PlackettLuce (version 0.2-9)

plfit: PlackettLuce Wrapper for Model-based Recursive Partitioning

Description

This is a wrapper around PlackettLuce as required by mob for model-based recursive partitioning. It is not intended for general use.

Usage

plfit(y, x = NULL, ref = 1L, start = NULL, weights = NULL,
  offset = NULL, ..., estfun = FALSE, object = FALSE)

Arguments

y

a "grouped_rankings" object giving the rankings to model.

x

unused.

ref

An integer or character string specifying the reference item (for which log worth will be set to zero). If NULL the sum of the log worth parameters is set to zero.

start

starting values for the worth parameters and the tie parameters on the raw scale (worth parameters need not be scaled to sum to 1). If normal is specified, exp(normal$mu) is used as starting values for the worth parameters. Coefficients from a previous fit can be passed as the result of a call to coef.PlackettLuce, or the coefficients element of a "PlackettLuce" object.

weights

an optional vector of weights for each ranking.

offset

unused.

...

additional arguments passed to PlackettLuce.

estfun

logical. If TRUE the empirical estimating functions (score/gradient contributions) are returned.

object

logical. If TRUE the fitted model is returned.

Value

a list with elements

coefficients

model coefficients.

objfun

the negative log-likelihood.

estfun

if estfun the empirical estimating functions.

object

if object the fitted model.

Examples

# NOT RUN {
# rankings
R <- matrix(c(1, 2, 0, 0,
              4, 1, 2, 3,
              2, 1, 1, 1,
              1, 2, 3, 0,
              2, 1, 1, 0,
              1, 0, 3, 2), nrow = 6, byrow = TRUE)
colnames(R) <- c("apple", "banana", "orange", "pear")
R <- as.rankings(R)

# group rankings into two groups
G <- group(R, rep(1:2, 3))

# plfit() gives the same results as PlackettLuce()
pl <- plfit(G)
pl$coefficients
-pl$objfun

mod <- PlackettLuce(R)
coef(mod)
logLik(mod)
# }

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