nlme (version 3.1-114)

pairs.lme: Pairs Plot of an lme 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. The expression on the right hand side of the formula, before a | operator, must evaluate to a data frame with at least two columns. If the data frame has two columns, a scatter plot of the two variables is displayed (the Trellis function xyplot is used). Otherwise, if more than two columns are present, a scatter plot matrix with pairwise scatter plots of the columns in the data frame is displayed (the Trellis function splom is used).

Usage

## S3 method for class 'lme':
pairs(x, form, label, id, idLabels, grid, \dots)

Arguments

x

an object inheriting from class "lme", representing a fitted linear mixed-effects model.

form

an optional one-sided 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 symb

label

an optional character vector of labels for the variables in the pairs plot.

id

an optional numeric value, or one-sided formula. If given as a value, it is used as a significance level for an outlier test based on the Mahalanobis distances of the estimated random effects. Groups with random effects distances greater than the

idLabels

an optional vector, or one-sided formula. If given as a vector, it is converted to character and used to label the points identified according to id. If given as a one-sided formula, its right hand side must evaluate to a vector w

grid

an optional logical value indicating whether a grid should be added to plot. Default is FALSE.

...

optional arguments passed to the Trellis plot function.

Value

• a diagnostic Trellis plot.

See Also

lme, pairs.compareFits, pairs.lmList, xyplot, splom

Examples

fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
# scatter plot of coefficients by gender, identifying unusual subjects
pairs(fm1, ~coef(., augFrame = TRUE) | Sex, id = 0.1, adj = -0.5)    
# scatter plot of estimated random effects
pairs(fm1, ~ranef(.))