Package 'ellipse' reference manual

Title:	Functions for Drawing Ellipses and Ellipse-Like Confidence Regions
Description:	Contains various routines for drawing ellipses and ellipse-like confidence regions, implementing the plots described in Murdoch and Chow (1996, <doi:10.2307/2684435>). There are also routines implementing the profile plots described in Bates and Watts (1988, <doi:10.1002/9780470316757>).
Authors:	Duncan Murdoch and E. D. Chow (porting to R by Jesus M. Frias Celayeta)
Maintainer:	Duncan Murdoch <[email protected]>
License:	GPL (>= 2)
Version:	0.5.0
Built:	2024-11-11 02:42:26 UTC
Source:	https://github.com/dmurdoch/ellipse

Functions for drawing ellipses and ellipse-like confidence regions

Description

This package 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).

Details

There are three groups of routines in the ellipse package. The first consists of those involved with plotcorr, which implements the plots described in Murdoch and Chow (1996). These display correlations using ellipses, whose shape is that of the contours of a bivariate normal distribution with matching correlation.

The second group implements a version of the profile plots described in Bates and Watts (1988); see ellipse.profile and pairs.profile.

The last group provide the basis for the others, drawing ellipses based on various S objects, including scalar correlations, covariance matrices arima, lm, and nls fits: see ellipse.

Author(s)

Duncan Murdoch and E. D. Chow (porting to R by Jesus M. Frias Celayeta.)

Maintainer: Duncan Murdoch <[email protected]>

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.

Make an ellipse

Description

A generic function returning an ellipse or other outline of a confidence region for two parameters.

Usage

ellipse(x, ...)
## Default S3 method:
ellipse(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,
    ...)
ellipse(x, ...)
## Default S3 method:
ellipse(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,
    ...)

Arguments

`x`	An object. In the default method the parameter `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.
`...`	Descendant methods may require additional parameters.
`scale`	If `x` is a correlation matrix, then the standard deviations of each parameter can be given in the scale parameter. This defaults to `c(1, 1)`, so no rescaling will be done.
`centre`	The centre of the ellipse will be at this position.
`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.
`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.
`which`	This parameter selects which pair of variables from the matrix will be plotted. The default is the first 2.
`npoints`	The number of points used in the ellipse. Default is 100.
`center`	An alternative to `centre` to accommodate US spelling.

Details

The default method uses the (cos(theta + d/2), cos(theta - d/2)) parametrization of an ellipse, where cos(d) is the correlation of the parameters.

Value

An npoints x 2 matrix is returned with columns named according to the row names of the matrix x (default 'x' and 'y'), suitable for plotting.

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')
# 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')

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

## S3 method for class 'arima0'
ellipse(x, which = c(1, 2), level = 0.95, t = sqrt(qchisq(level, 2)), ...)
## S3 method for class 'arima0'
ellipse(x, which = c(1, 2), level = 0.95, t = sqrt(qchisq(level, 2)), ...)

Arguments

`x`	The first argument should be an `arima0` object, usually resulting from a call to `arima0()`.
`which`	Which selects the pair of parameters to be plotted. The default is the first two.
`level`	The confidence level of the region. Default 95%.
`t`	The t statistic on the boundary of the ellipse.
`...`	Other `ellipse.default` parameters may also be used.

Details

The summary function is used to obtain the approximate covariance matrix of the fitted parameters.

Value

A matrix with columns x and y to outline the confidence region.

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])
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])

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

## S3 method for class 'glm'
ellipse(x, which = c(1, 2), level = 0.95, t, npoints = 100, 
        dispersion, ...)
## S3 method for class 'glm'
ellipse(x, which = c(1, 2), level = 0.95, t, npoints = 100, 
        dispersion, ...)

Arguments

`x`	The first argument should be a `glm` object, usually resulting from a call to `glm()`.
`which`	Which selects the pair of parameters to be plotted. The default is the first two.
`level`	The confidence level of the region. Default 95%.
`t`	The t statistic on the boundary of the ellipse. For Binomial or Poisson families, `sqrt(qchisq(level, 2))` is used; for other distributions, `sqrt(2*qf(level, 2, df))` where `df` is the residual degrees of freedom.
`npoints`	How many points to return in the ellipse.
`dispersion`	The value of dispersion to use. If specified, it is treated as fixed, and the chi-square limits for `t` are used. If missing, it is taken from `summary(x)`.
`...`	Other `ellipse.default` parameters may also be used.

Details

The summary function is used to obtain the approximate covariance matrix of the fitted parameters, the dispersion estimate, and the degrees of freedom.

Value

A matrix with columns named according to which to outline the confidence region.

