Build a matrix of predictions from CV model fits.
buildPredMat(
cvfitlist,
y,
lambda,
family,
foldid,
predict_fun,
predict_params,
predict_row_params = c(),
type.measure = NULL,
weights = NULL,
grouped = NULL
)A list of length `nfolds`, with each element being the model fit for each fold.
Response. It is only used to determine what dimensions the prediction array needs to have.
Lambda values for which we want predictions.
Model family; one of "gaussian", "binomial", "poisson", "cox", "multinomial", "mgaussian", or a class "family" object.
Vector of values identifying which fold each observation is in.
The prediction function; see `kfoldcv()` documentation for details.
Any other parameters that should be passed tp `predict_fun` to get predictions (other than `object` and `newx`); see `kfoldcv()` documentation for details.
A vector which is a subset of `names(predict_params)`, indicating which parameters have to be subsetted in the CV loop (other than `newx`); see `kfoldcv()` documentation for details.
Loss function to use for cross-validation. Only required for `family = "cox"`.
Observation weights. Only required for `family = "cox"`.
Experimental argument; see `kfoldcv()` documentation for details. Only required for `family = "cox"`.
A matrix of predictions.