multLogistic: multLogistic

In TaylorAndrew/atAnalyze: Methods for Iterative Analytical Reporting

Description

multLogistic performs univariate, multivariate, and/or conditional logistic regression models over a list of predictor variables.

Usage

multLogistic(data, y, predlist, catlist = NULL, controlCatList = NULL,
  controlContinList = NULL, conditionalLogistic = FALSE, matchIDs = NULL,
  varLabelTable = NULL, minObsForML = 20, dec = 2, pdec = 3,
  rowwisePercent = T, includeCI = F, verbose = TRUE)

Arguments

data	data.frame containing all data for analysis
y	Outcome variable name
predlist	Vector of predictor variable names
catlist	Vector of categorical variable names
controlCatList	Vector of categorical variable names to control for in each model
controlContinList	Vetor of continuous variable names to control for in each model
conditionalLogistic	If TRUE, perform conditional logisitic regression
matchIDs	If conditionalLogistic==TRUE, variable name that contains matchIDs
varLabelTable	varLabelTable object containing variable names in column 1 and variable labels in column 2
minObsForML	The minimum number of events (y==1) observed in the dataset before the method should be switched to Firth's Penalized Maximum Likelihood. Default is 20, which is commonly used as a good rule of thumb.
dec	Number of decimals to round descriptive statistics and odds ratios to
pdec	Number of decimals to round p-values to
rowwisePercent	If TRUE, present row-wise percents for descriptive statistics
includeCI	If TRUE, confidence intervals for the means/proportions are included in the output.
verbose	If TRUE, print additional information to the console

Value

An R data.frame object contianing all results.

Examples

#NULL

TaylorAndrew/atAnalyze documentation built on May 9, 2019, 4:21 p.m.