predictAnm {anm}R Documentation

Predict method for anm objects.

Description

Returns the predicted values based on the anm object.

Usage

predictAnm(object,newdata=NULL,se.fit=FALSE, ...)

Arguments

object the anm object inheriting from anm routine.
newdata an optional independant data. If specified, only the vector of predictions is returned.
se.fit if false, only the vector of predictions is returned.
... further arguments passed to or from other methods.

Value

A list with components

problem.dimension the number of predictor variables.
period.length the time period.
d.min the vector of minimum distances.
date.min the vector containing the dates corresponding to the minimum distances.
analog the vector of predictions.
maxi.anlg monthly maxima values of predictions.
mini.anlg monthly minima values of predictions.
error vector of errors between predictions and observations at each date.
correlation correlation coefficient between predictions and observations.
rmse root mean square errors between predictions and observations.

Author(s)

R.E. Benestad and Alexandra Imbert

References

URL http://www.R-project.org/

See Also

anm, stepANM

Examples

library(survival)
library(clim.pact)
data(temp.era)
data(susendal)
y<-susendal$V6 # temperatures
X<- eof$PC[,c(1,2)]
calibration <- c(susendal$V4>1979 & susendal$V4<1990 & (susendal$V3==1 | susendal$V3==2 | susendal$V3==12))
evaluation <- c((susendal$V4>1990 & susendal$V4<1993 | susendal$V4==1990) & (susendal$V3==1 | susendal$V3==2 | susendal$V3==12))
y.calib <- y[calibration]
y.eval <- y[evaluation]
eof.calib <- c(eof$yy>1979 & eof$yy<1990)
eof.eval <- c(eof$yy> 1990 & eof$yy<1993| eof$yy==1990)
period <- c(calibration, evaluation)
y.period <- y[(susendal$V4>1979 & susendal$V4<1993) & (susendal$V3==1 | susendal$V3==2 | susendal$V3==12)]
test.data <-data.frame(y=y.period,
                       X1=X[eof$yy< 1993 & eof$yy> 1979,1],
                       X2=X[eof$yy< 1993 & eof$yy> 1979,2],
                       yy=eof$yy[eof.calib | eof.eval],
                       mm=eof$mm[eof.calib | eof.eval],
                       dd=eof$dd[eof.calib | eof.eval])
test.anm<-anm(y ~ X1 + X2,data=test.data)
res <- predictAnm(test.anm)

[Package anm version 1.0-5 Index]