mets (version 1.2.8.1)

mlogit: 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, ...)

Arguments

formula

formula with outcome (see coxph)

data

data frame

offset

offsets for partial likelihood

weights

for score equations

...

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.

Examples

# NOT RUN {
data(bmt)
dfactor(bmt) <- cause1f~cause
drelevel(bmt,ref=3) <- cause3f~cause
dlevels(bmt)

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

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

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

# }