plotRandVar | R Documentation |
Plots, possibly transformed, random variates of a linear mixed model (random effects, contitional or marginal residuals).
plotRandVar(
obj,
term = NULL,
mode = c("raw", "student", "standard", "pearson"),
main = NULL,
Xlabels = list(),
Points = list(),
Vlines = list(),
pick = FALSE,
...
)
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 |
main |
(character) string used as main title of the plot, if NULL, it will be automatically generated |
Xlabels |
(list) passed to function |
Points |
(list) passed to function |
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. |
... |
additional arguments to be passed to methods, such as graphical parameters (see |
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.
Andre Schuetzenmeister andre.schuetzenmeister@roche.com
## 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)
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