glmTrainPhotoZ: Fit a glm for photometric redshift estimation
In CosmoPhotoz: Photometric redshift estimation using generalized linear models

Description Usage Arguments Details Value Author(s) Examples

glmTrainPhotoZ trains a generalized linear model for photometric redshift estimation.

1	glmTrainPhotoZ(x, formula, method, family)

`x`	a data.frame containing the data to train the model
`formula`	an object of class "formula" to be adopted
`method`	a string containing the chosen glm method. Two options are available: `Frequentist` will use the function `glm` from the package `stats`; `Bayesian` will use the function `bayesglm` from the package `arm`.
`family`	a string containing `gamma` or `inverse.gaussian` (a string with a description of the error distribution and link function to be used in the model).

The program is a simple alteration of glm() and bayesglm(). The new arguments here are: Frequentist, Bayesian.

a trained glm object containing the fit of the model

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 photometric redshift estimation
photoz <- predict(Fit$glmfit, newdata=Testpc, type="response")
specz <- PHAT0test$redshift

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

## End(Not run)