title{Data used in special issue of Leadership Quarterly, Vol. 13, 2002} \description{ Contains the complete data used in a special issue of Leadership Quarterly edited by Paul Bliese, Ronald Halverson and Chet Schriesheim in 2002 (Vol 13). Researchers from several universities analyzed this common dataset using various multilevel techniques. The three scales used in the analyses are Leadership Climate (LEAD), Task Significance (TSIG) and Hostility (HOSTILE). The data set contains each item making up these scales. These items were additionally used by Cohen, Doveh and Nahum-Shani (2009). } \usage{data(lq2002)} \format{A data frame with 27 columns and 2,042 observations from 49 groups \tabular{llll}{ [,1] \tab COMPID \tab numeric \tab Army Company Identifying Variable\cr [,2] \tab SUB \tab numeric \tab Subject Number\cr [,3] \tab LEAD01 \tab numeric \tab Officers Get Cooperation From Company (EXV01)\cr [,4] \tab LEAD02 \tab numeric \tab NCOs Get Cooperation From Company (EXV02)\cr [,5] \tab LEAD03 \tab numeric \tab Impressed By Leadership (EXV04)\cr [,6] \tab LEAD04 \tab numeric \tab Go For Help Within Chain of Command (EXV05)\cr [,7] \tab LEAD05 \tab numeric \tab Officers Would Lead Well In Combat (EXV07)\cr [,8] \tab LEAD06 \tab numeric \tab NCOs Would Lead Well In Combat (EXV08)\cr [,9] \tab LEAD07 \tab numeric \tab Officers Interested In Welfare (EXV11)\cr [,10] \tab LEAD08 \tab numeric \tab NCOs Interested In Welfare (EXV13)\cr [,11] \tab LEAD09 \tab numeric \tab Officers Interested In What I Think (EXV14)\cr [,12] \tab LEAD10 \tab numeric \tab NCOs Interested In What I Think (EXV15)\cr [,13] \tab LEAD11 \tab numeric \tab Chain Of Command Works Well (EXV16)\cr [,14] \tab TSIG01 \tab numeric \tab What I Am Doing Is Important (MIS05)\cr [,15] \tab TSIG02 \tab numeric \tab Making Contribution To Mission (MIS06)\cr [,16] \tab TSIG03 \tab numeric \tab What I Am Doing Accomplishes Mission (MIS07)\cr [,17] \tab HOSTIL01 \tab numeric \tab Easily Annoyed Or Irritated (BSI09)\cr [,18] \tab HOSTIL02 \tab numeric \tab Temper Outburst That You Cannot Control (BSI18)\cr [,19] \tab HOSTIL03 \tab numeric \tab Urges To Harm Someone (BSI47)\cr [,20] \tab HOSTIL04 \tab numeric \tab Urges To Break Things (BSI49)\cr [,21] \tab HOSTIL05 \tab numeric \tab Getting Into Frequent Arguments (BSI54)\cr [,22] \tab LEAD \tab numeric \tab Leadership Climate Scale Score\cr [,23] \tab TSIG \tab numeric \tab Task Significance Scale Score\cr [,24] \tab HOSTILE \tab numeric \tab Hostility Scale Score\cr [,25] \tab GLEAD \tab numeric \tab Leadership Climate Scale Score Aggregated By Company\cr [,26] \tab GTSIG \tab numeric \tab Task Significance Scale Score Aggregated By Company\cr [,27] \tab GHOSTILE \tab numeric \tab Hostility Scale Score Aggregated By Company } } \references{ Bliese, P. D., & Halverson, R. R. (2002). Using random group resampling in multilevel research. Leadership Quarterly, 13, 53-68. Bliese, P. D., Halverson, R. R., & Schriesheim, C. A. (2002). Benchmarking multilevel methods: Comparing HLM, WABA, SEM, and RGR. Leadership Quarterly, 13, 3-14. Cohen, A., Doveh, E., & Nahum-Shani, I. (2009). Testing agreement for multi-item scales with the indices rwg(j) and adm(j). Organizational Research Methods, 12, 148-164. } \keyword{datasets} multilevel/man/rwg.j.sim.Rd0000644000176200001440000000714214211440705015316 0ustar liggesusers\name{rwg.j.sim} \alias{rwg.j.sim} \title{Simulate rwg(j) values from a random null distribution} \description{Based on the work of Cohen, Doveh and Eick (2001) and Cohen, Doveh and Nahum-Shani (2009). Draws data from a random uniform null distribution and calculates the James, Demaree and Wolf (1984) within group agreement measure rwg(j) for multiple item scales. By repeatedly drawing random samples, a null distribution of the rwg(j) is generated. The null sampling distribution can be used to calculate confidence intervals for different combinations of group sizes and number of items (J). Users provide the number of scale response options (A) and the number of random samples. By default, items (J) drawn in the simulation are independent (non-correlated); however, an optional argument (itemcors) allows the user to specify a correlation matrix with relationships among items. Cohen et al. (2001) show that values of rwg(j) are primarily a function of the number of items and the group size and are not strongly influenced by correlations among items; nonetheless, assuming correlations among items is more realistic and thereby is a preferred model (see Cohen et al., 2009). } \usage{ rwg.j.sim(gsize, nitems, nresp, itemcors=NULL, nrep) } \arguments{ \item{gsize}{Group size used in the rwg(j) simulation.} \item{nitems}{The number of items (J) in the multi-item scale on which to base the simulation. If itemcors are provided, this is an optional argument as nitems will be calculated from the correlation matrix.} \item{nresp}{The number of response options for the J items in the simulation (e.g., there would be 5 response options if using Strongly Disagree, Disagree, Neither, Agree, Strongly Agree).} \item{itemcors}{An optional argument containing a correlation matrix with the item correlations.} \item{nrep}{The number of rwg(j) values to simulate. This will generally be 10,000 or more, but only 500 are used in the examples to reduce computational demands.