Extract lme Residuals

Usage

residuals(object, level, type, asList)

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. Defaults to the highest or innermost level of grouping.
type an optional character string specifying the type of residuals to be used. If "response", the "raw" residuals (observed - fitted) are used; else, 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".
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 length of level. Defaults to FALSE.

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.

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.

Author(s)

Jose Pinheiro and Douglas Bates

References

Bates, D.M. and Pinheiro, J.C. (1998) "Computational methods for multilevel models" available in PostScript or PDF formats at http://franz.stat.wisc.edu/pub/NLME/

See Also

lme, fitted.lme

Examples

library(lme)
data(Orthodont)
fm1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1)
residuals(fm1, level = 0:1)

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