View source: R/mutinomialreg.R
mlogit | R Documentation |
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, fix.X = FALSE, ...)
formula |
formula with outcome (see |
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 |
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.
Can fit cumulative odds model as a special case of interval.logitsurv.discrete
Thomas Scheike
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.