nlme (version 3.1-157)

predict.nlme: Predictions from an nlme Object

Description

The predictions at level $i$ are obtained by adding together the contributions from the estimated fixed effects and the estimated random effects at levels less or equal to $i$ and evaluating the model function at the resulting estimated parameters. If group values not included in the original grouping factors are present in newdata, the corresponding predictions will be set to NA for levels greater or equal to the level at which the unknown groups occur.

Usage

# S3 method for nlme
predict(object, newdata, level = Q, asList = FALSE,
        na.action = na.fail, naPattern = NULL, …)

Arguments

object

an object inheriting from class "nlme", representing a fitted nonlinear mixed-effects model.

newdata

an optional data frame to be used for obtaining the predictions. All variables used in the nonlinear model, the fixed and the random effects models, as well as the grouping factors, must be present in the data frame. If missing, the fitted values are returned.

level

an optional integer vector giving the level(s) of grouping to be used in obtaining the predictions. Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions. Defaults to the highest or innermost level of grouping (and is object$dims$Q).

asList

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

na.action

a function that indicates what should happen when newdata contains NAs. The default action (na.fail) causes the function to print an error message and terminate if there are any incomplete observations.

naPattern

an expression or formula object, specifying which returned values are to be regarded as missing.

…

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

See Also

nlme, fitted.lme

Examples

# NOT RUN {
head(Loblolly) # groupedData  w/  'Seed' is grouping variable :
## Grouped Data: height ~ age | Seed
##    height age Seed
## 1    4.51   3  301
## 15  10.89   5  301
## ..  .....   .  ...

fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc),  data = Loblolly,
            fixed = Asym + R0 + lrc ~ 1,
            random = Asym ~ 1, ## <---grouping--->  Asym ~ 1 | Seed
            start = c(Asym = 103, R0 = -8.5, lrc = -3.3))
fm1

age. <- seq(from = 2, to = 30, by = 2)
newLL.301 <- data.frame(age = age., Seed = 301)
newLL.329 <- data.frame(age = age., Seed = 329)
(p301 <- predict(fm1, newLL.301, level = 0:1))
(p329 <- predict(fm1, newLL.329, level = 0:1))
## Prediction are the same at level 0 :
all.equal(p301[,"predict.fixed"],
          p329[,"predict.fixed"])
## and differ by the 'Seed' effect at level 1 :
p301[,"predict.Seed"] -
p329[,"predict.Seed"]
# }