| pairs.lmList {nlme} | R Documentation | 
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, ...)
Arguments
| x | an object inheriting from class  | 
| form | an optional one-sided formula specifying the desired type of
plot. Any variable present in the original data frame used to obtain
 | 
| 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  | 
| grid | an optional logical value indicating whether a grid should
be added to plot. Default is  | 
| ... | optional arguments passed to the Trellis plot function. | 
Value
a diagnostic Trellis plot.
Author(s)
José Pinheiro and Douglas Bates bates@stat.wisc.edu
See Also
lmList,
pairs.lme,
pairs.compareFits,
xyplot,
splom
Examples
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 -- "bivariate Gaussian (?)"
pairs(fm1, ~ranef(.))