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}
\references{
Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications. Wiley. \doi{10.1002/9780470316757}.
Murdoch, D.J. and Chow, E.D. (1996). A graphical
display of large correlation matrices. The American
Statistician 50, 178-180. \doi{10.2307/2684435}.
}
\keyword{ package }
\keyword{ dplot }
ellipse/man/ellipse.arima0.Rd 0000644 0001762 0000144 00000002437 13663503243 015571 0 ustar ligges users \name{ellipse.arima0}
\alias{ellipse.arima0}
\title{
Outline an approximate pairwise confidence region
}
\description{
This function produces the ellipsoidal outline of an approximate
pairwise confidence region for an ARIMA model fit.
}
\usage{
\method{ellipse}{arima0}(x, which = c(1, 2), level = 0.95, t = sqrt(qchisq(level, 2)), \dots)
}
\arguments{
\item{x}{
The first argument should be an \code{arima0} object, usually resulting from a call
to \code{arima0()}.
}
\item{which}{
Which selects the pair of parameters to be plotted. The default is the
first two.
}
\item{level}{
The confidence level of the region. Default 95\%.
}
\item{t}{
The t statistic on the boundary of the ellipse.
}
\item{\dots}{
Other \code{ellipse.default} parameters may also be used.
}}
\value{
A matrix with columns \code{x} and \code{y} to outline the confidence region.
}
\details{
The \code{summary} function is used to obtain the approximate covariance matrix of the
fitted parameters.
}
\seealso{
\code{\link{ellipse}}
}
\examples{
data(USAccDeaths)
fit <- arima0(USAccDeaths, order = c(0, 1, 1), seasonal = list(order = c(0, 1, 1)))
# Plot the approximate 95\% confidence region for the first two parameters
# of the model
plot(ellipse(fit), type = 'l')
points(fit$coef[1], fit$coef[2])
}
\keyword{dplot}
\keyword{ts}
ellipse/DESCRIPTION 0000644 0001762 0000144 00000001624 14456303212 013420 0 ustar ligges users Package: ellipse
Version: 0.5.0
Title: Functions for Drawing Ellipses and Ellipse-Like Confidence
Regions
Author: Duncan Murdoch and E. D. Chow (porting to R by Jesus M. Frias Celayeta)
Maintainer: Duncan Murdoch
Description: Contains various routines for drawing
ellipses and ellipse-like confidence regions, implementing the plots
described in Murdoch and Chow (1996, ). There are
also routines implementing the profile plots described in Bates and
Watts (1988, ).
Depends: R (>= 2.0.0),graphics,stats
Suggests: MASS
LazyLoad: yes
License: GPL (>= 2)
URL: https://github.com/dmurdoch/ellipse,
https://dmurdoch.github.io/ellipse/
BugReports: https://github.com/dmurdoch/ellipse/issues
NeedsCompilation: no
Packaged: 2023-07-20 18:27:17 UTC; murdoch
Repository: CRAN
Date/Publication: 2023-07-20 19:10:02 UTC
ellipse/build/ 0000755 0001762 0000144 00000000000 14456276203 013017 5 ustar ligges users ellipse/build/partial.rdb 0000644 0001762 0000144 00000000075 14456276203 015146 0 ustar ligges users b```b`afd`b1 H020piּb C " {7 ellipse/R/ 0000755 0001762 0000144 00000000000 14456227630 012121 5 ustar ligges users ellipse/R/ellipse.default.R 0000644 0001762 0000144 00000002007 14411246067 015317 0 ustar ligges users "ellipse.default" <-
function (x, scale = c(1, 1), centre = c(0, 0), level = 0.95,
t = sqrt(qchisq(level, 2)), which = c(1, 2), npoints = 100,
center = centre, ...)
{
if(!missing(centre) && !missing(center)){
warning("Specify centre or center, not both. The value from center will be used.")
}
names <- c("x", "y")
if (is.matrix(x)) {
xind <- which[1]
yind <- which[2]
r <- x[xind, yind]
if (missing(scale)) {
scale <- sqrt(c(x[xind, xind], x[yind, yind]))
if (scale[1] > 0) r <- r/scale[1]
if (scale[2] > 0) r <- r/scale[2]
}
if (!is.null(dimnames(x)[[1]]))
names <- dimnames(x)[[1]][c(xind, yind)]
}
else r <- x
r <- min(max(r,-1),1) # clamp to -1..1, in case of rounding errors
d <- acos(r)
a <- seq(0, 2 * pi, len = npoints)
matrix(c(t * scale[1] * cos(a + d/2) + center[1], t * scale[2] *
cos(a - d/2) + center[2]), npoints, 2, dimnames = list(NULL,
names))
}
ellipse/R/ellipse.arima0.R 0000644 0001762 0000144 00000000272 13663503265 015052 0 ustar ligges users ellipse.arima0<-
function(x, which = c(1, 2), level = 0.95, t = sqrt(qchisq(level, 2)), ...)
{
ellipse.default(x$var.coef[which, which], centre = x$coef[which], t = t, ...)
}
ellipse/R/ellipse.R 0000644 0001762 0000144 00000000066 13663503265 013703 0 ustar ligges users "ellipse" <-function (x, ...)
UseMethod("ellipse")
ellipse/R/ellipse.glm.R 0000644 0001762 0000144 00000001020 13663503265 014450 0 ustar ligges users "ellipse.glm" <-
function (x, which = c(1, 2), level = 0.95, t, npoints = 100, dispersion, ...)
