plot.lme {nlme} | R Documentation

## Plot an lme or nls object

### Description

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 Trellis 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 Trellis 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 Trellis 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 Trellis function `bwplot` is used).

### Usage

    ## S3 method for class 'lme':
    plot(x, form, abline, id, idLabels, idResType, grid, ...)
    ## S3 method for class 'nls':
    plot(x, form, abline, id, idLabels, idResType, grid, ...)

### Arguments

`x` | an object inheriting from class `lme`, representing a fitted linear mixed-effects model, or from `nls`, representing an fitted nonlinear least squares 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 Trellis display. Default is `resid(., type = "p") ~ fitted(.)` , corresponding to a plot of the standardized residuals versus fitted values, both evaluated at the innermost level of nesting.
`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 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 innermost grouping factor.
`idResType` | an optional character string specifying the type of residuals to be used in identifying outliers, when `id` is a numeric value. If `"pearson"`, the standardized residuals (raw residuals divided by the corresponding standard errors) are used; else, if `"normalized"`, the normalized residuals (standardized residuals pre-multiplied by the inverse square-root factor of the estimated error correlation matrix) are used. Partial matching of arguments is used, so only the first character needs to be provided. Defaults to `"pearson"`.
`grid` | an optional logical value indicating whether a grid should be added to plot. Default depends on the type of Trellis plot used: if `xyplot` defaults to `TRUE`, else defaults to `FALSE`.
`...` | optional arguments passed to the Trellis plot function.

### Value

a diagnostic Trellis plot.

### Author(s)

Jose Pinheiro Jose.Pinheiro@pharma.novartis.com and Douglas Bates bates@stat.wisc.edu

### See Also

`lme`, `xyplot`, `bwplot`, `histogram`

### Examples

    data(Orthodont)
    fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
    # standardized residuals versus fitted values by gender
    plot(fm1, resid(., type = "p") ~ fitted(.) | Sex, abline = 0)
    # box-plots of residuals by Subject
    plot(fm1, Subject ~ resid(.))
    # observed versus fitted values by Subject
    plot(fm1, distance ~ fitted(.) | Subject, abline = c(0,1))

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