computeRawError {cvwrapr} | R Documentation |
Compute the nobs by nlambda matrix of errors
Description
Computes the nobs by nlambda matrix of errors corresponding to the error measure provided. Only works for "gaussian" and "poisson" families right now.
Usage
computeRawError(predmat, y, type.measure, family, weights, foldid, grouped)
Arguments
predmat |
Array of predictions. If 'y' is univariate, this has dimensions 'c(nobs, nlambda)'. If 'y' is multivariate with 'nc' levels/columns (e.g. for 'family = "multionmial"' or 'family = "mgaussian"'), this has dimensions 'c(nobs, nc, nlambda)'. Note that these should be on the same scale as 'y' (unlike in the glmnet package where it is the linear predictor). |
y |
Response variable. |
type.measure |
Loss function to use for cross-validation. See 'availableTypeMeasures()' for possible values for 'type.measure'. Note that the package does not check if the user-specified measure is appropriate for the family. |
family |
Model family; used to determine the correct loss function. |
weights |
Observation weights. |
foldid |
Vector of values identifying which fold each observation is in. |
grouped |
Experimental argument; see 'kfoldcv()' documentation for details. |
Value
A list with the following elements:
cvraw |
An nobs by nlambda matrix of raw error values. |
weights |
Observation weights. |
N |
A vector of length nlambda representing the number of non-NA predictions associated with each lambda value. |
type.measure |
Loss function used for CV. |