{
s <- summary(x)
est.disp <- missing(dispersion) & !(x$family$family %in% c('poisson','binomial'))
if (missing(dispersion)) dispersion <- s$dispersion
if (missing(t)) t <- ifelse(est.disp,sqrt(2 * qf(level, 2, s$df[2])),
sqrt(qchisq(level, 2)))
ellipse.default(dispersion * s$cov.unscaled[which, which],
centre = x$coefficients[which], t = t, npoints = npoints, ...)
}
ellipse/R/ellipse.nls.R 0000644 0001762 0000144 00000000453 13663503265 014476 0 ustar ligges users "ellipse.nls" <-
function (x, which = c(1, 2), level = 0.95, t = sqrt(2 * qf(level,
2, s$df[2])), ...)
{
s <- summary(x)
ellipse.default(s$sigma^2 * s$cov.unscaled[which, which],
centre = x$m$getPars()[which], t = t, ...)
}
ellipse/R/ellipse.profile.glm.R 0000644 0001762 0000144 00000001047 13663503265 016120 0 ustar ligges users "ellipse.profile.glm" <-
function (x, which = c(1, 2), level = 0.95, t, npoints = 100, dispersion, ...)
{
if (missing(dispersion)) dispersion <- ifelse(attr(x,"original.fit")$family$family %in% c('poisson','binomial'),
1, NA)
if (missing(t)) t <- ifelse(is.na(dispersion),sqrt(2 * qf(level, 2, attr(x,"summary")$df[2])),
sqrt(qchisq(level, 2)*dispersion/attr(x,"summary")$dispersion))
ellipse.profile(x, which = which, level = level, t = t, npoints = npoints)
}
ellipse/R/plotcorr.R 0000644 0001762 0000144 00000005630 13663503265 014114 0 ustar ligges users "plotcorr" <-
function (corr, outline = TRUE, col = 'grey', numbers = FALSE, type = c("full","lower","upper"),
diag = (type == "full"), bty = "n", axes = FALSE, xlab = "", ylab = "", asp = 1,
cex.lab = par("cex.lab"), cex = 0.75*par("cex"), mar = 0.1 + c(2,2,4,2), ...)
{
savepar <- par(pty = "s", mar = mar)
on.exit(par(savepar))
if (is.null(corr)) return(invisible())
if ((!is.matrix(corr)) || (round(min(corr, na.rm = TRUE), 6) < -1)
|| (round(max(corr, na.rm = TRUE), 6) > 1))
stop("Need a correlation matrix")
plot.new()
par(new = TRUE)
rowdim <- dim(corr)[1]
coldim <- dim(corr)[2]
rowlabs <- dimnames(corr)[[1]]
collabs <- dimnames(corr)[[2]]
if (is.null(rowlabs)) rowlabs <- 1:rowdim
if (is.null(collabs)) collabs <- 1:coldim
rowlabs <- as.character(rowlabs)
collabs <- as.character(collabs)
col <- rep(col, length = length(corr))
dim(col) <- dim(corr)
type <- match.arg(type)
cols <- 1:coldim
rows <- 1:rowdim
xshift <- 0
yshift <- 0
if (!diag) {
if (type == "upper") {
cols <- 2:coldim
rows <- 1:(rowdim - 1)
xshift <- 1
} else if (type == "lower") {
cols <- 1:(coldim-1)
rows <- 2:rowdim
yshift <- -1
}
}
maxdim <- max(length(rows), length(cols))
plt <- par('plt')
xlabwidth <- max(strwidth(rowlabs[rows],units='figure',cex=cex.lab))/(plt[2]-plt[1])
xlabwidth <- xlabwidth*maxdim/(1-xlabwidth)
ylabwidth <- max(strwidth(collabs[cols],units='figure',cex=cex.lab))/(plt[4]-plt[3])
ylabwidth <- ylabwidth*maxdim/(1-ylabwidth)
plot(c(-xlabwidth-0.5, maxdim + 0.5), c(0.5, maxdim + 1 + ylabwidth),
type = "n", bty = bty, axes = axes, xlab = "", ylab = "", asp = asp,
cex.lab = cex.lab, ...)
text(rep(0, length(rows)), length(rows):1, labels = rowlabs[rows], adj = 1, cex = cex.lab)
text(cols-xshift, rep(length(rows) + 1, length(cols)), labels = collabs[cols],
srt = 90, adj = 0, cex = cex.lab)
mtext(xlab,1,0)
mtext(ylab,2,0)
mat <- diag(c(1, 1))
plotcorrInternal <- function()
{
if (i == j && !diag) return()
if (!numbers) {
mat[1, 2] <- corr[i, j]
mat[2, 1] <- mat[1, 2]
ell <- ellipse(mat, t = 0.43)
ell[, 1] <- ell[, 1] + j - xshift
ell[, 2] <- ell[, 2] + length(rows) + 1 - i - yshift
polygon(ell, col = col[i, j])
if (outline) lines(ell)
} else {
text(j + 0.3 - xshift, length(rows) + 1 - i - yshift, round(10 * corr[i, j], 0),
adj = 1, cex = cex)
}
}
for (i in 1:dim(corr)[1]) {
for (j in 1:dim(corr)[2]) {
if (type == "full") {
plotcorrInternal()
} else if (type == "lower" && (i >= j)) {
plotcorrInternal()
} else if (type == "upper" && (i <= j)) {
plotcorrInternal()
}
}
}
invisible()
}
ellipse/R/ellipse.profile.nls.R 0000644 0001762 0000144 00000000403 13663503265 016130 0 ustar ligges users "ellipse.profile.nls" <-
function (x, which = c(1, 2), level = 0.95, t = sqrt(2 * qf(level, 2, attr(x,
"summary")$df[2])), npoints = 100, ...)
