nlme (version 3.1-114)

residuals.lme: Extract lme Residuals

Description

The residuals at level $i$ are obtained by subtracting the fitted levels at that level from the response vector (and dividing by the estimated within-group standard error, if type="pearson"). The fitted values at level $i$ are obtained by adding together the population fitted values (based only on the fixed effects estimates) and the estimated contributions of the random effects to the fitted values at grouping levels less or equal to $i$.

Usage

## S3 method for class 'lme':
residuals(object, level = Q,
          type = c("response", "pearson", "normalized"), asList = FALSE, ...)

Arguments

object

an object inheriting from class "lme", representing a fitted linear mixed-effects model.

level

an optional integer vector giving the level(s) of grouping to be used in extracting the residuals from object. Level values increase from outermost to innermost grouping, with level zero corresponding to the population residuals.

type

an optional character string specifying the type of residuals to be used. If "response", as by default, the raw residuals (observed - fitted) are used; else, if "pearson", the standardized residu

asList

an optional logical value. If TRUE and a single value is given in level, the returned object is a list with the residuals split by groups; else the returned value is either a vector or a data frame, according to the l

...

some methods for this generic require additional arguments. None are used in this method.

Value

• if a single level of grouping is specified in level, the returned value is either a list with the residuals split by groups (asList = TRUE) or a vector with the residuals (asList = FALSE); else, when multiple grouping levels are specified in level, the returned object is a data frame with columns given by the residuals at different levels and the grouping factors. For a vector or data frame result the naresid method is applied.

See Also

lme, fitted.lme

Examples

fm1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1)
head(residuals(fm1, level = 0:1))
summary(residuals(fm1) /
        residuals(fm1, type = "p")) # constant scaling factor 1.432