Multinomial regression based on phreg regression
Fits multinomial regression model
P_i = \frac{ \exp( X^β_i ) }{ ∑_{j=1}^K \exp( X^β_j ) }
for
i=1,..,K
where
β_1 = 0
, such that
∑_j P_j = 1
using phreg function. Thefore the ratio
\frac{P_i}{P_1} = \exp( X^β_i )
mlogit(formula, data, offset = NULL, weights = NULL, ...)
formula |
formula with outcome (see |
data |
data frame |
offset |
offsets for partial likelihood |
weights |
for score equations |
... |
Additional arguments to lower level funtions |
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 ∑ U_i^2 DU^-1
.
Can also get influence functions (possibly robust) via iid() function, response should be a factor.
Thomas Scheike
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)
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