{
ellipse.profile(x, which = which, level = level, t = t, npoints = npoints)
}
ellipse/R/ellipse.lm.R 0000644 0001762 0000144 00000000461 13663503265 014311 0 ustar ligges users "ellipse.lm" <-
function (x, which = c(1, 2), level = 0.95, t = sqrt(2 * qf(level,
2, x$df.residual)), ...)
{
s <- summary(x)
ellipse.default(s$sigma^2 * s$cov.unscaled[which, which],
centre = x$coefficients[which], t = t, ...)
}
ellipse/R/ellipse.profile.R 0000644 0001762 0000144 00000004526 13663503265 015347 0 ustar ligges users "ellipse.profile" <-
function (x, which = c(1, 2), level = 0.95, t = sqrt(qchisq(level,
2)), npoints = 100, ...)
{
aa <- x[[which[1]]][[2]][, which[1]]
ar <- x[[which[1]]][[2]][, which[2]]
ra <- x[[which[2]]][[2]][, which[1]]
rr <- x[[which[2]]][[2]][, which[2]]
atau <- x[[which[1]]][[1]]
rtau <- x[[which[2]]][[1]]
arange <- range(c(aa, ra))
rrange <- range(c(ar, rr))
atau <- atau/t
rtau <- rtau/t
getad <- function(tau1, tau2) {
if (abs(tau1) > 1)
tau1 <- tau1/abs(tau1)
if (abs(tau2) > 1)
tau2 <- tau2/abs(tau2)
acos1 <- acos(tau1)
acos2 <- acos(tau2)
d <- abs(acos1 - acos2)
a <- (acos1 + acos2)/2
if (acos1 < acos2)
a <- -a
c(a, d)
}
myapprox <- function(x, y, where) {
good <- is.finite(x) & is.finite(y)
x <- x[good]
y <- y[good]
if (length(x) > 1) {
result <- approx(x[good], y[good], where)$y
bad <- is.na(result)
if (any(bad)) {
for (i in 1:length(result)) {
if (bad[i]) {
if (where[i] > x[length(x)]) {
x1 <- x[length(x) - 1]
y1 <- y[length(x) - 1]
x2 <- x[length(x)]
y2 <- y[length(x)]
}
else if (where[i] < x[1]) {
x1 <- x[1]
y1 <- y[1]
x2 <- x[2]
y2 <- y[2]
}
else stop("Unexpected NA")
result[i] <- y1 + (where[i] - x1)/(x2 - x1) *
(y2 - y1)
}
}
}
}
else result <- rep(y, length(where))
result
}
ad <- matrix(NA, nrow = 5, ncol = 2)
ad[1, ] <- getad(1, myapprox(rr, rtau, myapprox(aa, ar, myapprox(atau, aa, 1))))
ad[2, ] <- getad(myapprox(aa, atau, myapprox(rr, ra, myapprox(rtau, rr, 1))), 1)
ad[3, ] <- getad(-1, myapprox(rr, rtau, myapprox(aa, ar, myapprox(atau, aa, -1))))
ad[4, ] <- getad(myapprox(aa, atau, myapprox(rr, ra, myapprox(rtau, rr, -1))), -1)
i <- order(ad[1:4, 1])
ad[1:4, ] <- ad[i, ]
ad[5, 1] <- ad[1, 1] + 2 * pi
ad[5, 2] <- ad[1, 2]
ad <- ad[!duplicated(ad[, 1]), ]
adv <- spline(ad, n = npoints, method= "periodic")
avals <- adv$x
dvals <- adv$y
matrix(c(myapprox(atau, aa, cos(avals + dvals/2)), myapprox(rtau, rr, cos(avals - dvals/2))), length(avals), 2, dimnames = list(NULL, names(x[which])))
}
ellipse/R/pairs.profile.R 0000644 0001762 0000144 00000010001 14456265506 015015 0 ustar ligges users pairs <- function(x, ...) {
if (inherits(x, "profile"))
.Deprecated("pairs_profile", msg = "ellipse::pairs is deprecated for profile objects. Use ellipse::pairs_profile instead.")
UseMethod("pairs")
}
.pairs_default <- function(x, ...)
graphics::pairs(x, ...)
.pairs_formula <- function(formula, data = NULL, ..., subset, na.action = stats::na.pass) {
# This code is copied from graphics:::pairs.formula
# because the non-standard evaluation wouldn't survive
# the indirect call.
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, parent.frame())))
m$data <- as.data.frame(data)
m$... <- NULL
m$na.action <- na.action
m[[1L]] <- quote(stats::model.frame)
mf <- eval(m, parent.frame())
graphics::pairs(mf, ...)
}
pairs_profile <-
function (x, labels = c(names(x), "Profile tau"), panel = lines,
invert = TRUE, plot.tau = TRUE, plot.trace = TRUE, plot.sketch = TRUE,
plot.ellipse = FALSE, level = 0.95, ...)
{
stopifnot(inherits(x, "profile"))
doaxis <- function(which, dolabel = TRUE) axis(which, labels = dolabel) # outer = TRUE, line = -0.5, labels = dolabel)
setup <- function(x, y, ...) plot(range(x[!is.na(x)]),
range(y[!is.na(y)]), type = "n", axes = FALSE, ...)