} } \value{ \item{rwg.j}{rwg(j) value from each of the nrep simulations.} \item{gsize}{Simulation group size.} \item{nresp}{Simulated number of response options.} \item{nitems}{Number of items in the multiple item scale. Either provided in the call or calculated from the correlation matrix, if given.} \item{rwg.j.95}{95 percent confidence interval estimate associated with a p-value of .05. Values greater than or equal to the rwg.j.95 value are considered significant.} } \author{ Paul Bliese \email{pdbliese@gmail.com} } \references{ Cohen, A., Doveh, E., & Nahum-Shani, I. (2009). Testing agreement for multi-item scales with the indices rwg(j) and adm(j). Organizational Research Methods, 12, 148-164. Cohen, A., Doveh, E., & Eick, U. (2001). Statistical properties of the rwg(j) index of agreement. Psychological Methods, 6, 297-310. James, L.R., Demaree, R.G., & Wolf, G. (1984). Estimating within-group interrater reliability with and without response bias. Journal of Applied Psychology, 69, 85-98. } \seealso{ \code{\link{rwg.j}} \code{\link{rwg}} \code{\link{rwg.sim}} \code{\link{rwg.j.lindell}} \code{\link{rgr.agree}} } \examples{ #An example assuming independent items RWG.J.OUT<-rwg.j.sim(gsize=10,nitems=6,nresp=5,nrep=500) summary(RWG.J.OUT) quantile(RWG.J.OUT, c(.95,.99)) #A more realistic example assuming correlated items. The #estimate in Cohen et al. (2006) is .61. data(lq2002) RWG.J.OUT<-rwg.j.sim(gsize=10,nresp=5, itemcors=cor(lq2002[,c("TSIG01","TSIG02","TSIG03")]), nrep=500) summary(RWG.J.OUT) quantile(RWG.J.OUT,c(.95,.99)) } \keyword{attribute}multilevel/man/rtoz.Rd0000644000176200001440000000132314211425434014473 0ustar liggesusers\name{rtoz} \alias{rtoz} \title{Conducts an r to z transformation} \description{ Transforms a correlation (r) to a z variate using the formula provided on page 53 of Cohen & Cohen (1983). The formula is z=.5*((log(1+r))-(log(1-r))) where r is the correlation. } \usage{ rtoz(rvalue) } \arguments{ \item{rvalue}{The correlation to be z transformed.} } \value{The z transformation. } \author{ Paul Bliese \email{pdbliese@gmail.com} } \references{ Cohen, J. & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd Ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. } \seealso{ \code{\link{cordif}} } \examples{ rtoz(.84) } \keyword{htest}multilevel/man/boot.icc.Rd0000644000176200001440000000326514211434637015211 0ustar liggesusers\name{boot.icc} \alias{boot.icc} \title{Bootstrap ICC values in 2-level data} \description{ An experimental function that implements a 2-level bootstrap to estimate non-parametric bootstrap confidence intervals of the ICC1 using the percentile method. The bootstrap first draws a sample of level-2 units with replacement, and in a second stage draws a sample of level-1 observations with replacement from the level-2 units. Following each bootstrap replication, the ICC(1) is estimated using the lme function (default) or the ANOVA method. } \usage{ boot.icc(x, grpid, nboot, aov.est=FALSE) } \arguments{ \item{x}{A vector representing the variable upon which to estimate the ICC values.} \item{grpid}{A vector representing the level-2 unit identifier.} \item{nboot}{The number of bootstrap iterations. Computational demands underlying a 2-level bootstrap are heavy, so the examples use 100; however, the number of interations should generally be 10,000.} \item{aov.est}{An option to estimate the ICC values using aov.} } \value{Provides ICC(1) estimates for each bootstrap draw.} \author{ Paul Bliese \email{pdbliese@gmail.com} } \references{ Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability: Implications for data aggregation and analysis. In K. J. Klein & S. W. Kozlowski (Eds.), Multilevel Theory, Research, and Methods in Organizations (pp. 349-381). San Francisco, CA: Jossey-Bass, Inc.} \seealso{ \code{\link{ICC1}} \code{\link{ICC2}} \code{\link{sim.icc}} \code{\link{sim.mlcor}} } \examples{ \dontrun{ data(bh1996) ICC.OUT<-boot.icc(bh1996$WBEING,bh1996$GRP,100) quantile(ICC.OUT,c(.025,.975)) } } \keyword{attribute}multilevel/man/bhr2000.Rd0000644000176200001440000000467714211412235014564 0ustar liggesusers\name{bhr2000} \docType{data} \alias{bhr2000} \title{Data from Bliese, Halverson and Rothberg (2000)} \description{ The complete data used in Bliese, Halverson and Rothberg (2000). Contains 14 variables referencing individual ratings of US Army Company leadership, work hours, and the degree to which individuals find comfort from religion. The leadership and workhours variables are subsets of the Bliese and Halveson (1996) data (bh1996); however, in the case of leadership, the data set contains the 11 scale items whereas the bh1996 data set contains only the scale score. Most items are on a strongly disagree to strongly agree scale. The RELIG item is on a never to always scale. } \usage{data(bhr2000)} \format{A data frame with 14 columns and 5,400 observations from 99 groups \tabular{llll}{ [,1] \tab GRP \tab numeric \tab Group ID\cr [,2] \tab AF06 \tab numeric \tab Officers get willing and whole-hearted cooperation\cr [,3] \tab AF07 \tab numeric \tab NCOS most always get willing and whole-hearted cooperation\cr [,4] \tab AP12 \tab numeric \tab I am