titanicgrp: titanicgrp

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

Description

The data is an grouped version of the 1912 Titanic passenger survival log,

Usage

data(titanicgrp)

Format

A data frame with 12 observations on the following 5 variables.

survive

number of passengers who survived

cases

number of passengers with same pattern of covariates

age

1=adult; 0=child

sex

1=male; 0=female

class

ticket class 1= 1st class; 2= second class; 3= third class

Details

titanicgrp is saved as a data frame. Used to assess risk ratios

Source

Found in many other texts

References

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

Hilbe, Joseph M (2014), Modeling Count Data, Cambridge University Press.

Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press.

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

Examples

library(MASS)   # if not automatically loaded

# LOGISTIC REGRESSION
library(LOGIT)
data(titanicgrp)
tg <- titanicgrp
head(tg)
tg$died <- tg$cases - tg$survive
summary(mylr <- glm( cbind(survive, died) ~ age + sex + factor(class),
                family=binomial, data=tg))
toOR(mylr)
P__disp(mylr)

# SCALED LOGISTIC REGRESSION
summary(myqr <- glm( cbind(survive, died) ~ age + sex + factor(class),
                family=quasibinomial, data=tg))
toOR(myqr)


# POISSON REGRESSION
# library(COUNT)
data(titanicgrp)
titanicgrp$class <-  as.factor(titanicgrp$class)
titanicgrp$logcases <- log(titanicgrp$cases)
glmpr <- glm(survive ~ age + sex + class + offset(logcases), family= poisson, data=titanicgrp)
summary(glmpr)
exp(coef(glmpr))

#lcases <- log(titanicgrp$cases)
#nb2o <- nbinomial(survive ~ age + sex + factor(class),
#                                        formula2 =~ age + sex,
#                                        offset = lcases,
#                                        mean.link="log",
#                                        scale.link="log_s",
#                                        data=titanicgrp)
#summary(nb2o)
#exp(coef(nb2o))

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