if (is.character(panel))
panel <- get(panel, mode = "function")
n <- length(x)
if (plot.tau)
n <- n + 1
oldpar <- par("oma", "mar", "cex", "tck", "mgp", "mex",
"mfrow")
oldcex <- par("cex")
CEX <- oldcex * max(7.7/(2 * n + 3), 0.6)
par(mfrow = c(n, n), mgp = c(2, 0.8, 0), oma = rep(3, 4),
mar = rep(0.5, 4), tck = -0.03/n)
on.exit({
par(oldpar)
})
par(cex = CEX)
if (length(labels) < n)
labels <- paste(deparse(substitute(x)), "[,", 1:n, "]",
sep = "")
if (par("pty") == "s") {
dif <- diff(par("fin"))/2
if (dif > 0)
par(omi = c(dif * n, 0, dif * n, 0) + par("omi"))
else par(omi = c(0, (-dif) * n, 0, (-dif) * n) + par("omi"))
}
alltau <- unlist(lapply(x, function(x) x[[1]]), use.names = FALSE)
order <- if (invert)
1:n
else n:1
for (i in order) {
for (j in 1:n) {
if (i <= length(x)) {
icomp <- x[[i]]
ipars <- as.matrix(icomp[[2]])
}
if (j <= length(x)) {
jcomp <- x[[j]]
jpars <- as.matrix(jcomp[[2]])
}
xx1 <- NA
xx2 <- NA
yy1 <- NA
yy2 <- NA
if (i <= length(x)) {
yy1 <- ipars[, i]
if (j <= length(x)) {
xx1 <- ipars[, j]
xx2 <- jpars[, j]
yy2 <- jpars[, i]
}
else {
xx1 <- icomp[[1]]
}
}
else {
yy1 <- jcomp[[1]]
if (j <= length(x)) {
xx1 <- jpars[, j]
}
}
xx <- c(xx1, NA, xx2)
yy <- c(yy1, NA, yy2)
if (i <= length(x)) {
if (j <= length(x))
setup(xx, yy, ...)
else setup(alltau, yy, ...)
}
else {
if (j <= length(x))
setup(xx, alltau, ...)
else setup(alltau, alltau)
}
box()
if (i == 1)
doaxis(3, j%%2 == 0)
if (i == n)
doaxis(1, j%%2 == 1)
if (j == 1)
doaxis(2, i%%2 == 0)
if (j == n)
doaxis(4, i%%2 == 1)
if (i != j) {
if ((i <= length(x)) && (j <= length(x))) {
if (plot.trace)
panel(xx, yy, ...)
if (plot.sketch)
for (l in level) panel(ellipse(x, which = c(j,
i), level = l), ...)
if (plot.ellipse && !is.null(fit <- attr(x,
"original.fit")))
for (l in level) panel(ellipse(fit, which = c(j,
i), level = l), ...)
}
else if (plot.tau)
panel(xx, yy, ...)
}
else {
par(usr = c(0, 1, 0, 1))
text(0.5, 0.5, labels[i], cex = 1.5 * CEX)
}
}
}
invisible()
}
pairs.profile <- pairs_profile ellipse/NEWS.md 0000644 0001762 0000144 00000003032 14456276007 013016 0 ustar ligges users ## Version 0.5.0
* The `pairs()` generic and `pairs.profile()` method have both
been deprecated, and replaced with the `pairs_profile()` function.
## Version 0.4.5
* `center` was ignored in `ellipse.default()`.
## Version 0.4.4
* Put source on Github.
* Added a `NEWS.md` file to track changes to the package.
* Added `README.Rmd`.
* Added `pkgdown` site.
* Added US spelling `center` to `ellipse.default()` (thanks to
James Curran).
## Version 0.4.3
- At request of CRAN, fixed some references.
## Version 0.4.2
- Update maintainer email.
## Version 0.4.1
- At request of CRAN, added `pairs()` generic function to avoid
clash with `MASS::pairs.profile()`
- other minor cleanups.
## Version 0.3.8
- Minor fix (no more `\synopsis` tag in help)
## Version 0.3.7
- Minor fix (clean up DESCRIPTION file)
## Version 0.3.6
- Bug fix: `...` was not being passed from methods to `ellipse.default()`.
## Version 0.3.5
- License clarification
## Version 0.3.4
- Update to strict test compliance
## Version 0.3.3
- Nicer colors in `plotcorr()` example (from Gregor Gorjanc)
## Version 0.3.2
- Remove deprecated `install.R`
## Version 0.3.1
- Added NAMESPACE, package overview topic
- Allowed `plotcorr()` to plot just upper or lower triangle
of matrix.
## Version 0.2.14
- Allowed separate colors for each ellipse in `plotcorr()`
and fixed some `.Rd` errors.