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])
## 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])

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

## S3 method for class 'lm'
ellipse(x, which = c(1, 2), level = 0.95, 
    t = sqrt(2 * qf(level, 2, x$df.residual)), ...)
## S3 method for class 'lm'
ellipse(x, which = c(1, 2), level = 0.95, 
    t = sqrt(2 * qf(level, 2, x$df.residual)), ...)

Arguments

`x`	The first argument should be an `lm` object, usually resulting from a call to `lm()`.
`which`	Which selects the pair of parameters to be plotted. The default is the first two.
`level`	The confidence level of the region. Default 95%.
`t`	The t statistic on the boundary of the ellipse.
`...`	Other `ellipse.default` parameters may also be used.

Details

The summary function is used to obtain the covariance matrix of the fitted parameters.

Value

A matrix with columns x and y to outline the confidence region.

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'])
# 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'])

Outline an approximate pairwise confidence region

Description

This function produces the ellipsoidal outline of an approximate pairwise confidence region for a nonlinear model fit.

Usage

## S3 method for class 'nls'
ellipse(x, which = c(1, 2), level = 0.95, 
    t = sqrt(2 * qf(level, 2, s$df[2])), ...)
## S3 method for class 'nls'
ellipse(x, which = c(1, 2), level = 0.95, 
    t = sqrt(2 * qf(level, 2, s$df[2])), ...)

Arguments

`x`	The first argument should be an `nls` object, usually resulting from a call to `nls()`.
`which`	Which selects the pair of parameters to be plotted. The default is the first two.
`level`	The confidence level of the region. Default 95%.
`t`	The t statistic on the boundary of the ellipse.
`...`	Other `ellipse.default` parameters may also be used.

Details

The summary function is used to obtain the approximate covariance matrix of the fitted parameters.

Value

A matrix with columns x and y to outline the confidence region.

Examples

# Plot an approximate 95% confidence region for the weight and displacement
# parameters in the Michaelis Menten model
data(Puromycin)
fit <- nls(rate ~ Vm*conc/(K + conc), data = Puromycin, subset = state=="treated", 
    start = list(K = 0.05, Vm = 200))
plot(ellipse(fit,which=c('Vm','K')), type = 'l')
params <- fit$m$getPars()
points(params['Vm'],params['K'])
# Plot an approximate 95% confidence region for the weight and displacement
# parameters in the Michaelis Menten model
data(Puromycin)
fit <- nls(rate ~ Vm*conc/(K + conc), data = Puromycin, subset = state=="treated", 
    start = list(K = 0.05, Vm = 200))
plot(ellipse(fit,which=c('Vm','K')), type = 'l')
params <- fit$m$getPars()
points(params['Vm'],params['K'])

Pairwise profile sketch

Description

This routine approximates a contour of a function based on the profile of that function.

Usage

## S3 method for class 'profile'
ellipse(x, which = c(1, 2), level = 0.95, t = sqrt(qchisq(level, 2)), 
    npoints = 100, ...)
## S3 method for class 'profile'
ellipse(x, which = c(1, 2), level = 0.95, t = sqrt(qchisq(level, 2)), 
    npoints = 100, ...)

Arguments

`x`	An object of class `profile`, e.g. from profile.glm in the MASS package.
`which`	Which pair of parameters to use.
`level`	The `ellipse.profile` function defaults assume that the profiled function is -2 times the log likelihood of a regular model. With this assumption the `level` argument specifies the confidence level for an asymptotic confidence region.
`t`	The square root of the value to be contoured.
`npoints`	How many points to use in the ellipse.
`...`	Extra arguments are not used.

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.

Value

An npoints x 2 matrix with columns having the chosen parameter names, which approximates a contour of the function that was profiled.

References

Bates and Watts (1988). Nonlinear Regression Analysis and Its Applications. Wiley. doi:10.1002/9780470316757.

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'])
# 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'])

Pairwise profile sketch for GLM profiles

Description

This routine approximates a pairwise confidence region for a glm model.

Usage

## S3 method for class 'profile.glm'
ellipse(x, which = c(1, 2), level = 0.95, t, 
    npoints = 100, dispersion, ...)
## S3 method for class 'profile.glm'
ellipse(x, which = c(1, 2), level = 0.95, t, 
    npoints = 100, dispersion, ...)

Arguments

`x`	An object of class `profile.glm`.
`which`	Which pair of parameters to use.
`level`	The `level` argument specifies the confidence level for an asymptotic confidence region.
`t`	The square root of the value to be contoured. By default, this is `qchisq(level, 2)` for models with fixed dispersion (i.e. binomial and Poisson), and `2 * qf(level, 2, df)` for other models, where `df` is the residual degrees of freedom.
`npoints`	How many points to use in the ellipse.
`dispersion`	If specified, fixed dispersion is assumed, otherwise the dispersion is taken from the model.
`...`	Extra parameters which are not used (for compatibility with the generic).

Details

Value

An npoints x 2 matrix with columns having the chosen parameter names, which approximates a contour of the function that was profiled.

