mylgg: mylgg

In LOGIT: Functions, Data and Code for Binary and Binomial Data

Description

The data has 11 grouped observations and 6 variables. Grouped subset of medpar data.

Usage

data(mylgg)

Format

A data frame with 11 observations with the following 6 variables.

white

0=identified as non-white; identified as white

hmo

0=patient not an HMO member; 1=patient member of HMO

type

Type of admission: 1=elective;2=urgent; 3=emergency

alive

# patients alive per patient profile

dead

# patients died within 48 hrs admit per patient profile

m

# patients in each patient profile (same predictor values)

Details

mylgg is saved as a data frame. Used to assess odds ratios and predict survival folllowing surgery

Source

Subset of medpar data, grouped format.

References

Hilbe, Joseph M (2015), Practical Guide to Logistic Regression, Chapman & Hall/CRC.

Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC.

Examples

library(MASS)   # if not automatically loaded
# LOGISTIC REGRESSION
library(LOGIT)
data(mylgg)
mylgg
summary(lg <- glm(cbind(alive, dead) ~ white + hmo + factor(type),
                family=binomial, data=mylgg))
toOR(lg)
P__disp(lg)

LOGIT documentation built on May 29, 2017, 10:26 a.m.