AICcmodavg (version 1.0)

predictSE.lme: Computing Predicted Values and Standard Errors

Description

Function to compute predicted values based on fixed effects (population predictions) and associated standard errors from a linear mixed effect model.

Usage

predictSE.lme(mod, newdata, se.fit = TRUE, level = 0,
print.matrix = FALSE)

Arguments

mod

an object of class 'lme' containing the output of a model.

newdata

a data frame with the same structure as that of the original data frame for which we want to make predictions.

se.fit

logical. If TRUE, compute standard errors on predictions.

level

the level for which predicted values and standard errors are to be computed. The current version of the function only supports predictions for the populations excluding random effects (i.e., level = 0).

print.matrix

logical. If TRUE, the output is returned as a matrix, with predicted values and standard errors in columns. If FALSE, the output is returned as a list.

Value

• 'predictSE.lme' returns requested values either as a matrix ('print.matrix = TRUE') or list ('print.matrix = FALSE') with components:
• fit
  the predicted values.
• se.fit
  the standard errors of the predicted values (if 'se.fit = TRUE').

Details

'predictSE.lme' computes predicted values based on fixed effects and associated standard errors. Standard errors are approximated using the delta method (Oehlert 1992).

References

Gelman, A., Hill, J. (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press: New York. Oehlert, G. W. (1992) A note on the delta method. American Statistician 46, 27--29.

See Also

lme, predict.lme

Examples

##Orthodont data set in Pinheiro and Bates (2000)
require(nlme)
m1 <- lme(distance ~ age, random = ~1 | Subject, data = Orthodont,
method= "ML")
##create a new data frame to make predictions
newOrth <- data.frame(Sex = c("Male","Male","Female","Female"),
                           age = c(15, 20, 15, 20),
                           Subject = c("M01","M01","F30","F30"))
predictSE.lme(m1, newdata = newOrth, level = 0) 
predict(m1, newdata = newOrth, level = 0) #compare against predict.lme( )

##because only 'level = 0' is supported, the grouping levels are not
##necessary for prediction  
newd <- data.frame(Sex = c("Male","Male","Female","Female"),
                           age = c(15, 20, 15, 20))
predictSE.lme(m1, newdata = newd, level = 0)
predictSE.lme(m1, newdata = newd, level = 0, print.matrix = TRUE)

predictSE.lme(m1, newdata = newd, level = 1, print.matrix = TRUE)
##generates an error