single_cvglm.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/single_cvglm.R
\name{single_cvglm}
\alias{single_cvglm}
\title{Use glmnet}
\usage{
single_cvglm(
  model_matrix,
  response,
  fold_vector = NULL,
  alpha = 0.5,
  outer_folds = 10,
  type_measure = "class",
  inner_folds = 5,
  family = NULL,
  standardize = F,
  seed = NULL,
  stratify = F,
  ...
)
}
\arguments{
\item{seed}{Integer - Number for set.seed}

\item{...}{Parameters for glmnet}

\item{data}{A data frame or SummarizedExperiment.}

\item{classification}{Either a matrix containing response vector or an integer corresponding to the index of the assay in the SE containing the matrix.}

\item{gene_names}{Character - vector of gene names to apply glmnet to.}

\item{use_cv}{Logical - Should nested cross validation be used to estimate error?}

\item{col_names}{Character - Set of column names to be used as predictors in regression}

\item{verbose}{Logical - Show Progress of function, not in parallel}

\item{return_plot}{Logical - If set to true, the output is a list of the Regression  results and a correlation plot with AUC plot.}

\item{exclude}{character vector - factors/columns to be excluded. You dont want to include factors which are more like observatory variables as opposed to possible explanatory variables in the analysis.}
}
\description{
This function uses glmnet to do variable selection within a classification of a response variable.
}
\details{
Best use for summarized experiments
}