mlogit: Multinomial regression based on phreg regression

In mets: Analysis of Multivariate Event Times

Multinomial regression based on phreg regression

Description

Fits multinomial regression model

P_i = \frac{ \exp( X^\beta_i ) }{ \sum_{j=1}^K \exp( X^\beta_j ) }

for

i=1,..,K

where

\beta_1 = 0

, such that

\sum_j P_j = 1

using phreg function. Thefore the ratio

\frac{P_i}{P_1} = \exp( X^\beta_i )

Usage

mlogit(formula, data, offset = NULL, weights = NULL, fix.X = FALSE, ...)

Arguments

formula	formula with outcome (see coxph)
data	data frame
offset	offsets for partial likelihood
weights	for score equations
fix.X	to have same coefficients for all categories
...	Additional arguments to lower level funtions

Details

Coefficients give log-Relative-Risk relative to baseline group (first level of factor, so that it can reset by relevel command). Standard errors computed based on sandwhich form

DU^-1 \sum U_i^2 DU^-1

.

Can also get influence functions (possibly robust) via iid() function, response should be a factor.

Can fit cumulative odds model as a special case of interval.logitsurv.discrete

Author(s)

Thomas Scheike

Examples

data(bmt)
dfactor(bmt) <- cause1f~cause
drelevel(bmt,ref=3) <- cause3f~cause
dlevels(bmt)

mreg <- mlogit(cause1f~+1,bmt)
summary(mreg)

mreg <- mlogit(cause1f~tcell+platelet,bmt)
summary(mreg)

mreg3 <- mlogit(cause3f~tcell+platelet,bmt)
summary(mreg3)

## inverse information standard errors 
lava::estimate(coef=mreg3$coef,vcov=mreg3$II)

## predictions based on seen response or not 
newdata <- data.frame(tcell=c(1,1,1),platelet=c(0,1,1),cause1f=c("2","1","0"))
predictmlogit(mreg,newdata,response=FALSE)
predictmlogit(mreg,newdata)

mets documentation built on May 29, 2024, 3:51 a.m.