| plot.merMod {lme4} | R Documentation |
diagnostic plots for merMod fits
## S3 method for class 'merMod'
plot(x,
form = resid(., type = "pearson") ~ fitted(.), abline,
id = NULL, idLabels = NULL, grid, ...)
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).
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)
if (require(ggplot2)) {
## we can create the same plots using ggplot2 and the fortify() function
fm1F <- fortify(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)
detach("package:ggplot2")
}