buildPredMat: Build a prediction matrix from CV model fits
In cvwrapr: Tools for Cross Validation

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
)

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