impressed by the quality of leadership in this company\cr [,5] \tab AP17 \tab numeric \tab I would go for help with a personal problem to the chain of command\cr [,6] \tab AP33 \tab numeric \tab Officers in this Company would lead well in combat\cr [,7] \tab AP34 \tab numeric \tab NCOs in this Company would lead well in combat\cr [,8] \tab AS14 \tab numeric \tab My officers are interested in my personal welfare\cr [,9] \tab AS15 \tab numeric \tab My NCOs are interested in my personal welfare\cr [,10] \tab AS16 \tab numeric \tab My officers are interested in what I think and feel about things\cr [,11] \tab AS17 \tab numeric \tab My NCOs are intested in what I think and fell about things\cr [,12] \tab AS28 \tab numeric \tab My chain-of-command works well\cr [,13] \tab HRS \tab numeric \tab How many hours do you usually work in a day\cr [,14] \tab RELIG \tab numeric \tab How often do you gain strength of comfort from religious beliefs } } \references{ Bliese, P. D. & Halverson, R. R. (1996). Individual and nomothetic models of job stress: An examination of work hours, cohesion, and well-being. Journal of Applied Social Psychology, 26, 1171-1189. Bliese, P. D., Halverson, R. R., & Rothberg, J. (2000). Using random group resampling (RGR) to estimate within-group agreement with examples using the statistical language R. } \keyword{datasets}multilevel/man/ICC2.Rd0000644000176200001440000000217614211417012014155 0ustar liggesusers\name{ICC2} \alias{ICC2} \title{Intraclass Correlation Coefficient 2 or ICC(2) from an aov model} \description{Calculates the Intraclass Correlation Coefficient 2 or ICC(2) from an ANOVA model. This value represents the reliability of the group means. } \usage{ ICC2(object) } \arguments{ \item{object}{An ANOVA (aov) object from an one-way analysis of variance.} } \value{Provides an estimate of ICC(2) for the sample.} \references{ Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability: Implications for data aggregation and Analysis. In K. J. Klein & S. W. Kozlowski (Eds.), Multilevel Theory, Research, and Methods in Organizations (pp. 349-381). San Francisco, CA: Jossey-Bass, Inc. Bartko, J.J. (1976). On various intraclass correlation reliability coefficients. Psychological Bulletin, 83, 762-765.} \author{ Paul Bliese \email{pdbliese@gmail.com} } \seealso{ \code{\link{ICC1}} \code{\link{aov}} \code{\link{sim.icc}} \code{\link{sim.mlcor}} } \examples{ data(bh1996) hrs.mod<-aov(HRS~as.factor(GRP),data=bh1996) ICC2(hrs.mod) } \keyword{attribute} multilevel/man/item.total.Rd0000644000176200001440000000162614211417252015562 0ustar liggesusers\name{item.total} \alias{item.total} \title{Item-total correlations} \description{Calculates item-total correlations in multi-item scales.} \usage{ item.total(items) } \arguments{ \item{items}{A matrix or dataframe where each column represents an item in a multi-item scale.} } \value{ \item{Variable}{Variable examined in the reliability analyses.} \item{Item.Total}{The item correlation with the mean of the other items.} \item{Alpha.Without}{The Cronbach Alpha reliability estimate of the scale without the variable.} \item{N}{The number of observations on which the analyses were calculated.} } \references{Cronbach L. J. (1951) Coefficient Alpha and the internal structure of tests. Psychometrika, 16,297-334} \author{ Paul Bliese \email{pdbliese@gmail.com}} \seealso{\code{\link{cronbach}}} \examples{ data(bhr2000) item.total(bhr2000[,2:11])} \keyword{reliability} multilevel/man/summary.rgr.agree.Rd0000644000176200001440000000211114211432345017040 0ustar liggesusers\name{summary.rgr.agree} \alias{summary.rgr.agree} \title{S3 method for class 'rgr.agree'} \description{Provides a concise summary of objects created using the function rgr.agree. } \usage{ \method{summary}{rgr.agree}(object,\dots) } \arguments{ \item{object}{An object of class 'rgr.agree'.} \item{\dots}{Optional additional arguments. None used.} } \value{ \item{Summary Statistics for Random and Real Groups}{Number of random groups, Average random group variance, Standard Deviation of random group variance, Actual group variance, z-value} \item{Lower Confidence Intervals (one-tailed)}{Lower confidence intervals based on sorted random group variances.} \item{Upper Confidence Intervals (one-Tailed)}{Upper confidence intervals based on sorted random group variances.} } \author{ Paul Bliese \email{pdbliese@gmail.com} } \seealso{ \code{\link{rgr.agree}} } \examples{ # Example with a small number of replications (500). Increase in actual use. data(bh1996) RGROUT<-rgr.agree(bh1996$HRS,bh1996$GRP,500) summary(RGROUT) } \keyword{programming}multilevel/man/summary.agree.sim.Rd0000644000176200001440000000144414211432034017041 0ustar liggesusers\name{summary.agree.sim} \alias{summary.agree.sim} \title{S3 method for class 'agree.sim'} \description{Provides a concise summary of objects created using the functions rwg.sim and rwg.j.sim. } \usage{ \method{summary}{agree.sim}(object,\dots) } \arguments{ \item{object}{An object of class 'agree.sim'.} \item{\dots}{Optional additional arguments. None used.} } \value{A summary of all the output elements in the agree.sim class object.