nlme (version 3.1-1)

qqnorm.gls: Normal Plot of Residuals from a gls Object

Description

Diagnostic plots for assessing the normality of residuals the generalized least squares 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.

Usage

qqnorm(object, form, abline, id, idLabels, grid, ...)

Arguments

object

an object inheriting from class gls, representing a generalized least squares fitted model.

form

an optional one-sided 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 usin

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

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 (random effects). Observations with absolute standardized residuals (random effec

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 vecto

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.

Value

• a diagnostic Trellis plot for assessing normality of residuals.

See Also

gls, plot.gls

Examples

data(Ovary)
fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary,
           correlation = corAR1(form = ~ 1 | Mare))
qqnorm(fm1, abline = c(0,1))