mlogit: Multinomial Logistic Regression
In predkmeans: Covariate Adaptive Clustering

Description Usage Arguments Details Value Author(s) See Also Examples

Solves a multinomial logistic problem using Newton-Raphson method

mlogit(
  Y,
  X,
  beta = NULL,
  add.intercept = FALSE,
  betaOnly = FALSE,
  tol.zero = 1e-08,
  verbose = T,
  suppressFittedWarning = FALSE,
  maxNR.print.level = 0,
  iterlim = 150,
  checkY = TRUE
)

`Y`	A matrix of the outcomes, with K columns for the K groups. Row sums of the matrix should be equal to one, but entries do not have to be 0/1 (but they should be positive). i.e. this is a matrix of hard or soft assignments to K categories. The first column is used as the reference category.
`X`	matrix of covariates for regression. Should have the same number of rows (observations) as Y. Coefficients for all parameters in X are computed for K-1 groups. The coefficients corresponding to the first column of Y are assumed to be zero.
`beta`	starting values for the optimziation. Should be given as a matrix of column vectors, each vector a different starting value. If null, defaults to zeros.
`add.intercept`	a logical indicator of whether an intercept column should be added to X
`betaOnly`	logical indicator of whether only the parameter estimates beta should be returned. Otherwise, beta is returned along with fitted objects. See Output.
`tol.zero`	the tolerance threshold for considering a fitted value as equal to zero. Used for warning about fitted values of 0/1. Is NOT part of the optimization control parameters.
`verbose`	logical indicator that controls whether text indicating progress is output to display
`suppressFittedWarning`	indicator of whether or not warnings about fitted values of 1 are returned
`maxNR.print.level`	numeric value giving the level of output produced by maxNR. see `?maxNR` for details. Defaults to 0.
`iterlim`	iteration limit for maxNR. Defaults to 150.
`checkY`	indicator for whether Y should be checked to be a valid assignment matrix. Set to `FALSE` if using decimal values in Y.

The optimization is done using the maxNR function from the maxLik package. The log-likehood function, along with its gradient and hessian, are implemented as C++ functions (via the RcppArmadillo package).

A list containing the following:

`beta`	a p x K matrix of parameter estimates corresponding to the K columns of Y and p covariates in X
`fitted01`	indicator of whether fitted values of 1 were present.
`fitted`	the fitted probabilities
`res.best`	the best result from the maxNR fit
`status`	small data frame summarizing the status of the fits
`res. all`	a list containing the results from all maxNR fits

Joshua Keller

predkmeans

n <- 2000
X <- cbind(1,
           matrix(rnorm(2*n), nrow=n, ncol=2),
           rbinom(n, size=1, prob=0.3))
beta <- cbind(rep(0, 4),
              c(0.5, 1, 0, -1),
              c(0, 2, 2, 0))
probs <- exp(X %*% beta)
probs <- probs/rowSums(probs)
Y <- t(apply(probs, 1, function(p) rmultinom(1, 1, p)))
mfit <- mlogit(Y=Y, X=X, betaOnly=TRUE)
mfit