glm_assump: Examining assumptions of a Logistic Regression

In jpedroza1228/reg.diagnostics: Regression Diagnostics

Examining assumptions of a Logistic Regression

Description

This function is to examine linearity and outliers of a logistic regression.

Usage

glm_assump(
  data,
  x,
  y,
  alpha,
  se = c(TRUE, FALSE),
  size,
  line_size,
  color,
  loess_color,
  line_color
)

Arguments

data	The data frame that includes the variables you are interested in examining in a logistic regression.
x	Predictors to be included in your linear regression. You can either include one variable in quotations (e.g., "hp" from the mtcars dataset) or you can create an object of predictors (e.g., predictors <- c("disp", "cyl", "wt")).
y	Your outcome of interest.
alpha	Value to determine how transparent you'd like your points in this function's plots.
se	A logical vector to decide if you'd like to include the standard error for your plots.
size	Value to determine the size of the points in this function's plots. You can also determine if you'd like to assign these values to a categorical variable in your dataset.
line_size	Value to decide if you'd like your lines to be thinner or thicker in your plots
color	Value to determine what color you'd like your points to be in the scatterplot (e.g., "blue", "#6a1f25")
loess_color	value to determine what color you'd like your loess line to be in the scatterplot (e.g., "blue", "#6a1f25")
line_color	value to determine what color you'd like your linear relationship to be in the scatterplot (e.g., "blue", "#6a1f25")

Value

Returns two ggplot2 visuals. One for the assumption of linearity predicting logit of the outcome and the second to check for outliers in the residuals.

Examples

glm_assump(data = mtcars, x = "hp", y = vs, alpha = .5, se = FALSE, size = 2, line_size = 1.25, color = "dodgerblue", loess_color = "red", line_color = "blue")

predictors <- c("hp", "carb", "gear")
glm_assump(data = mtcars, x = predictors, y = vs, alpha = .5, se = FALSE, size = 2, line_size = 1.25, color = "dodgerblue", loess_color = "red", line_color = "blue")

jpedroza1228/reg.diagnostics documentation built on June 15, 2022, 9:30 p.m.