nlme (version 3.1-68.1)

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

## S3 method for class 'gls':
qqnorm(y, form, abline, id, idLabels, grid, \dots)

Arguments

y

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 y can be referenced. In addition, y itself can be referenced in the formula using the symb

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

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