CosmoPhotoZestimator: Photometric redshift estimation from a training dataset and a...
In CosmoPhotoz: Photometric redshift estimation using generalized linear models

Description Usage Arguments Value Author(s) Examples

CosmoPhotoZestimator returns photometric redshift estimated from photometric data and a training dataset with photometry and spectroscopy. The estimation is based on generalized linear models (see glmTrainPhotoZ and glmPredictPhotoZ).

1	CosmoPhotoZestimator(trainData, testData, numberOfPcs, method, family, robust)

`trainData`	vector containing spectroscopic redshift data and photometry (at least one column shall be called redshift)
`testData`	vector containing spectroscopic redshift data and photometry (at least one column shall be called redshift)
`numberOfPcs`	an integer indicating the number of principal components to consider
`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 description of the error distribution and link function to be used in the model)
`robust`	a boolean indicating if robust PCA should be used or not

a vector with the estimated photometric redshifts

Alberto Krone-Martins, Rafael S. de Souza

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

# Run the analysis
photoZest <- CosmoPhotoZestimator(PHAT0train, PHAT0test, 6)

# Create a boxplot showing the results
plotDiagPhotoZ(photoz = photoZest, specz = PHAT0test$redshift, type = "box")

## End(Not run)