plot.merMod

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Diagnostic Plots for 'merMod' Fits

diagnostic plots for merMod fits

Usage
# S3 method for merMod
plot(x,
form = resid(., type = "pearson") ~ fitted(.), abline,
id = NULL, idLabels = NULL, grid, …)
# S3 method for merMod
qqmath(x, id = NULL, idLabels = NULL, …)
Arguments
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 x can be referenced. In addition, x itself can be referenced in the formula using the symbol ".". Conditional expressions on the right of a | operator can be used to define separate panels in a lattice display. Default is resid(., type = "pearson") ~ fitted(.), corresponding to a plot of the standardized residuals versus fitted values.
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. If given as a one-sided formula, its right hand side must evaluate to a logical, integer, or character vector which is used to identify observations in the plot. If missing, no observations are identified.
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 id. If given as a vector, it is converted to character and used to label the observations identified according to id. If given as a one-sided formula, its right hand side must evaluate to a vector which is converted to character and used to label the identified observations. Default is the interaction of all the grouping variables in the data frame. The special formula idLabels=~.obs will label the observations according to observation number.
grid
an optional logical value indicating whether a grid should be added to plot. Default depends on the type of lattice plot used: if xyplot defaults to TRUE, else defaults to FALSE.
optional arguments passed to the lattice plot function.
Details

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).

Aliases
• plot.merMod
• qqmath.merMod
Examples
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)
require("lattice")
qqmath(fm1, id=0.05)
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")
}

Documentation reproduced from package lme4, version 1.1-13, License: GPL (>= 2)

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