Diagnostic Plots for 'merMod' Fits
diagnostic plots for merMod fits
## S3 method for class 'merMod'
plot(x,
form = resid(., type = "pearson") ~ fitted(.), abline,
id = NULL, idLabels = NULL, grid, ...)
## S3 method for class 'merMod'
qqmath(x, id = NULL, idLabels = NULL, ...)x |
a fitted [ng]lmer model |
form |
an optional formula specifying the desired
type of plot. Any variable present in the original data
frame used to obtain |
abline |
an optional numeric value, or numeric vector of length two. If given as a single value, a horizontal line will be added to the plot at that coordinate; else, if given as a vector, its values are used as the intercept and slope for a line added to the plot. If missing, no lines are added to the plot. |
id |
an optional numeric value, or one-sided
formula. If given as a value, it is used as a
significance level for a two-sided outlier test for the
standardized, or normalized residuals. Observations with
absolute standardized (normalized) residuals greater than
the 1-value/2 quantile of the standard normal
distribution are identified in the plot using
|
idLabels |
an optional vector, or one-sided formula.
If given as a vector, it is converted to character and
used to label the observations identified according to
|
grid |
an optional logical value indicating whether
a grid should be added to plot. Default depends on the
type of lattice plot used: if |
... |
optional arguments passed to the lattice plot function. |
Diagnostic plots for the linear mixed-effects fit are
obtained. The form argument gives considerable
flexibility in the type of plot specification. A
conditioning expression (on the right side of a |
operator) always implies that different panels are used
for each level of the conditioning factor, according to a
lattice display. If form is a one-sided formula,
histograms of the variable on the right hand side of the
formula, before a | operator, are displayed (the
lattice function histogram is used). If
form is two-sided and both its left and right hand
side variables are numeric, scatter plots are displayed
(the lattice function xyplot is used). Finally, if
form is two-sided and its left had side variable
is a factor, box-plots of the right hand side variable by
the levels of the left hand side variable are displayed
(the lattice function bwplot is used).
qqmath produces a Q-Q plot of the residuals
(see qqmath.ranef.mer for Q-Q plots of the
conditional mode values).
original version in nlme package by Jose Pinheiro and Douglas Bates.
data(Orthodont,package="nlme")
fm1 <- lmer(distance ~ age + (age|Subject), data=Orthodont)
## standardized residuals versus fitted values by gender
plot(fm1, resid(., scaled=TRUE) ~ fitted(.) | Sex, abline = 0)
## box-plots of residuals by Subject
plot(fm1, Subject ~ resid(., scaled=TRUE))
## observed versus fitted values by Subject
plot(fm1, distance ~ fitted(.) | Subject, abline = c(0,1))
## residuals by age, separated by Subject
plot(fm1, resid(., scaled=TRUE) ~ age | Sex, abline = 0)
library(lattice) ## needed for qqmath
qqmath(fm1, id=0.05)
ggp.there <- "package:ggplot2" %in% search()
if (ggp.there || require("ggplot2")) {
## we can create the same plots using ggplot2 and the fortify() function
fm1F <- fortify.merMod(fm1)
ggplot(fm1F, aes(.fitted,.resid)) + geom_point(colour="blue") +
facet_grid(.~Sex) + geom_hline(yintercept=0)
## note: Subjects are ordered by mean distance
ggplot(fm1F, aes(Subject,.resid)) + geom_boxplot() + coord_flip()
ggplot(fm1F, aes(.fitted,distance)) + geom_point(colour="blue") +
facet_wrap(~Subject) +geom_abline(intercept=0,slope=1)
ggplot(fm1F, aes(age,.resid)) + geom_point(colour="blue") + facet_grid(.~Sex) +
geom_hline(yintercept=0)+ geom_line(aes(group=Subject),alpha=0.4) +
geom_smooth(method="loess")
## (warnings about loess are due to having only 4 unique x values)
if(!ggp.there) detach("package:ggplot2")
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