References

Bates and Watts (1988). Nonlinear Regression Analysis and Its Applications. Wiley. doi:10.1002/9780470316757.

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')
	 
## 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')

Pairwise profile sketch

Description

This routine approximates a pairwise confidence region for a nonlinear regression model.

Usage

## S3 method for class 'profile.nls'
ellipse(x, which = c(1, 2), level = 0.95, 
          t = sqrt(2 * qf(level, 2, attr(x, "summary")$df[2])), 
          npoints = 100, ...)
## S3 method for class 'profile.nls'
ellipse(x, which = c(1, 2), level = 0.95, 
          t = sqrt(2 * qf(level, 2, attr(x, "summary")$df[2])), 
          npoints = 100, ...)

Arguments

`x`	An object of class `profile.nls`.
`which`	Which pair of parameters to use.
`level`	The `level` argument specifies the confidence level for an asymptotic confidence region.
`t`	The square root of the value to be contoured.
`npoints`	How many points to use in the ellipse.
`...`	Extra parameters which are not used (for compatibility with the generic).

Details

Value

An npoints x 2 matrix with columns having the chosen parameter names, which approximates a contour of the function that was profiled.

References

Bates and Watts (1988). Nonlinear Regression Analysis and Its Applications. Wiley. doi:10.1002/9780470316757.

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'])
# 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'])

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, ...)
	
# Deprecated generic function.  Use graphics::pairs instead.
pairs(x, ...)
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, ...)
	
# Deprecated generic function.  Use graphics::pairs instead.
pairs(x, ...)

Arguments

`x`	An object of class `profile`, generally the result of the `profile()` function.
`labels`	The labels to use for each variable. These default to the variable names.
`panel`	The function to use to draw the sketch in each panel.
`invert`	Whether to swap the axes so things look better.
`plot.tau`	Whether to do the profile tau (profile t) plots.
`plot.trace`	Whether to do the profile trace plots.
`plot.sketch`	Whether to do the profile sketch plots.
`plot.ellipse`	Whether to do the ellipse approximations.
`level`	The nominal confidence level for the profile sketches and ellipses.
`...`	Other plotting parameters.

Details

This function implements the plots used in Bates and Watts (1988) for nonlinear regression diagnostics.

Prior to ellipse version 0.5, the pairs_profile function was a profile method for the pairs generic. This caused various conflicts, because graphics also exports a pairs generic, and package MASS exported a profile method for graphics::pairs. As of R version 4.4.0, the MASS method will be in stats instead.

If x is a profile object then pairs_profile(x) will call the function from this package. If you'd rather use the MASS/stats method, then make sure the appropriate package is loaded, and call pairs(x). (Prior to ellipse 0.5, there were complicated rules to determine what pairs(x) would do; those should still work for now, but ellipse::pairs will disappear in a future release.)

Side Effects

Produces a plot on the current device for each pair of variables in the profile object.

References

Bates and Watts (1988). Nonlinear Regression Analysis and Its Applications. Wiley. doi:10.1002/9780470316757.

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)
# 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)

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), ...)
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

`corr`	A matrix containing entries between `-1` and `1` to be plotted as correlations.
`outline`	Whether the ellipses should be outlined in the default colour.
`col`	Which colour(s) to use to fill the ellipses.
`numbers`	Whether to plot numerical correlations in place of ellipses. If numbers is `TRUE`, then the correlations will be rounded to a single decimal place and placed on the plot.
`type`	Character. Plot `"full"` matrix or just `"upper"` or `"lower"` triangular part of it.
`diag`	Logical. Plot diagonal elements or not.
`bty`, `axes`, `xlab`, `ylab`, `asp`, `mar`, `cex.lab`, `...`	Graphical parameters which will be passed to `plot` when plotting.
`cex`	Graphical parameter which will be passed to `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 numbers = FALSE, the col vector will be recycled to colour each of the ellipses; if TRUE, it will be ignored.

Author(s)

Duncan Murdoch; Gregor Gorjanc suggested the type and diag options.

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

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)
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)

Package 'ellipse'

Help Index

Functions for drawing ellipses and ellipse-like confidence regions

Description

Details

Author(s)

References

Make an ellipse

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Outline an approximate pairwise confidence region

Description

Usage

Arguments

Details

Value

See Also

Examples

Outline an approximate pairwise confidence region

Description

Usage

Arguments

Details

Value

See Also

Examples

Outline a pairwise confidence region for a linear model fit.

Description

Usage

Arguments

Details

Value

See Also

Examples

Outline an approximate pairwise confidence region

Description

Usage

Arguments

Details

Value

See Also

Examples

Pairwise profile sketch

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Pairwise profile sketch for GLM profiles

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Pairwise profile sketch

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Profile pairs

Description

Usage

Arguments

Details