pairs.lmList: Pairs Plot of an lmList Object

Description

Diagnostic plots for the linear model fits corresponding to the x components 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 'lmList':
pairs(x, form, label, id, idLabels, grid, \dots)

Arguments

an object inheriting from class lmList, representing a list of lm objects with a common 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.

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.

Examples

Run this code

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

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