plot.merMod {lme4} | R Documentation

## diagnostic plots for merMod fits

### Description

diagnostic plots for merMod fits

### Usage

      ## 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, ...)

### 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 `link{qqmath.ranef.mer}` for Q-Q plots of the conditional mode values).

### Author(s)

original version in `nlme` package by Jose Pinheiro and Douglas Bates

### 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")
    }

[Package lme4 version 1.1-12 Index]