mll: mll

In VMTdeJong/mPerformance: Performance measures for multinomial outcome data

Description

The multinomial log-likelihood, for predicted or fitted values. Requires the predicted (or fitted) probability matrix p, and one of the following: labels, indices or indicator.matrix. Preferably one of the two former.

Usage

mll(p, labels, indices, indicator.matrix, names = colnames(p), na.rm = T)

Arguments

p	An n x K matrix of probabilities, where n is the number of observations, and K the number of mutually exclusive outcome categories.
labels	Vector of length n, containing the labels (character or factor) of the observed outcome categories. If specified, must correspond with the column names of p or with names.
indices	Optional. A vector of length n, containing the indices k, k = 1,...,K, of the observed outcome categories. If specified, these indices must corresond with their respective indices in p.
indicator.matrix	Optional. An n x K matrix indicating the outcome category of each observation, where n is the number of observations, and K the number of mutually exclusive outcome categories. If specified, the order of the columns should correspond with the order of the columns of p.
names	Optional. What are the labels to which the columns of p should be matched? By default, the colnames of the outcome matrix p.
na.rm	logical. Should missing values (including NaN) be removed?

Details

The multinomial log-likelihood

Value

mll provides the multinomial log-likelihood.

Examples

# When we observe 3 outcomes with indices 1, 2 and 3,
# we can obtain the log-likelihood of the null-model:
# by letting all observed probabilities equal 1/3:
probabilities <- matrix(1/3, nrow = 3, ncol = 3)
indices <- c(1,2,3)
mll(probabilities, indices = indices)

# If the outcome is measured as a factor, the levels need to correspond to colnames(p):
labels <- as.factor(indices)
colnames(probabilities) <- labels
mll(probabilities, labels)

VMTdeJong/mPerformance documentation built on May 14, 2019, 7:42 a.m.