PcrossVD: PcrossVD
In lucapresicce/DescendMethods: Descend Methods for Linear Models
View source: R/CrossValidation.R
PcrossVD R Documentation
PcrossVD
Description
This function is the parallel version of CrossVD.
Usage
PcrossVD(data, K = NULL, get_mean = T, n_clust = NULL, OPT, ...)
Arguments
data
list containing the data, elements must be named X and Y, where X is a
n x k matrix and Y is a vector of length n. Here, n represents the number of
observations and k is the number of \mjseqn\beta coefficients.
n_clust
integer the number of clusters to use. If NULL, the function selects all available cores but one.
Value
if get_mean is TRUE, the CV-MSE is returned.
Otherwise the function returns a vector containing all MSE computed for each fold.
lucapresicce/DescendMethods documentation built on April 26, 2022, 6 p.m.
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View source: R/CrossValidation.R
| PcrossVD | R Documentation |
PcrossVD
Description
This function is the parallel version of CrossVD.
Usage
PcrossVD(data, K = NULL, get_mean = T, n_clust = NULL, OPT, ...)
Arguments
data |
list containing the data, elements must be named |
n_clust |
integer the number of clusters to use. If |
Value
if get_mean is TRUE, the CV-MSE is returned.
Otherwise the function returns a vector containing all MSE computed for each fold.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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