predict.gppr: Generalised projection pursuit regression

In gsg: Calculation of selection coefficients

Description

Obtains linear predictor- or data-scale predictions for a fitted gppr object

Usage

predict.gppr(object,newdata,type="link")

Arguments

object	a fitted gppr object
newdata	an optional data frame containing new predictor variable values. If 'newdata' is omitted, then the predictions are made for the original data from which the gppr model was fitted.
type	the type of prediction required. The default is the scale of the linear predictor, alternativels 'type="response"' gives expected values.

Value

A vector of predictions.

Author(s)

Michael Morrissey [e-mail]

References

M.B. Morrissey. in prep.

See Also

gppr, ppr,gppr.gradients

Examples

# simulated data (two traits, stabilizing selection on trait 1)
n<-250
z<-cbind(rnorm(n,0,1),rnorm(n,0,1))
W<-rpois(n,exp(2-0.6*z[,1]^2))
d<-as.data.frame(cbind(W,z))
names(d)<-c("W","z1","z2")

fit.func<-gppr(y="W",xterms=c("z1","z2"),data=d,family="poisson",
   nterms=2,max.terms=2)

# hist(predict(fit.func))

gsg documentation built on May 2, 2019, 9:37 a.m.