Predictions from a biglm/bigglm
Computes fitted means and standard errors at new data values after
fitting a model with biglm or bigglm.
## S3 method for class 'bigglm'
predict(object, newdata, type = c("link", "response"),
se.fit = FALSE, make.function = FALSE, ...)
## S3 method for class 'biglm'
predict(object, newdata=NULL, se.fit = FALSE, make.function = FALSE, ...)object |
fitted model |
newdata |
data frame with variables for new values |
type |
|
se.fit |
Compute standard errors? |
make.function |
If |
... |
not used |
When make.function is TRUE, the return value is either a
single function that computes the fitted values or a list of two
functions that compute the fitted values and standard errors. The
input to these functions is the design matrix, without the intercept
column. This allows the relatively time-consuming calls to
model.frame() and model.matrix() to be avoided.
Either a vector of predicted values or a data frame with predicted values and standard errors.
based on code by Christophe Dutang
~put references to the literature/web site here ~
example(biglm) predict(a,newdata=trees) f<-predict(a,make.function=TRUE) X<- with(trees, cbind(log(Girth),log(Height))) f(X)
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