anm {anm} | R Documentation |
anm is used to compute the analog method.
anm(formula,data,weights=NULL,cross.valid=NULL)
formula |
a symbolic description of the model to be fit. |
data |
the data.frame containing the variables in the model. |
weights |
an optional matrix of weights to be used in the fitting process. |
cross.valid |
an optional matrix of booleans. If not specified, a cross validation is used in the fitting process. |
Models for anm are specified symbolically. A typical model has the form predictand ~ terms where terms is a series of predictors whose specification can be of the for first + second. anm calls the lower level function anmFit.
An object of class "anm". An object of class "anm" is a list containing the following components:
coefficients |
a vector containing the values for the principal components corresponding to the maximum among observations. |
contrasts |
(not used). |
call |
the matched call. |
terms |
the terms object used. |
model |
the model frame used. |
x |
the matrix used for predictors. |
y |
the predictand. |
weights |
the matrix of weights. |
cross.valid |
equals to True if the cross.validation will be used for the fitting process. |
data |
the input data.frame. |
Alexandra Imbert
link{predictAnm}, link{stepANM}
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]) anm(y ~ X1 + X2,data=test.data)