## Version 0.2.12
- Bug fix for missing values in `plotcorr()`, doc error
## Version 0.0.1
- First CRAN release
ellipse/MD5 0000644 0001762 0000144 00000003010 14456303212 012211 0 ustar ligges users 2393b07c5b3d4f084aee653e22bd61e1 *DESCRIPTION
c36867f0ca9e93bbe4cb76e43d071e5b *NAMESPACE
410b363885c52f4b1ab38185511cabd1 *NEWS.md
73fea51373da9bbc4ff85c413745b526 *R/ellipse.R
4be8275a614fcb4065c37b8762941247 *R/ellipse.arima0.R
fa50df8672315eda7e472a308039914d *R/ellipse.default.R
3c552169931b0006165ac5b1ae888eb3 *R/ellipse.glm.R
f3a81546119e55c3f45191add9fea049 *R/ellipse.lm.R
84a3d7dd1aa99c82424453da914feaaa *R/ellipse.nls.R
1f6cc6a9602728096e1a5c17db75a3d1 *R/ellipse.profile.R
179ff82c9db9ac24d7c28250d7dbd4ac *R/ellipse.profile.glm.R
e9db7c17f6821149220b411bfd6441a6 *R/ellipse.profile.nls.R
7e7874f0597fde230d8112ce1262e282 *R/pairs.profile.R
f410502cb27f5f2cfaee61bcf133abe3 *R/plotcorr.R
ba3be347e3ed8272ffa6fd5e39a4de87 *README.md
7080893e02c49cd296d4424b9be55069 *build/partial.rdb
9d18b036d7fe5945f3878be9871c0c05 *man/ellipse-package.Rd
9409a7bef609ccc13a41970539583435 *man/ellipse.Rd
2a7bc110f3041d43ddd9ae601bf81912 *man/ellipse.arima0.Rd
13796f5667a8b5b3c3eda8778ec508b1 *man/ellipse.glm.Rd
8de4342bb0e4fd05f84cd2f244d3a58a *man/ellipse.lm.Rd
a44b6aebadb5c6335f6e711b23152475 *man/ellipse.nls.Rd
144c1f7486400314ca265a1c98278628 *man/ellipse.profile.Rd
15b9baaa7097e4c553b36d4039c808ea *man/ellipse.profile.glm.Rd
9648870599fe1802b3c344d74825dffa *man/ellipse.profile.nls.Rd
a748689269d7422ec285351a622f049b *man/figures/README-pairs.profile-1.png
0349c605fbc4848a60234ab396386459 *man/figures/README-plotcorr-1.png
569f000c9673298db43faa2a95578db9 *man/pairs.profile.Rd
1e4a078912e531deddde4170a259cc48 *man/plotcorr.Rd
The `plotcorr()` function can plot a matrix of ellipses:
``` r
library(ellipse)
#>
#> Attaching package: 'ellipse'
#> The following object is masked from 'package:graphics':
#>
#> pairs
corr.mtcars <- cor(mtcars)
ord <- order(corr.mtcars[1,])
xc <- corr.mtcars[ord, ord]
colors <- c("#A50F15","#DE2D26","#FB6A4A","#FCAE91","#FEE5D9","white",
"#EFF3FF","#BDD7E7","#6BAED6","#3182BD","#08519C")
plotcorr(xc, col=colors[5*xc + 6])
```
ellipse/man/ 0000755 0001762 0000144 00000000000 14456231120 012460 5 ustar ligges users ellipse/man/pairs.profile.Rd 0000644 0001762 0000144 00000006034 14456232001 015526 0 ustar ligges users \name{pairs_profile}
\alias{pairs_profile}
\alias{pairs.profile}
\alias{ellipse-deprecated}
\alias{pairs}
\title{
Profile pairs
}
\description{
This function produces pairwise plots of profile traces, profile
sketches, and ellipse approximations to confidence intervals.
}
\usage{
pairs_profile(x, labels = c(names(x), "Profile tau"), panel = lines, invert = TRUE,
plot.tau = TRUE, plot.trace = TRUE, plot.sketch = TRUE,
plot.ellipse = FALSE, level = 0.95, \dots)
# Deprecated generic function. Use graphics::pairs instead.
pairs(x, ...)
}
\arguments{
\item{x}{
An object of class \code{profile}, generally the result of the \code{profile()}
function.
}
\item{labels}{
The labels to use for each variable. These default to the variable
names.
}
\item{panel}{
The function to use to draw the sketch in each panel.
}
\item{invert}{
Whether to swap the axes so things look better.
}
\item{plot.tau}{
Whether to do the profile tau (profile t) plots.
}
\item{plot.trace}{
Whether to do the profile trace plots.
}
\item{plot.sketch}{
Whether to do the profile sketch plots.
}
\item{plot.ellipse}{
Whether to do the ellipse approximations.
}
\item{level}{
The nominal confidence level for the profile sketches and ellipses.
}
\item{\dots}{
Other plotting parameters.
}}
\section{Side Effects}{
Produces a plot on the current device for each pair of variables in the
profile object.
}
\details{
This function implements the plots used in Bates and Watts (1988) for
nonlinear regression diagnostics.
Prior to \pkg{ellipse} version 0.5,
the \code{pairs_profile} function was a \code{profile}
method for the \code{pairs} generic. This caused
various conflicts, because \pkg{graphics} also exports a \code{pairs}
generic, and package \pkg{MASS} exported a \code{profile}
method for \code{graphics::pairs}. As of \R version 4.4.0,
the \pkg{MASS} method will be in \pkg{stats} instead.
If \code{x} is a profile object then \code{pairs_profile(x)}
will call the function from this package. If you'd rather use
the \pkg{MASS}/\pkg{stats} method, then make sure the appropriate
package is loaded, and call \code{pairs(x)}. (Prior to
\pkg{ellipse} 0.5, there were complicated rules to determine what
\code{pairs(x)} would do; those should still work for now, but
\code{ellipse::pairs} will disappear in a future release.)
}
\references{
Bates and Watts (1988). Nonlinear Regression Analysis and Its Applications. Wiley. \doi{10.1002/9780470316757}.
}
\seealso{
\code{\link{pairs}}, \code{\link{profile}}, \code{\link{ellipse.profile}}, \code{\link{ellipse.nls}}
}
\examples{
# Plot everything for the Puromycin data
data(Puromycin)
Purboth <- nls(formula = rate ~ ((Vm + delV * (state == "treated"))
* conc)/(K + conc), data = Puromycin,
start = list(Vm = 160, delV = 40, K = 0.05))
Pur.prof <- profile(Purboth)
pairs_profile(Pur.prof, plot.ellipse = TRUE)
# Show the corresponding plot from MASS/stats:
if (getRversion() < "4.4.0") {
loadNamespace("MASS")
} else
loadNamespace("stats")
graphics::pairs(Pur.prof)
}
\keyword{dplot}
\keyword{regression}
\keyword{nonlinear}
ellipse/man/ellipse.profile.nls.Rd 0000644 0001762 0000144 00000003731 14245466215 016655 0 ustar ligges users \name{ellipse.profile.nls}
\alias{ellipse.profile.nls}
\title{
Pairwise profile sketch
}
\description{
This routine approximates a pairwise confidence region for a nonlinear regression
model.