} \author{ Paul Bliese \email{pdbliese@gmail.com} } \seealso{ \code{\link{rwg.sim}} \code{\link{rwg.j.sim}} } \examples{ #An example from Dunlap et al. (2003). The estimate from Dunlap et al. #Table 2 is 0.53. Increase replications in actual use. RWG.OUT<-rwg.sim(gsize=10,nresp=5,nrep=500) summary(RWG.OUT) } \keyword{programming}multilevel/DESCRIPTION0000644000176200001440000000233414211511442014137 0ustar liggesusersPackage: multilevel Version: 2.7 Date: 2022-03-07 Title: Multilevel Functions Authors@R: c(person("Paul", "Bliese", role = c("aut", "cre"), email = "pdbliese@gmail.com"), person("Gilad", "Chen", role = "ctb"), person("Patrick", "Downes", role = "ctb"), person("Donald","Schepker",role= "ctb"), person("Jonas", "Lang", role = "ctb")) Author: Paul Bliese [aut, cre], Gilad Chen [ctb], Patrick Downes [ctb], Donald Schepker [ctb], Jonas Lang [ctb] Maintainer: Paul Bliese Depends: R (>= 3.5.0), nlme, MASS Description: Tools used by organizational researchers for the analysis of multilevel data. Includes four broad sets of tools. First, functions for estimating within-group agreement and reliability indices. Second, functions for manipulating multilevel and longitudinal (panel) data. Third, simulations for estimating power and generating multilevel data. Fourth, miscellaneous functions for estimating reliability and performing simple calculations and data transformations. License: GPL (>= 2) URL: https://www.r-project.org NeedsCompilation: no Packaged: 2022-03-07 22:41:09 UTC; pblie Repository: CRAN Date/Publication: 2022-03-07 23:20:02 UTC multilevel/R/0000755000176200001440000000000014211505005012626 5ustar liggesusersmultilevel/R/multilevel.R0000644000176200001440000011033414211437447015153 0ustar liggesusers#ad.m #### ad.m<-function (x, grpid,type="mean") { NEWDAT <- data.frame(x, grpid = grpid) NEWDAT <- na.exclude(NEWDAT) DATSPLIT <- split(NEWDAT[, 1:(ncol(NEWDAT) - 1)], NEWDAT$grpid) # Code to estimate AD Mean on scales if(ncol(as.matrix(x))>1){ ans1 <- lapply(DATSPLIT, function(Q) { if (nrow(Q) > 1) { mean(apply(Q,2,function(AD){ sum(abs(AD-eval(call(paste(type),AD))))/length(AD)})) } else { NA } }) ans2 <- lapply(DATSPLIT, nrow) ans1 <- unlist(ans1) ans2 <- unlist(ans2) OUTPUT <- data.frame(grpid = names(DATSPLIT), AD.M = ans1, gsize = ans2) return(OUTPUT) stop() } #Code to estimate AD Mean on single items ans1<-lapply(DATSPLIT,function(AD){ sum(abs(AD-eval(call(paste(type),AD))))/length(AD)}) ans2 <- lapply(DATSPLIT, length) ans1 <- unlist(ans1) ans2 <- unlist(ans2) ans1[ans2==1]<-NA OUTPUT <- data.frame(grpid = names(DATSPLIT), AD.M = ans1, gsize = ans2) return(OUTPUT) } #ad.m.sim#### ad.m.sim<-function (gsize, nitems = 1, nresp, itemcors = NULL, type = "mean", nrep) { OUT <- rep(NA, nrep) if (nitems == 1 & is.null(itemcors)) { for (i in 1:nrep) { OUT[i] <- ad.m(x = sample(1:nresp, gsize, replace = T), grpid = rep(1, gsize), type)[, 2] } } if (nitems > 1 & is.null(itemcors)) { for (i in 1:nrep) { OUT[i] <- ad.m(x = matrix(sample(1:nresp, gsize * nitems, replace = T), ncol = nitems), grpid = rep(1, gsize), type)[, 2] } } if (!is.null(itemcors)) { nitems <- ncol(itemcors) for (i in 1:nrep) { SIMDAT <- mvrnorm(n = gsize, mu = rep(0, nitems), itemcors) SIMDAT <- apply(SIMDAT, 2, cut, breaks = qnorm(c(0, (1/nresp) * 1:nresp)), include.lowest = T, labels = F) OUT[i] <- ad.m(x = SIMDAT, grpid = rep(1, gsize), type)[, 2] } } cumpct <- cumsum(table(OUT)/length(OUT)) lag1 <- c(NA, cumpct[1:length(cumpct) - 1]) TDAT <- matrix(c(as.numeric(names(cumpct)),cumpct, lag1,1:length(cumpct)),ncol=4) TR <- TDAT[TDAT[,2] > 0.05 & TDAT[,3] <= 0.05,4] ad.m.05 <- TDAT[TR - 1, 1] estout <- list(ad.m = OUT, gsize = gsize, nresp = nresp, nitems = nitems, ad.m.05 = ad.m.05, pract.sig = nresp/6) class(estout) <- "disagree.sim" return(estout) } #quantile.disagree.sim#### quantile.disagree.sim<-function(x, confint, ...) { out<-data.frame(quantile.values=confint,confint.estimate=rep(NA,length(confint))) cumpct<-cumsum(table(x[[1]])/length(x[[1]])) lag1<-c(NA,cumpct[1:length(cumpct)-1]) TDAT<-data.frame(dagree.val=as.numeric(names(cumpct)),cumpct,lag1) row.names(TDAT)<-1:nrow(TDAT) for(i in 1:length(confint)){ TR<-as.numeric(row.names(TDAT[TDAT$cumpct>confint[i]&TDAT$lag1<=confint[i],])) out[i,2]<-TDAT[TR-1,1] } return(out) } #summary.disagree.sim#### summary.disagree.sim<-function(object, ...) { out<-list(summary(object[[1]]), object[[2]], object[[3]], object[[4]], object[[5]], object[[6]]) names(out)<-names(object) return(out) } #awg #### awg<-function (x, grpid, range = c(1, 5)) { NEWDAT <- data.frame(x, grpid = grpid) NEWDAT <- na.exclude(NEWDAT) DATSPLIT <- split(NEWDAT[, 1:(ncol(NEWDAT) - 1)], NEWDAT$grpid) if (ncol(as.matrix(x)) > 1) { ans1 <- lapply(DATSPLIT, function(Q) { if (nrow(Q) > 1) { mean(apply(Q, 2, function(AW) { H <- range[2] L <- range[1] M <- mean(AW) k <- length(AW) A.WG <- 1 - ((2 * var(AW))/(((H + L) * M - (M^2) - (H * L)) * (k/(k - 1)))) if (M < ((L * (k - 1) + H)/k)) A.WG <- NA if (M > ((H * (k - 1) + L)/k)) A.WG <- NA if (M == H | M == L) A.WG = 1 A.WG }), na.rm = T) } else { NA } }) ans2 <- lapply(DATSPLIT, nrow) ans3 <- lapply(lapply(DATSPLIT, var),mean,na.rm=T) ans1 <- unlist(ans1) ans2 <- unlist(ans2) ans3 <- unlist(ans3) OUTPUT <- data.frame(grpid = names(DATSPLIT), a.wg = ans1, nitems = ncol(as.matrix(x)), nraters = ans2, avg.grp.var = ans3) return(OUTPUT) stop() } ans1 <- lapply(DATSPLIT, function(AW) { H <- range[2] L <- range[1] M <- mean(AW) k <- length(AW) A.WG <- 1 - ((2 * var(AW))/(((H + L) * M - (M^2) - (H * L)) * (k/(k - 1)))) if (M < ((L * (k - 1) + H)/k)) A.WG <- NA if (M > ((H * (k - 1) + L)/k)) A.WG <- NA if (M == H | M == L) A.WG = 1 A.WG }) ans2 <- lapply(DATSPLIT, length) ans3 <- lapply(DATSPLIT, var) ans1 <- unlist(ans1) ans2 <- unlist(ans2) ans3 <- unlist(ans3) ans1[ans2 == 1] <- NA OUTPUT <- data.frame(grpid = names(DATSPLIT), a.wg = ans1, nraters = ans2, grp.var = ans3) return(OUTPUT) } #boot.icc#### boot.icc<-function(x, grpid, nboot, aov.est=FALSE){ if(aov.est){ data<-data.frame(grpid,x) #fixes data because code below uses the first column to select level 2 B.OUT<-rep(NA,nboot) ngrp<-length(unique(grpid)) ugrp<-unique(grpid) for(i in 1:nboot) { #The code below creates an empty list, selects a sample of level 2 #units and then goes in and samples level-1 units for each level 2 unit ROUT<-list(NA) rgrps<-sample(ugrp,ngrp,replace=T) for (k in 1:ngrp){ ROUT[[k]]<-data.frame(newgrp=k,data[is.element(data[,1],rgrps[k]),]) dindex<-sample(nrow(ROUT[[k]]),nrow(ROUT[[k]]),replace=T) ROUT[[k]]<-ROUT[[k]][dindex,] } ROUT<-(do.call(rbind,ROUT)) tmod<-aov(x~as.factor(newgrp),data=ROUT) B.OUT[i]<-ICC1(tmod) } return(B.OUT) } if(!aov.est){ data<-data.frame(grpid,x) #fixes data because code below uses the first column to select level 2 B.OUT<-rep(NA,nboot) ngrp<-length(unique(grpid)) ugrp<-unique(grpid) for(i in 1:nboot) { #The code below creates an empty list, selects a sample of level 2 #units and then goes in and samples level-1 units for each level 2 unit ROUT<-list(NA) rgrps<-sample(ugrp,ngrp,replace=T) for (k in 1:ngrp){ ROUT[[k]]<-data.frame(newgrp=k,data[is.element(data[,1],rgrps[k]),]) dindex<-sample(nrow(ROUT[[k]]),nrow(ROUT[[k]]),replace=T) ROUT[[k]]<-ROUT[[k]][dindex,] } ROUT<-(do.call(rbind,ROUT)) tmod<-lme(x~1, random=~1|newgrp,data=ROUT,control=list(opt="optim")) temp<-VarCorr(tmod) Tau<-as.numeric(temp[[1]]) Sigma.Sq<-(tmod$sigma)^2 B.OUT[i]<-Tau/(Tau+Sigma.Sq) } return(B.OUT) } } #cordif#### cordif<-function(rvalue1,rvalue2,n1,n2){ zvalue1<-.5*((log(1+rvalue1))-(log(1-rvalue1))) zvalue2<-.5*((log(1+rvalue2))-(log(1-rvalue2))) zest<-(zvalue1-zvalue2)/sqrt(1/(n1-3)+1/(n2-3)) out<-list("z value"=zest) return(out) } #cordif.dep#### cordif.dep<-function(r.x1y, r.x2y, r.x1x2, n) { # # This function tests whether two dependent correlations are significantly # different from each other. The formula is taken from Cohen & Cohen (1983) # p. 56 # rbar <- (r.x1y + r.x2y)/2 barRbar <- 1 - r.x1y^2 - r.x2y^2 - r.x1x2^2 + 2 * r.x1y * r.x2y * r.x1x2 tvalue.num <- ((r.x1y - r.x2y) * sqrt((n - 1) * (1 + r.x1x2))) tvalue.den <- sqrt(((2 * ((n - 1)/(n - 3))) * barRbar + ((rbar^2)) * (1 - r.x1x2)^3)) t.value <- tvalue.num/tvalue.den DF <- n - 3 p.value <- (1 - pt(abs(t.value), DF)) * 2 OUT <- data.frame(t.value, DF, p.value) return(OUT) } #cronbach#### cronbach<-function(items) { items<-na.exclude(items) N <- ncol(items) TOTVAR <- var(apply(items, 1, sum)) SUMVAR <- sum(apply(items, 2, var)) ALPHA <- (N/(N - 1)) * (1 - (SUMVAR/TOTVAR)) OUT<-list(Alpha=ALPHA,N=nrow(items)) return(OUT) } #dgm.code#### dgm.code<-function(grp,time,event,n.events=FALSE,first.obs=FALSE){ #ensure data structure is correct newdata<-data.frame(grp=grp,time=time,event=event) newdata<-na.exclude(newdata) newdata<-newdata[order(newdata$grp,newdata$time),] #If first observation is an event and first.obs is TRUE #change the first observation to a non-event if(first.obs){ fobs.grps<-newdata$grp[!duplicated(newdata$grp)&(newdata$event==1)] #print(fobs.grps) newdata$event[!duplicated(newdata$grp)&(newdata$event==1)]<-0 } #Check to see if first observation is an event s.event<-nrow(newdata[!duplicated(newdata$grp)&(newdata$event==1),]) if(s.event>0){ print("The following groups start with an event") print(newdata[!duplicated(newdata$grp)&(newdata$event==1),]) print("Drop the groups or use the first.obs=TRUE option") stop() } #count the maximum number of events for any group max.events<-max(with(newdata,tapply(event,grp,sum))) n.grps<-length(unique(newdata$grp)) #adjust the maximum number of events to a specified level if(n.events){ max.events=n.events } # Set up the structure for the output ANS<-matrix(0,nrow(newdata),ncol=max.events*2) ANS<-data.frame(ANS) names(ANS)<-c(paste0("trans",c(1:max.events)),paste0("post",c(1:max.events))) ANS<-data.frame(ANS,grp=newdata$grp,time=newdata$time,event=newdata$event) g.size<-tapply(newdata$grp,newdata$grp,length) # ANS$cum.event<-unlist(tapply(ANS$event,ANS$grp,cumsum)) ANS$time.a<-ANS$time ANS$cum.event<-do.call(c, tapply(ANS$event, ANS$grp, FUN=cumsum)) ANS$num.grp<-rep(1:n.grps,times=g.size) #create a numeric group for loops # Add two check variables for total events and whether an event # occured on the first occasion ANS$tot.events<-NA ANS$event.first<-0 if(first.obs){ ANS$event.first[ANS$grp%in%fobs.grps]<-1 } #collapse the number of events to a specified level if(n.events){ ANS$cum.event<-ifelse(ANS$cum.event>n.events,n.events,ANS$cum.event) } # Set up a factor outside of the loop to get all levels ANS$cum.event.f<-factor(ANS$cum.event,levels=c(0:max.events)) # Set up a loop to put values in trans and post variables # First