pairs.lmList {nlme} | R Documentation |
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).
## S3 method for class 'lmList' pairs(x, form, label, id, idLabels, grid, ...)
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
1-value percentile of the appropriate chi-square distribution
are identified in the plot using |
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. |
a diagnostic Trellis plot.
José Pinheiro and Douglas Bates bates@stat.wisc.edu
lmList
,
pairs.lme
,
pairs.compareFits
,
xyplot
,
splom
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 ## Not run: pairs(fm1, ~ranef(.)) ## End(Not run)