}
\usage{
\method{ellipse}{profile.nls}(x, which = c(1, 2), level = 0.95,
t = sqrt(2 * qf(level, 2, attr(x, "summary")$df[2])),
npoints = 100, \dots)
}
\arguments{
\item{x}{
An object of class \code{\link{profile.nls}}.
}
\item{which}{
Which pair of parameters to use.
}
\item{level}{
The \code{level} argument
specifies the confidence level for an asymptotic confidence region.
}
\item{t}{
The square root of the value to be contoured.
}
\item{npoints}{
How many points to use in the ellipse.
}
\item{\dots}{
Extra parameters which are not used (for compatibility with the generic).
}
}
\value{
An \code{npoints} x \code{2} matrix with columns having the chosen parameter names,
which approximates a contour of the function that was profiled.
}
\details{
This function uses the 4 point approximation to the contour as described
in Appendix 6 of Bates and Watts (1988). It produces the exact contour for
quadratic surfaces, and good approximations for mild deviations from
quadratic. If the surface is multimodal, the algorithm is likely to
produce nonsense.
}
\references{
Bates and Watts (1988). Nonlinear Regression Analysis and Its Applications. Wiley. \doi{10.1002/9780470316757}.
}
\seealso{
\code{\link{profile}}, \code{\link{ellipse.nls}}
}
\examples{
# Plot an approximate 95\% confidence region for the Puromycin
# parameters Vm and K, and overlay the ellipsoidal region
data(Puromycin)
Purboth <- nls(formula = rate ~ ((Vm + delV * (state == "treated"))
* conc)/(K + conc), data = Puromycin,
start = list(Vm = 160, delV = 40, K = 0.05))
Pur.prof <- profile(Purboth)
plot(ellipse(Pur.prof, which = c('Vm', 'K')), type = 'l')
lines(ellipse(Purboth, which = c('Vm', 'K')), lty = 2)
params <- Purboth$m$getPars()
points(params['Vm'],params['K'])
}
\keyword{dplot}
\keyword{models}
ellipse/man/plotcorr.Rd 0000644 0001762 0000144 00000005521 14245466215 014631 0 ustar ligges users \name{plotcorr}
\alias{plotcorr}
\title{
Plot correlation matrix ellipses
}
\description{
This function plots a correlation matrix using ellipse-shaped glyphs for
each entry. The ellipse represents a level curve of the density of a
bivariate normal with the matching correlation.
}
\usage{
plotcorr(corr, outline = TRUE, col = 'grey', numbers = FALSE,
type = c("full","lower","upper"),
diag = (type == "full"), bty = "n", axes = FALSE,
xlab = "", ylab = "", asp = 1,
cex.lab = par("cex.lab"), cex = 0.75*par("cex"),
mar = 0.1 + c(2,2,4,2), ...)
}
\arguments{
\item{corr}{
A matrix containing entries between \code{-1} and \code{1} to be plotted as
correlations.
}
\item{outline}{
Whether the ellipses should be outlined in the default colour.
}
\item{col}{
Which colour(s) to use to fill the ellipses.
}
\item{numbers}{
Whether to plot numerical correlations in place of ellipses. If
numbers is \code{TRUE}, then the correlations will be rounded to a single decimal
place and placed on the plot.
}
\item{type}{Character. Plot \code{"full"} matrix or just \code{"upper"} or
\code{"lower"} triangular part of it.}
\item{diag}{Logical. Plot diagonal elements or not. }
\item{bty, axes, xlab, ylab, asp, mar, cex.lab, ...}{Graphical parameters
which will be passed to \code{\link{plot}} when plotting.}
\item{cex}{Graphical parameter
which will be passed to \code{\link{text}} when plotting.}
}
\details{
The ellipses being plotted will be tangent to a unit character square,
with the shape chosen to match the required correlation. If \code{numbers = FALSE},
the \code{col} vector will be recycled to colour each of the ellipses; if
\code{TRUE}, it will be ignored.
}
\references{
Murdoch, D.J. and Chow, E.D. (1996). A graphical display of large
correlation matrices. The American Statistician 50, 178-180. \doi{10.2307/2684435}.
}
\seealso{
\code{\link{ellipse}}
}
\author{Duncan Murdoch; Gregor Gorjanc suggested the \code{type} and \code{diag}
options.}
\examples{
save.par <- par(ask = interactive())
# Plot the correlation matrix for the mtcars data full model fit
data(mtcars)
fit <- lm(mpg ~ ., mtcars)
plotcorr(summary(fit, correlation = TRUE)$correlation)
# Plot a second figure with numbers in place of the
# ellipses
plotcorr(summary(fit, correlation = TRUE)$correlation, numbers = TRUE)
# Colour the ellipses to emphasize the differences. The color range
# is based on RColorBrewer's Reds and Blues (suggested by Gregor Gorjanc)
corr.mtcars <- cor(mtcars)
ord <- order(corr.mtcars[1,])
xc <- corr.mtcars[ord, ord]
colors <- c("#A50F15","#DE2D26","#FB6A4A","#FCAE91","#FEE5D9","white",
"#EFF3FF","#BDD7E7","#6BAED6","#3182BD","#08519C")
plotcorr(xc, col=colors[5*xc + 6])
plotcorr(xc, col=colors[5*xc + 6], type = "upper")
plotcorr(xc, col=colors[5*xc + 6], type = "lower", diag = TRUE)
par(save.par)
}
\keyword{hplot}
ellipse/man/ellipse.lm.Rd 0000644 0001762 0000144 00000002523 13663503243 015024 0 ustar ligges users \name{ellipse.lm}
\alias{ellipse.lm}
\title{
Outline a pairwise confidence region for a linear model fit.
