DTD_cv_lambda_test_input_generic: input tests for cross validation

In MarianSchoen/DTD: Digital Tissue Deconvolution

input tests for cross validation

Description

tests common input parameters to the cxx and R implementation of DTD_cv_lambda.

Usage

DTD_cv_lambda_test_input_generic(
  lambda.seq,
  tweak.start,
  n.folds,
  lambda.length,
  train.data.list,
  cv.verbose,
  warm.start
)

Arguments

lambda.seq	numeric vector or NULL or "none": Over this series of lambdas the FISTA will be optimized. If 'lambda.seq' is set to NULL, a generic series of lambdas - depending on the dimensions of the training set - will be generated. If 'lambda.seq' is "none", no cross validation is done. Only one model with lambda = 0 is trained on the complete data set.
tweak.start	numeric vector, starting vector for the DTD algorithm.
n.folds	integer, number of buckets in the cross validation.
lambda.length	integer, how many lambdas will be generated (only used if lambda.seq is NULL)
train.data.list	list, with two entries, a numeric matrix each, named 'mixtures' and 'quantities' Within this list the train/test cross validation will be done. (see Vignette 'browseVignettes("DTD")' for details)
cv.verbose	logical, should information about the cv process be printed to the screen?
warm.start	logical, should the solution of a previous model of the cross validation be used as start in the next model. Notice, that the warm.start starts with the most unpenalized model. @return NULL, or it throws an error

MarianSchoen/DTD documentation built on April 29, 2022, 1:59 p.m.