| predict.nlme {nlme} | R Documentation |
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 class 'nlme'
predict(object, newdata, level = Q, asList = FALSE,
na.action = na.fail, naPattern = NULL, ...)
Arguments
object |
an object inheriting from class |
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 |
asList |
an optional logical value. If |
na.action |
a function that indicates what should happen when
|
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.
Author(s)
José Pinheiro and Douglas Bates bates@stat.wisc.edu
See Also
Examples
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"]