VCA (version 1.3.1)

plotRandVar: Plot Random Variates of a Mixed Model ('VCA' Object).

Description

Plots, possibly transformed, random variates of a linear mixed model (random effects, contitional or marginal residuals).

Usage

plotRandVar(obj, term = NULL, mode = c("raw", "student", "standard", "pearson"), main = NULL, Xlabels = list(), Points = list(), Vlines = list(), pick = FALSE)

Arguments

obj

(VCA) object

term

(character, integer) specifying a type of residuals if one of c("conditional", "marginal"), or, the name of a random term (one of obj$re.assign$terms). If 'term' is a integer, it is interpreted as the i-th random term in 'obj$re.assign$terms'.

mode

(character) string specifying a possible transformation of random effects or residuals (see residuals.VCA and ranef.VCAfor details)

main

(character) string used as main title of the plot, if NULL, it will be automatically generated

Xlabels

(list) passed to function text adding labels to the bottom margin at x-coordinates 1:N, where N is the number of random variates. Useful for customization.

Points

(list) passed to function points for customization of plotting symbols

Vlines

(list) passed to function (abline) adding vertical lines, separating random variates for better visual separation, set to NULL for omitting vertical lines.

pick

(logical) TRUE = lets the user identify single points using the mouse, useful, when many, points were drawn where the X-labels are not readable.

Details

Function plots either random effects of a 'VCA' object or residuals. Parameter 'term' is used to specify either one. If 'term' is one of c("conditional", "marginal") corresponding residuals will be plotted (see resid for details). If 'term' is either the name of a random term in the formula of the 'VCA' object or an integer specifying the i-th random term, corresponding random effects will be plotted. Both types of random variates (random effects, residuals) can be plotted untransformed ("raw"), "studentized" or "standardized". In case of residuals, one can also use the "Pearson"-type transformation.

Examples

## Not run: 
# data(dataEP05A2_1)
# fit <- anovaVCA(y~day/run, dataEP05A2_1)
# # solve mixed model equations including random effects
# fit <- solveMME(fit)
# plotRandVar(fit, "cond", "stand")
# plotRandVar(fit, 1, "stud")						# 1st random term 'day'
# plotRandVar(fit, "day", "stud")					# equivalent to the above
# 
# # for larger datasets residuals can hardly be identified
# # pick out interesting points with the mouse
# 
# plotRandVar(fit, "marg", "stud", pick=TRUE)
# 
# # customize the appearance
# plotRandVar( fit, 1, "stud", Vlines=list(col=c("red", "darkgreen")),
# 	Xlabels=list(offset=.5, srt=60, cex=1, col="blue"),
# 	Points=list(col=c("black", "red", rep("black", 18)),
# 	pch=c(3,17,rep(3,18)), cex=c(1,2,rep(1,18))))
# ## End(Not run)