}
\description{
This function produces the ellipsoidal outline of a pairwise confidence
region for a linear model fit.
}
\usage{
\method{ellipse}{lm}(x, which = c(1, 2), level = 0.95,
t = sqrt(2 * qf(level, 2, x$df.residual)), \dots)
}
\arguments{
\item{x}{
The first argument should be an \code{lm} object, usually resulting from a call
to \code{lm()}.
}
\item{which}{
Which selects the pair of parameters to be plotted. The default is the
first two.
}
\item{level}{
The confidence level of the region. Default 95\%.
}
\item{t}{
The t statistic on the boundary of the ellipse.
}
\item{\dots}{
Other \code{ellipse.default} parameters may also be used.
}}
\value{
A matrix with columns \code{x} and \code{y} to outline the confidence region.
}
\details{
The summary function is used to obtain the covariance matrix of the
fitted parameters.
}
\seealso{
\code{\link{ellipse.default}}
}
\examples{
# Plot the estimate and joint 90\% confidence region for the displacement and cylinder
# count linear coefficients in the mtcars dataset
data(mtcars)
fit <- lm(mpg ~ disp + cyl , mtcars)
plot(ellipse(fit, which = c('disp', 'cyl'), level = 0.90), type = 'l')
points(fit$coefficients['disp'], fit$coefficients['cyl'])
}
\keyword{dplot}
\keyword{regression}
ellipse/man/ellipse.glm.Rd 0000644 0001762 0000144 00000003670 13663503243 015177 0 ustar ligges users \name{ellipse.glm}
\alias{ellipse.glm}
\title{
Outline an approximate pairwise confidence region
}
\description{
This function produces the ellipsoidal outline of an approximate
pairwise confidence region for a generalized linear model fit.
}
\usage{
\method{ellipse}{glm}(x, which = c(1, 2), level = 0.95, t, npoints = 100,
dispersion, \dots)
}
\arguments{
\item{x}{
The first argument should be a \code{glm} object, usually resulting from a call
to \code{glm()}.
}
\item{which}{
Which selects the pair of parameters to be plotted. The default is the
first two.
}
\item{level}{
The confidence level of the region. Default 95\%.
}
\item{t}{
The t statistic on the boundary of the ellipse. For Binomial or Poisson
families, \code{sqrt(qchisq(level, 2))} is used; for other distributions,
\code{sqrt(2*qf(level, 2, df))} where \code{df} is the residual degrees of freedom.
}
\item{npoints}{
How many points to return in the ellipse.
}
\item{dispersion}{
The value of dispersion to use. If specified, it is treated as fixed,
and the chi-square limits for \code{t} are used. If missing, it is
taken from \code{summary(x)}.
}
\item{\dots}{
Other \code{ellipse.default} parameters may also be used.
}}
\value{
A matrix with columns named according to which to outline the confidence region.
}
\details{
The summary function is used to obtain the approximate covariance matrix of the
fitted parameters, the dispersion estimate, and the degrees of freedom.
}
\seealso{
\code{\link{ellipse.default}}
}
\examples{
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
# Plot an approximate 95 \% confidence region for the two Outcome parameters
plot(ellipse(glm.D93, which = c(2,3)), type = 'l')
points(glm.D93$coefficients[2], glm.D93$coefficients[3])
}
\keyword{dplot}
\keyword{regression}
ellipse/man/ellipse.profile.Rd 0000644 0001762 0000144 00000004114 14245466215 016056 0 ustar ligges users \name{ellipse.profile}
\alias{ellipse.profile}
\title{
Pairwise profile sketch
}
\description{
This routine approximates a contour of a function based on the profile
of that function.
}
\usage{
\method{ellipse}{profile}(x, which = c(1, 2), level = 0.95, t = sqrt(qchisq(level, 2)),
npoints = 100, \dots)
}
\arguments{
\item{x}{
An object of class \code{\link{profile}}, e.g. from
\link[MASS:confint]{profile.glm} in the MASS package.
}
\item{which}{
Which pair of parameters to use.
}
\item{level}{
The \code{ellipse.profile} function defaults assume that the profiled
function is -2 times the log likelihood of a regular model.
With this assumption the \code{level} argument
specifies the confidence level for an asymptotic confidence region.
}
\item{t}{
The square root of the value to be contoured.
}
\item{npoints}{
How many points to use in the ellipse.
}
\item{\dots}{
Extra arguments are not used.
}
}
\value{
An \code{npoints} x \code{2} matrix with columns having the chosen parameter names,
which approximates a contour of the function that was profiled.
}
\details{
This function uses the 4 point approximation to the contour as described
in Appendix 6 of Bates and Watts (1988). It produces the exact contour for
quadratic surfaces, and good approximations for mild deviations from
quadratic. If the surface is multimodal, the algorithm is likely to
produce nonsense.
}
\references{
Bates and Watts (1988). Nonlinear Regression Analysis and Its Applications. Wiley. \doi{10.1002/9780470316757}.
}
\seealso{
\code{\link{profile}}, \code{\link{ellipse.nls}}
}
\examples{
# Plot an approximate 95\% confidence region for the Puromycin
# parameters Vm and K, and overlay the ellipsoidal region
data(Puromycin)
Purboth <- nls(formula = rate ~ ((Vm + delV * (state == "treated"))
* conc)/(K + conc), data = Puromycin,
start = list(Vm = 160, delV = 40, K = 0.05))
Pur.prof <- profile(Purboth)
plot(ellipse(Pur.prof, which = c('Vm', 'K')), type = 'l')
lines(ellipse(Purboth, which = c('Vm', 'K')), lty = 2)
params <- Purboth$m$getPars()
points(params['Vm'],params['K'])
}
\keyword{dplot}
\keyword{models}
ellipse/man/ellipse.Rd 0000644 0001762 0000144 00000005060 14411031702 014400 0 ustar ligges users \name{ellipse}
\alias{ellipse}
\alias{ellipse.default}
\title{
Make an ellipse
}
\description{
A generic function returning an ellipse or other outline of a confidence region
for two parameters.
