mapGLMTables: mapGLMTables: Loop over regression models and extract...

In nkappelmann/PubHelper: Facilitating Result-Processing for Scientific Publication

Description

mapGLMTables loops over regression models (linear, logistic, & ordinal logistic) and returns summarised output from getGLMTable. It is possible to include covariates, which are automatically excluded from the output. However, the full output can be obtained as a more complex output by setting simplify=FALSE. Note that a complex model output is returned if predictor variables include factors with >2 levels.

Usage

mapGLMTables(
  data = NULL,
  y = NULL,
  x = NULL,
  z = NULL,
  model.type = "lm",
  format.output = FALSE,
  simplify = TRUE
)

Arguments

data	Data.frame including model variables
y	Vector of outcome variables
x	Vector of predictor variables
z	Vector of covariates if covariates are included
model.type	Specify which statistical model to run. Options are "lm" for linear regression, "glm" for logistic regression, and "polr" for ordinal logistic regression
format.output	Should output be formatted for scientific publications? Default is FALSE.
simplify	Should results be simplified to relevant regression parameters of the predictor? If not, full lm models, GLMTables, and plots to check model assumptions (using the performance package) are included in the output. Default is simplify=TRUE.

Author(s)

Nils Kappelmann

Examples

data(airquality)
mapGLMTables(data = airquality, y = "Ozone", x = c("Solar.R", "Wind"), z = "Temp")

nkappelmann/PubHelper documentation built on Aug. 7, 2021, 5:09 a.m.