odds.n.ends: A binary logistic regression function

In odds.n.ends: Odds Ratios, Contingency Table, and Model Significance from a Generalized Linear Model Object

Description

This function allows you to compute model significance (model chi-squared), model fit (percent correctly predicted, sensitivity, specificity), ROC plot, predicted probability plot, and odds ratios with 95 percent confidence intervals for a glm object from a binary logistic regression analysis.

Usage

odds.n.ends(
  mod,
  thresh = 0.5,
  rocPlot = FALSE,
  predProbPlot = FALSE,
  color1 = "#7463AC",
  color2 = "deeppink"
)

Arguments

mod	is a glm object
thresh	is the threshold between 0-1 for predicted prob to be considered a case
rocPlot	is TRUE or FALSE to display an ROC plot
predProbPlot	is TRUE or FALSE to display predicted prob histogram by outcome value
color1	choose color for plot
color2	choose 2nd color for plot

Examples

sick <- c(0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1)
age <- c(23, 25, 26, 34, 54, 46, 48, 95, 81, 42, 62, 25, 31, 49, 57, 52, 54, 63, 61, 50)
logisticModel <- glm(sick ~ age, na.action = na.exclude, family = binomial(logit))
odds.n.ends(mod = logisticModel)

odds.n.ends documentation built on Sept. 18, 2021, 1:07 a.m.