skip groups with no events for(i in 1:n.grps){ if(sum(ANS$event[ANS$num.grp==i])==0){ ANS$tot.events[ANS$num.grp==i]<-0 next(i) } # Use model.matrix to set up dummy codes for trans and post ANS[ANS$num.grp==i,1:max.events]<-model.matrix(~C(cum.event.f,contr.treatment),data=ANS[ANS$num.grp==i,])[,2:(max.events+1)] ANS[ANS$num.grp==i,(max.events+1):(2*max.events)]<-model.matrix(~C(cum.event.f,contr.treatment),data=ANS[ANS$num.grp==i,])[,2:(max.events+1)] if(max.events==1){ ANS[ANS$num.grp==i,2]<-cumsum(ANS[ANS$num.grp==i,2])-1 } if(max.events>1){ ANS[ANS$num.grp==i,(max.events+1):(2*max.events)]<-apply(ANS[ANS$num.grp==i,(max.events+1):(2*max.events)],2,cumsum)-1 } ANS$time.a[ANS$num.grp==i]<-ifelse(ANS$cum.event[ANS$num.grp==i]==0,ANS$time[ANS$num.grp==i],NA) ANS$time.a[is.na(ANS$time.a)&ANS$num.grp==i]<-max(ANS$time.a[!is.na(ANS$time.a)&ANS$num.grp==i]) ANS$tot.events[ANS$num.grp==i]<-sum(ANS$event[ANS$num.grp==i]) next(i) } # Clean up the ANS matrix column by column # print(ANS) to see the structure of the previous loop for(j in 1:max.events){ ANS[,max.events+j]<-ifelse(ANS[,j]==0,0,ANS[,max.events+j]) next(j) } #rearrange the ANS matrix for output #print(ANS[1:30,]) to see the first 30 rows of complete data ANS[,c((max.events*2)+1:3,1:(max.events*2),(max.events*2)+c(4,7,8))] } #GmeanRel#### gmeanrel<-function(object) { OUTFILE<-aggregate(object$group,object$group,length) names(OUTFILE)<-c("Group","GrpSize") temp<-VarCorr(object) Tau<-as.numeric(temp[[1]]) Sigma.Sq<-(object$sigma)^2 ICC<-Tau/(Tau+Sigma.Sq) OUTFILE$GmeanRel<-(OUTFILE[,2]*ICC)/(1+(OUTFILE[,2]-1)*ICC) estout<-list(ICC=ICC,Group=OUTFILE[,1],GrpSize=OUTFILE[,2],MeanRel=OUTFILE[,3]) class(estout)<-"gmeanrel" return(estout) } #graph.ran.mean#### graph.ran.mean<-function(x, grpid, nreps, limits, graph=TRUE, bootci=FALSE) { if(bootci){ if (missing(limits)) limits <- quantile(x[is.na(x) == F], c(0.10, 0.90)) if (is.factor(grpid)) grpid <- grpid[, drop = TRUE] TDAT<-na.exclude(data.frame(x,grpid)) x<-TDAT[,1] grpid<-TDAT[,2] NGRPS <- length(tapply(x, grpid, length)) OUT <- matrix(NA, NGRPS, nreps) for (i in 1:nreps) { TOUT <- mix.data(x, grpid) OUT[, i] <- sort(tapply(TOUT[, 3], TOUT[, 1], mean, na.rm = T)) } REALGRP <- sort(tapply(x, grpid, mean, na.rm = T)) if (graph) { plot(c(REALGRP, max(REALGRP)), type = "h", ylim = limits, ylab = "Group Average") lines(c(REALGRP, max(REALGRP)), type = "s") PSEUDOMEAN <- apply(OUT, 1, mean) lines(PSEUDOMEAN, type = "l") PSEUDO.LCI <- apply(OUT, 1, quantile, 0.025) lines(PSEUDO.LCI, type = "l",lty=2) PSEUDO.HCI <- apply(OUT, 1, quantile, 0.975) lines(PSEUDO.HCI, type = "l",lty=2) } else { REALGRP <- sort(tapply(x, grpid, mean, na.rm = T)) GRPNAMES <- row.names(REALGRP) REALGRP <- as.vector(REALGRP) PSEUDOMEAN <- apply(OUT, 1, mean) PSEUDO.LCI <- apply(OUT, 1, quantile, .025) PSEUDO.HCI <- apply(OUT, 1, quantile, .975) OUT <- data.frame(GRPNAMES, GRPMEAN = REALGRP, PSEUDOMEAN, PSEUDO.LCI, PSEUDO.HCI) return(OUT) } } if(!bootci){ if (missing(limits)) limits <- quantile(x[is.na(x) == F], c(0.10, 0.90)) if (is.factor(grpid)) grpid <- grpid[, drop = TRUE] TDAT<-na.exclude(data.frame(x,grpid)) x<-TDAT[,1] grpid<-TDAT[,2] NGRPS <- length(tapply(x, grpid, length)) OUT <- matrix(NA, NGRPS, nreps) for (i in 1:nreps) { TOUT <- mix.data(x, grpid) OUT[, i] <- sort(tapply(TOUT[, 3], TOUT[, 1], mean, na.rm = T)) } REALGRP <- sort(tapply(x, grpid, mean, na.rm = T)) if (graph) { plot(c(REALGRP, max(REALGRP)), type = "h", ylim = limits, ylab = "Group Average") lines(c(REALGRP, max(REALGRP)), type = "s") PSEUDOGRP <- apply(OUT, 1, mean) lines(PSEUDOGRP, type = "l") } else { REALGRP <- sort(tapply(x, grpid, mean, na.rm = T)) GRPNAMES <- row.names(REALGRP) REALGRP <- as.vector(REALGRP) PSEUDOGRP <- apply(OUT, 1, mean) OUT <- data.frame(GRPNAMES = GRPNAMES, GRPMEAN = REALGRP, PSEUDOMEAN = PSEUDOGRP) return(OUT) } } } #ICC1#### ICC1<- function(object) { MOD <- summary(object) MSB <- MOD[[1]][1, 3] MSW <- MOD[[1]][2, 3] GSIZE <- (MOD[[1]][2, 1] + (MOD[[1]][1, 1] + 1))/(MOD[[1]][1, 1] + 1) # print(GSIZE) OUT <- (MSB - MSW)/(MSB + ((GSIZE - 1) * MSW)) return(OUT) } #ICC2#### ICC2 <-function(object) { MOD <- summary(object) MSB <- MOD[[1]][1, 3] MSW <- MOD[[1]][2, 3] OUT <- (MSB - MSW)/MSB return(OUT) } #item.total#### item.total<-function(items) { items<-na.exclude(items) N <- ncol(items) ans <- matrix(0, N, 3) ans[, 1] <- labels(items)[[2]] for(i in 1:N) { ans[i, 2] <- cor(items[, i], apply(items[, - i], 1, mean)) ans[i, 3] <- cronbach(items[, - i])[[1]] } OUT <- data.frame(Variable=ans[,1],Item.Total=as.numeric(ans[,2]), Alpha.Without=as.numeric(ans[,3]),N=nrow(items)) return(OUT) } #make.univ#### make.univ<-function (x, dvs, tname="TIME", outname="MULTDV") { NREPOBS <- ncol(dvs) UNIV.DAT <- x[rep(1:nrow(x), rep(NREPOBS, nrow(x))), 1:ncol(x)] FINAL.UNIV <- data.frame(timedat = rep(0:(NREPOBS - 1), nrow(x)), outdat = as.vector(t(dvs))) names(FINAL.UNIV)<-c(tname,outname) FINAL.DAT <- data.frame(UNIV.DAT, FINAL.UNIV) return(FINAL.DAT) } #mix.data#### mix.data<-function (x, grpid) { TDAT <- cbind(rnorm(length(grpid)), grpid, x) TDAT <- TDAT[is.na(grpid) == F & grpid != "NA", ] TDAT <- TDAT[order(TDAT[, 1]),1:ncol(TDAT)] TMAT <- tapply(TDAT[, 2], TDAT[, 2], length) NGRPS <- length(TMAT) newid <- rep(1:NGRPS, TMAT) OUT <- cbind(newid, TDAT[, 2:ncol(TDAT)]) return(OUT) } #mult.icc#### mult.icc<-function (x, grpid) { ans <- data.frame(Variable = names(x[, 1:ncol(x)]), ICC1 = as.numeric(rep(NA, ncol(x))), ICC2 = as.numeric(rep(NA, ncol(x)))) GSIZE <- mean(aggregate(grpid, list(grpid), length)[,2]) for (i in 1:ncol(x)) { DV <- x[, i] tmod <- lme(DV ~ 1, random = ~1 | grpid, na.action = na.omit, control=list(opt="optim")) TAU <- as.numeric(VarCorr(tmod)[, 1][1]) SIGMASQ <- tmod$sigma^2 ICC1 <- TAU/(TAU + SIGMASQ) ICC2 <- (GSIZE * ICC1)/(1 + (GSIZE - 1) * ICC1) ans[i, 2] <- ICC1 ans[i, 3] <- ICC2 } return(ans) } #mult.make.univ#### mult.make.univ <- function(x,dvlist,tname="TIME",outname="MULTDV") { NREPOBS <- length(dvlist[[1]]) UNIV.DAT <- x[rep(1:nrow(x), rep(NREPOBS, nrow(x))), 1:ncol(x)] FINAL.UNIV <- data.frame(timedat = rep(0:(NREPOBS - 1), nrow(x)), as.data.frame(lapply(dvlist,function(cols) {as.vector(t(x[,cols]))}))) if (is.null(names(dvlist))) { names(FINAL.UNIV) <- c(tname,paste(outname,1:(ncol(FINAL.UNIV)-1),sep='')) }else{ names(FINAL.UNIV) <- c(tname,names(dvlist)) } FINAL.DAT <- data.frame(UNIV.DAT,FINAL.UNIV) return(FINAL.DAT) } #sam.cor#### sam.cor<-function(x,rho) { y <- (rho * (x - mean(x)))/sqrt(var(x)) + sqrt(1 - rho^2) * rnorm(length(x)) cat("Sample corr = ", cor(x, y), "\n") return(y) } #rmv.blanks#### rmv.blanks<-function (object) { OUT <- lapply(object, function(xsub) { ANY.BLNK <- grep(" +$", xsub) if (length(ANY.BLNK) < length(xsub)) xsub <- xsub else xsub <- sub(" +$", "", xsub) }) return(data.frame(OUT)) } #rgr.agree#### rgr.agree<-function (x, grpid, nrangrps) { GVARDAT <- tapply(x, grpid, var) NGRPS <- length(GVARDAT) GSIZE <- tapply(grpid, grpid, length) if(min(GSIZE)<2){ print("One or more groups has only one group member.") stop("There must be at least two group members per group to estimate rgr.agree.") } NREPS <- round((nrangrps/NGRPS), digits = 0) ans <- rep(0, (length(GSIZE) * NREPS)) for (i in 1:NREPS) { ans[((i * length(GSIZE)) - (length(GSIZE)) + 1):(i * length(GSIZE))] <- ran.group(x, grpid, var) } AVGRPVAR <- mean(GVARDAT) NGRPS <- length(GVARDAT) RGRVAR <- mean(ans) RGRSD <- sqrt(var(ans)) ZVALUE <- (AVGRPVAR - RGRVAR)/(RGRSD/sqrt(NGRPS)) estout <- list(NRanGrp =length(ans), AvRGRVar = RGRVAR, SDRGRVar = RGRSD, AvGrpVar = AVGRPVAR, zvalue = ZVALUE, RGRVARS =ans) class(estout)<-"rgr.agree" return(estout) } ran.group<-function(x, grpid, fun, ...) { if(!is.null(ncol(x))) { GSIZE <- tapply(grpid, grpid, length) ans <- rep(0, length(GSIZE)) if(length(x[, 1]) != sum(GSIZE)) stop("The sum of group sizes does not match the number of observations" ) for(i in 1:length(GSIZE)) { GID2 <- c(1:length(x[, 1])) SAM <- sample(GID2, size = GSIZE[i]) ans[i] <- mean(apply(x[SAM, ], 2, fun)) x <- x[ - SAM, ] } return(ans) } GSIZE <- tapply(grpid, grpid, length) ans <- rep(0, length(GSIZE)) if(length(x) != sum(GSIZE,na.rm=T)) stop("The sum of group sizes does not match the number of observations" ) for(i in 1:length(GSIZE)) { GID2 <- c(1:length(x)) SAM <- sample(GID2, size = GSIZE[i]) ans[i] <- fun(x[c(SAM)]) x <- x[ - SAM] } ans } summary.rgr.agree<-function(object, ...) { Table <- data.frame(object$NRanGrp, object$AvRGRVar, object$SDRGRVar, object$AvGrpVar, object$zvalue) names(Table) <- c("N.RanGrps", "Av.RanGrp.Var", "SD.Rangrp.Var", "Av.RealGrp.Var", "Z-value") object$Table <- as.matrix(Table) object$lowercis<-quantile(object$RGRVARS,c(.005,.01,.025,.05,.10)) object$uppercis<-quantile(object$RGRVARS,c(.90,.95,.975,.99,.995)) OUT<-list(object$Table,object$lowercis,object$uppercis) names(OUT)<-c("Summary Statistics for Random and Real Groups","Lower Confidence Intervals (one-tailed)", "Upper Confidence Intervals (one-Tailed)") OUT } #rgr.OLS#### rgr.ols<-function(xdat1, xdat2, ydata, grpid, nreps) { # # The number of columns in the output matrix has to correspond to # the number of mean squares you want in the output. # This function does RGR on a two IV OLS hierarchical OLS model. # OUT <- matrix(0, nreps, 4) NEWDAT <- cbind(grpid, xdat1, xdat2, ydata) for(k in 1:nreps) { TDAT <- mix.data(NEWDAT, grpid) Y <- tapply(TDAT[, 6], TDAT[, 1], mean) X1 <- tapply(TDAT[, 4], TDAT[, 1], mean) X2 <- tapply(TDAT[, 5], TDAT[, 1], mean) MOD <- lm(Y ~ X1 * X2) #print(anova(MOD,test="F")) TOUT <- anova(MOD, test = "F")[, 3] OUT[k, ] <- TOUT } return(OUT) } #rgr.waba#### rgr.waba<-function(x, y, grpid, nrep) { # # Create Matrix and sort it by Group ID # SMAT <- cbind(grpid, x, y) SMAT <- SMAT[order(SMAT[, 1]), 1:3] GID.S <- SMAT[, 1] X.S <- SMAT[, 2] Y.S <- SMAT[, 3] # # # Create a matrix in which to put the random WABA elements # ans <- matrix(1, nrep, 7) # # # WABA random group loop # for(i in 1:nrep) { # # Generate a random number and sort x and y by it # TR <- rnorm(length(X.S)) T.DAT <- cbind(TR, X.S, Y.S) T.DAT <- T.DAT[order(T.DAT[, 1]), 1:3] # # # Create a Matrix in which to put the WABA elements # tmat <- matrix(0, length(X.S), 6) # # # Split up the x observations by the Group ID and make WABA elements # TX <- split(T.DAT[, 2], GID.S) TX.M <- unlist(lapply(TX, mean)) TX.L <- unlist(lapply(TX, length)) tmat[, 1] <- T.DAT[, 2] tmat[, 2] <- rep(TX.M, TX.L) tmat[, 3] <- (T.DAT[, 2] - tmat[, 2]) # # # Split up the y observations by the Group ID and make WABA elements # TY <- split(T.DAT[, 3], GID.S) TY.M <- unlist(lapply(TY, mean)) TY.L <- unlist(lapply(TY, length)) tmat[, 4] <- T.DAT[, 3] tmat[, 5] <- rep(TY.M, TY.L) tmat[, 6] <- T.DAT[, 3] - tmat[, 5] # # # Calculate WABA parameters and put them in a Matrix format # ans[i, 1] <- cor(tmat[, 1], tmat[, 4]) ans[i, 2] <- cor(tmat[, 1], tmat[, 2]) ans[i, 3] <- cor(tmat[, 4], tmat[, 5]) ans[i, 4] <- cor(tmat[, 2], tmat[, 5]) ans[i, 5] <- cor(tmat[, 1], tmat[, 3]) ans[i, 6] <- cor(tmat[, 4], tmat[, 6]) ans[i, 7] <- cor(tmat[, 3], tmat[, 6]) } estout <- data.frame(ans) names(estout) <- c("RawCorr", "EtaBx", "EtaBy", "CorrB", "EtaWx", "EtaWy", "CorrW") class(estout) <- "rgr.waba" return(estout) } summary.rgr.waba<-function(object, ...) { T.DAT <- rep(0, 3) object2<-data.frame(object$RawCorr,object$EtaBx,object$EtaBy, object$CorrB,object$EtaWx,object$EtaWy,object$CorrW) ans <- data.frame(RawCorr = T.DAT, EtaBx = T.DAT, EtaBy = T.DAT, CorrB = T.DAT, EtaWx = T.DAT, EtaWy = T.DAT, CorrW = T.DAT, row.names = c("NRep", "Mean", "SD")) for (i in 1:7) { ans[1, i] <- length(object2[, i]) ans[2, i] <- mean(object2[, i]) ans[3, i] <- sqrt(var(object2[, i])) } return(ans) } quantile.rgr.waba<-function (x, confint, ...) { object2 <- data.frame(x$EtaBx, x$EtaBy, x$CorrB, x$EtaWx, x$EtaWy, x$CorrW) names(object2)<-c("EtaBx","EtaBy","CorrB","EtaWx","EtaWy","CorrW") ans<-apply(object2,2,quantile,confint) return(ans) } #rtoz#### rtoz<-function(rvalue){ zest<-.5*((log(1+rvalue))-(log(1-rvalue))) #out<-list("z prime"=zest) return(zest) } #rwg#### rwg<-function(x, grpid, ranvar=2) { NEWDAT<-data.frame(x=x,grpid=grpid) NEWDAT<-na.exclude(NEWDAT) DATSPLIT <- split(NEWDAT$x, NEWDAT$grpid) ans1 <- lapply(DATSPLIT, function(Q) { if (length(Q) > 1) { V <- var(Q) if (V > ranvar) V <- ranvar out <- 1 - (V/ranvar) out} else {out<-NA out} }) ans2<-lapply(DATSPLIT,length) ans1 <- unlist(ans1) ans2<-unlist(ans2) OUTPUT <- data.frame(grpid=names(DATSPLIT),rwg = ans1, gsize = ans2) return(OUTPUT) } #rwg.j#### rwg.j<-function(x, grpid,ranvar=2) { NEWDAT<-data.frame(x,grpid=grpid) NEWDAT<-na.exclude(NEWDAT) DATSPLIT <- split(NEWDAT[,1:(ncol(NEWDAT)-1)], NEWDAT$grpid) ans1 <- lapply(DATSPLIT, function(Q) { J <- ncol(Q) if (nrow(Q) > 1) { S <- mean(apply(Q, 2, var,na.rm=T)) if (S > ranvar) S <- ranvar out <- (J * (1 - (S/ranvar)))/((J * (1 - (S/ranvar))) + (S/ranvar)) out } else {out<-NA out } }) ans2<-lapply(DATSPLIT,nrow) ans1 <- unlist(ans1) ans2 <-unlist(ans2) OUTPUT <- data.frame(grpid=names(DATSPLIT),rwg.j = ans1, gsize = ans2) return(OUTPUT) } #rwg.j.lindell#### rwg.j.lindell<-function (x, grpid, ranvar = 2) { NEWDAT<-data.frame(x,grpid=grpid) NEWDAT<-na.exclude(NEWDAT) DATSPLIT <- split(NEWDAT[,1:(ncol(NEWDAT)-1)], NEWDAT$grpid) ans1 <- lapply(DATSPLIT, function(Q) { if (nrow(Q) > 1) { S <- mean(apply(Q, 2, var)) out <- 1-(S/ranvar) out } else {out<-NA out } }) ans2<-lapply(DATSPLIT,nrow) ans1 <- unlist(ans1) ans2 <-unlist(ans2) OUTPUT <- data.frame(grpid=names(DATSPLIT),rwg.lindell = ans1, gsize = ans2) return(OUTPUT) } #rwg.sim#### rwg.sim<-function (gsize, nresp, nrep) { OUT <- rep(NA, nrep) for (i in 1:nrep) { OUT[i] <- rwg(x = sample(1:nresp, gsize, replace = T), grpid = rep(1, gsize), ranvar = (nresp^2 - 1)/12)[,2] } cumpct <- cumsum(table(OUT)/length(OUT)) lag1 <- c(NA, cumpct[1:length(cumpct) - 1]) lag2 <- c(NA, lag1[1:length(lag1) - 1]) TDAT <- matrix(c(as.numeric(names(cumpct)),cumpct,lag1,lag2),ncol=4) rwg.95 <- TDAT[TDAT[,2] > 0.95 & TDAT[,3] >= 0.95 & TDAT[,4] < 0.95, 1] estout <- list(rwg = OUT, gsize = gsize, nresp = nresp, nitems = 1, rwg.95 = rwg.95) class(estout) <- "agree.sim" return(estout) } #rwg.j.sim#### rwg.j.sim<-function(gsize, nitems, nresp, itemcors = NULL, nrep) { OUT <- rep(NA,nrep) if (is.null(itemcors)) { for (i in 1:nrep) { OUT[i] <- rwg.j(x = matrix(sample(1:nresp, gsize * nitems, replace = T), ncol = nitems), grpid = rep(1, gsize), ranvar = (nresp^2 - 1)/12)[, 2] } } if (!is.null(itemcors)){ for (i in 1:nrep) { nitems <- ncol(itemcors) SIMDAT <- mvrnorm(n = gsize, mu = rep(0, nitems), itemcors) SIMDAT <- apply(SIMDAT, 2, cut, breaks = qnorm(c(0, (1/nresp) * 1:nresp)), include.lowest = T, labels = F) OUT[i] <- rwg.j(SIMDAT, grpid = rep(1, gsize), ranvar = ((nresp^2 - 1)/12))[, 2] } } cumpct <- cumsum(table(OUT)/length(OUT)) lag1 <- c(NA, cumpct[1:length(cumpct) - 1]) lag2 <- c(NA, lag1[1:length(lag1) - 1]) TDAT <- matrix(c(as.numeric(names(cumpct)),cumpct,lag1,lag2),ncol=4) rwg.95 <- TDAT[TDAT[,2] > 0.95 & TDAT[,3] >= 0.95 & TDAT[,4] < 0.95, 1] estout <- list(rwg = OUT, gsize = gsize, nresp = nresp, nitems = nitems, rwg.95 = rwg.95) class(estout) <- "agree.sim" return(estout) } #summary.agree.sim#### summary.agree.sim<-function(object, ...) { out<-list(summary(object[[1]]), object[[2]], object[[3]], object[[4]], object[[5]]) names(out)<-names(object) return(out) } #quantile.agree.sim#### quantile.agree.sim<-function(x, confint, ...) { out<-data.frame(quantile.values=confint,confint.estimate=rep(NA,length(confint))) cumpct<-cumsum(table(x[[1]])/length(x[[1]])) lag1<-c(NA,cumpct[1:length(cumpct)-1]) lag2<-c(NA,lag1[1:length(lag1)-1]) TDAT<-data.frame(agree.val=as.numeric(names(cumpct)),cumpct,lag1,lag2) for(i in 1:length(confint)){ out[i,2]<-TDAT[TDAT$cumpct>confint[i]&TDAT$lag1>=confint[i]&TDAT$lag2