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).
an object inheriting from class
lme, representing
a fitted linear mixed-effects model.
OPTIONAL ARGUMENTS:
form
an optional formula specifying the desired type of
plot. Any variable present in the original data frame used to obtain
object can be referenced. In addition,
object 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 residuals. Observations with
absolute standardized 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.
subset
an optional expression indicating the subset of the
observations that should be used in the plot. This can be a logical
vector, or a numeric vector indicating which observation numbers
are to be included, or a character vector of the row names to be
included. All observations are included by default.
...
optional arguments passed to the Trellis plot function.
VALUE:
a diagnostic Trellis plot.
SEE ALSO:
,
,
,
EXAMPLES:
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))