glmPredictPhotoZ: Predict photometric redshifts using a given glm fit object
In CosmoPhotoz: Photometric redshift estimation using generalized linear models

Description Usage Arguments Value Author(s) Examples

glmPredictPhotoZ computes a list of simple summary statistics for the photometric redshift estimation.

1	glmPredictPhotoZ(data, train)

`data`	a data.frame containing the data one wished to compute the redshift
`train`	a trained glm object containing the fit of the model

list containing the results of the redshift estimation

Rafael S. de Souza, Alberto Krone-Martins

## Not run: 
# Load the data
data(PHAT0train)
data(PHAT0test)

# Combine the training and test data and calculate the principal components
PC_comb <- computeCombPCA(subset(PHAT0train, select=c(-redshift)),
           subset(PHAT0test, select=c(-redshift)),
           robust=FALSE) # robust is false here just to make it faster
Trainpc <- cbind(PC_comb$x, redshift=PHAT0train$redshift)
Testpc <- PC_comb$y

# Fitting
Fit <- glmTrainPhotoZ(Trainpc, formula=redshift~poly(Comp.1,2)*
           poly(Comp.2,2)*Comp.3*Comp.4*Comp.5*Comp.6,
           method="Bayesian", family="gamma")

# Perform the photo-z estimation using the glmPredictPhotoZ function
photoz <- glmPredictPhotoZ(data=Testpc, train=Fit$glmfit)
specz <- PHAT0test$redshift

# Show a plot with the results
plotDiagPhotoZ(photoz$photoz, specz, "box")

## End(Not run)