logit: Logistic Regression Model

In mStats: Epidemiological Data Analysis

Description

logit() produces summary of the model with coefficients or odds ratios (OR) and 95% Confident Intervals.

Usage

logit(model, or = TRUE, digits = 5)

Arguments

model	glm or lm model
or	TRUEreports odds ratios instead of coefficients
digits	specify rounding of numbers. See round.

Details

logit() is based on glm with binomial family. All statistics presented in the function's output are derivatives of glm, except AIC value which is obtained from AIC.

Outputs

Outputs can be divided into three parts.

1. Info of the model: Here provides number of observations (Obs.), chi value from Likelihood Ratio test (LR chi2) and its degree of freedom, p-value from LR test, Pseudo R Squared, log likelihood and AIC values.

2. Regression Output: Coefficients from summary of model are tabulated here along with 95\ confidence interval.

Value

a list containing

1. info - info and error tables

2. reg - regression table

3. model - raw model output from lm()

4. fit - formula for fitting the model

5. lbl - variable labels for further processing in summary.

Author(s)

Email: [e-mail]

Website: https://myominnoo.github.io/

Examples

mylogit <- glm(case ~ education + age + parity, family = binomial,
           data = infert)
logit(mylogit)

## Not run: 
## Example from UCLA website:
## LOGIT REGRESSION | R DATA ANALYSIS EXAMPLES
## https://stats.idre.ucla.edu/r/dae/logit-regression/

mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
mydata <- replace(mydata, rank, factor(rank))
mydata <- label(mydata, gre = "GRE", gpa = "GPA score", rank = "Ranking")
mylogit <- glm(admit ~ gre + gpa + rank, data = mydata, family = "binomial")

## Showing Odds Ratios
logit(mylogit)

## Showing coefficients
logit(mylogit, or = FALSE)

## End(Not run)

mStats documentation built on Nov. 23, 2020, 9:07 a.m.