stepANM {anm}R Documentation

Choose a model by the analog method in a stepwise algorithm

Description

Performs the analog method step by step to select a model and plots on the same graph both correlation and rmse at each step.

Usage

stepANM(anm.obj,trace=1,steps=8)

Arguments

anm.obj the anm object inheriting from anm routine.
trace if equal to 1, information is printed during the running of the stepwise algorithm.
steps maximum number of steps, forced to the number of predictor variables if steps exceeds it.

Value

A list with components

Call the matched call.
PC the predictor variables selected.
anm.obj the anm object selected.
coefficients the coeffecients of the anm object.
step.min the number of steps which returns the minimum rmse.
model the model corresponding to the minimum rmse.
Rmse the minimum root mean square error.
correlation the correlation between predictions and observations for the selected model.

Note

The running of the stepwise algorithm can be quite slow especially if the number of steps specified in the steps argument is high.

Author(s)

Alexandra Imbert

See Also

anm, predictAnm

Examples

library(survival)
library(clim.pact)
data(susendal)
data(temp.era)
y<-susendal$V6 # temperatures
X<- eof$PC[,c(1,2,3)]
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$yy> 1979,2],
                       X3=X[eof$yy< 1993 & eof$yy> 1979,3],
                       yy=eof$yy[eof.calib | eof.eval],
                       mm=eof$mm[eof.calib | eof.eval],
                       dd=eof$dd[eof.calib | eof.eval])
test.anm<-anm(formula=y ~ X1 + X2 + X3,data=test.data)
stepANM(test.anm,steps=3)

[Package anm version 1.0-5 Index]