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, ...)
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 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. |
Value
a diagnostic Trellis plot.
Author(s)
José Pinheiro and Douglas Bates [email protected]
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(.))
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Licensed under the GNU General Public License.