fitted.lme {nlme}

Extract lme Fitted Values

Description

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. The resulting values estimate the best linear unbiased predictions (BLUPs) at level i.

Usage

## S3 method for class 'lme'
fitted(object, level, 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 fitted values from object. Level values increase from outermost to innermost grouping, with level zero corresponding to the population fitted values. Defaults to the highest or innermost level of grouping.

asList

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

...

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 fitted values split by groups (asList = TRUE) or a vector with the fitted values (asList = FALSE); else, when multiple grouping levels are specified in level, the returned object is a data frame with columns given by the fitted values at different levels and the grouping factors. For a vector or data frame result the napredict method is applied.

Author(s)

José Pinheiro and Douglas Bates [e-mail]

References

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

Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S and S-PLUS", Springer, esp. pp. 235, 397.

See Also

lme, residuals.lme

Examples

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