| plot.lme {nlme} | R Documentation |
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. If form is a one-sided formula, histograms of the
variable on the right hand side of the formula, before a |
operator, are displayed (the Trellis function histogram is
used). If form is two-sided and both its left and
right hand side variables are numeric, scatter plots are displayed
(the Trellis function xyplot is used). Finally, if form
is two-sided and its left had side variable is a factor, box-plots of
the right hand side variable by the levels of the left hand side
variable are displayed (the Trellis function bwplot is used).
## S3 method for class 'lme': plot(x, form, abline, id, idLabels, idResType, grid, ...) ## S3 method for class 'nls': plot(x, form, abline, id, idLabels, idResType, grid, ...)
x |
an object inheriting from class lme, representing
a fitted linear mixed-effects model, or from nls, representing
an fitted nonlinear least squares model. |
form |
an optional 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 symbol
".". Conditional expressions on the right of a |
operator can be used to define separate panels in a Trellis
display. Default is resid(., type = "p") ~ fitted(.) ,
corresponding to a plot of the standardized residuals versus fitted
values, both evaluated at the innermost level of nesting. |
abline |
an optional numeric value, or numeric vector of length two. If given as a single value, a horizontal line will be added to the plot at that coordinate; else, if given as a vector, its values are used as the intercept and slope for a line added to the plot. If missing, no lines are added to the plot. |
id |
an optional numeric value, or one-sided formula. If given as
a value, it is used as a significance level for a two-sided outlier
test for the standardized, or normalized residuals. Observations with
absolute standardized (normalized) residuals greater than the 1
- value/2 quantile of the standard normal distribution are
identified in the plot using idLabels. If given as a one-sided
formula, its right hand side must evaluate to a logical, integer, or
character vector which is used to identify observations in the
plot. If missing, no observations are identified. |
idLabels |
an optional vector, or one-sided formula. If given as a
vector, it is converted to character and used to label the
observations identified according to id. If given as a
one-sided formula, its right hand side must evaluate to a vector
which is converted to character and used to label the identified
observations. Default is the innermost grouping factor. |
idResType |
an optional character string specifying the type of
residuals to be used in identifying outliers, when id is a
numeric value. If "pearson", the standardized residuals (raw
residuals divided by the corresponding standard errors) are used;
else, if "normalized", the normalized residuals (standardized
residuals pre-multiplied by the inverse square-root factor of the
estimated error correlation matrix) are used. Partial matching of
arguments is used, so only the first character needs to be
provided. Defaults to "pearson". |
grid |
an optional logical value indicating whether a grid should
be added to plot. Default depends on the type of Trellis plot used:
if xyplot defaults to TRUE, else defaults to
FALSE. |
... |
optional arguments passed to the Trellis plot function. |
a diagnostic Trellis plot.
Jose Pinheiro Jose.Pinheiro@pharma.novartis.com and Douglas Bates bates@stat.wisc.edu
lme, xyplot,
bwplot, histogram
fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject) # standardized residuals versus fitted values by gender plot(fm1, resid(., type = "p") ~ fitted(.) | Sex, abline = 0) # box-plots of residuals by Subject plot(fm1, Subject ~ resid(.)) # observed versus fitted values by Subject plot(fm1, distance ~ fitted(.) | Subject, abline = c(0,1))