Package 'odds.n.ends'

Title: Odds Ratios, Contingency Table, and Model Significance from a Generalized Linear Model Object
Description: Computes odds ratios and 95% confidence intervals from a generalized linear model object. It also computes model significance with the chi-squared statistic and p-value and it computes model fit using a contingency table to determine the percent of observations for which the model correctly predicts the value of the outcome. Calculates model sensitivity and specificity.
Authors: Jenine Harris [aut, cre]
Maintainer: Jenine Harris <[email protected]>
License: CC0
Version: 0.1.4
Built: 2026-05-31 09:08:01 UTC
Source: https://github.com/jenineharris/odds.n.ends

Help Index

A binary logistic regression function

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)