}
\usage{
ellipse(x, \dots)
\method{ellipse}{default}(x, scale = c(1, 1), centre = c(0, 0), level = 0.95,
t = sqrt(qchisq(level, 2)), which = c(1, 2), npoints = 100, center = centre,
\dots)
}
\arguments{
\item{x}{
An object. In the default method the parameter \code{x} should be a correlation between -1 and 1 or a
square positive definite matrix at least 2x2
in size. It will be treated as the correlation or covariance
of a multivariate normal distribution.
}
\item{\dots}{
Descendant methods may require additional parameters.
}
\item{scale}{
If \code{x} is a correlation matrix, then the standard deviations of each
parameter can be given in the scale parameter. This defaults to \code{c(1, 1)},
so no rescaling will be done.
}
\item{centre}{
The centre of the ellipse will be at this position.
}
\item{level}{
The confidence level of a pairwise confidence region. The default is
0.95, for a 95\% region. This is used to control the size of the ellipse
being plotted. A vector of levels may be used.
}
\item{t}{
The size of the ellipse may also be controlled by specifying the value
of a t-statistic on its boundary. This defaults to the appropriate
value for the confidence region.
}
\item{which}{
This parameter selects which pair of variables from the matrix will be
plotted. The default is the first 2.
}
\item{npoints}{
The number of points used in the ellipse. Default is 100.
}
\item{center}{
An alternative to \code{centre} to accommodate US spelling.
}
}
\value{
An \code{npoints} x \code{2} matrix is returned with columns named according to the
row names of the matrix \code{x} (default \code{'x'} and \code{'y'}), suitable
for plotting.
}
\details{
The default method uses the
\code{(cos(theta + d/2), cos(theta - d/2))} parametrization of an ellipse, where
\code{cos(d)} is the correlation of the parameters.
}
\seealso{
\code{\link{ellipse.lm}}, \code{\link{ellipse.nls}},
\code{\link{ellipse.profile}}, \code{\link{ellipse.profile.nls}},
\code{\link{ellipse.arima0}},
\code{\link{plotcorr}}
}
\references{
Murdoch, D.J. and Chow, E.D. (1996). A graphical display of large
correlation matrices. The American Statistician 50, 178-180. \doi{10.2307/2684435}.
}
\examples{
# Plot an ellipse corresponding to a 95\% probability region for a
# bivariate normal distribution with mean 0, unit variances and
# correlation 0.8.
plot(ellipse(0.8), type = 'l')
}
\keyword{dplot}
ellipse/man/ellipse.profile.glm.Rd 0000644 0001762 0000144 00000005006 14411031702 016615 0 ustar ligges users \name{ellipse.profile.glm}
\alias{ellipse.profile.glm}
\title{
Pairwise profile sketch for GLM profiles
}
\description{
This routine approximates a pairwise confidence region for a \code{glm}
model.
}
\usage{
\method{ellipse}{profile.glm}(x, which = c(1, 2), level = 0.95, t,
npoints = 100, dispersion, \dots)
}
\arguments{
\item{x}{
An object of class \code{\link[MASS]{profile.glm}}.
}
\item{which}{
Which pair of parameters to use.
}
\item{level}{
The \code{level} argument
specifies the confidence level for an asymptotic confidence region.
}
\item{t}{
The square root of the value to be contoured. By default, this is \code{qchisq(level, 2)}
for models with fixed dispersion (i.e. binomial and Poisson), and \code{2 * qf(level, 2, df)} for other models,
where \code{df} is the residual degrees of freedom.
}
\item{npoints}{
How many points to use in the ellipse.
}
\item{dispersion}{
If specified, fixed dispersion is assumed, otherwise the dispersion is taken from the model.
}
\item{\dots}{
Extra parameters which are not used (for compatibility with the generic).
}
}
\value{
An \code{npoints} x \code{2} matrix with columns having the chosen parameter names,
which approximates a contour of the function that was profiled.
}
\details{
This function uses the 4 point approximation to the contour as described
in Appendix 6 of Bates and Watts (1988). It produces the exact contour for
quadratic surfaces, and good approximations for mild deviations from
quadratic. If the surface is multimodal, the algorithm is likely to
produce nonsense.
}
\references{
Bates and Watts (1988). Nonlinear Regression Analysis and Its Applications. Wiley. \doi{10.1002/9780470316757}.
}
\seealso{
\code{\link{profile}}, \code{\link{glm}}, \code{\link{ellipse.glm}}
}
\examples{
## MASS has a pairs.profile function that conflicts with ours, so
## do a little trickery here
noMASS <- is.na(match('package:MASS', search()))
if (noMASS) require(MASS)
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
## Plot an approximate 95\% confidence region for the two outcome variables
prof.D93 <- profile(glm.D93)
plot(ellipse(prof.D93, which = 2:3), type = 'l')
lines(ellipse(glm.D93, which = 2:3), lty = 2)
params <- glm.D93$coefficients
points(params[2],params[3])
## Clean up our trickery
if (noMASS) detach('package:MASS')
}
\keyword{dplot}
\keyword{models}
ellipse/man/figures/ 0000755 0001762 0000144 00000000000 14411030433 014117 5 ustar ligges users ellipse/man/figures/README-plotcorr-1.png 0000644 0001762 0000144 00000277214 14411030433 017577 0 ustar ligges users PNG
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