int_to_prob {mHMMbayes}

Transforming a set of Multinomial logit regression intercepts to probabilities

Description

int_to_prob transforms a set of Multinomial logit regression intercepts to the corresponding state transition or categorical emission observation probabilities. Note that the first state or category is assumed to be the reference category, hence no intercept is to specified for the first state or category.

Usage

int_to_prob(int_matrix)

Arguments

int_matrix

A matrix with (number of states OR categories - 1) columns and number of rows to be determined by the user. For obtaining the set of probabilities of the complete transition probability matrix gamma or categorical emission distribution matrix, the number of rows equals the number of states m. The first state / category is assumed to be the reference category, no intercept is to be specified for this first category.

Details

Designed to ease the specification of informative hyper-prior values for the mean intercepts of the transition probability matrix gamma and categorical emission distribution(s) of the multilevel hidden Markov model through the functions prior_gamma and prior_emiss_cat. No check is performed on correct specifications of the dimensions.

Value

int_to_prob returns a matrix containing probabilities with each row summing to one, with the number of columns equal to the number of states / categories and the number of rows equal to the number rows specified in the input matrix.

See Also

prob_to_int for transforming a set of probabilities to a set of Multinomial logit regression intercepts, prior_gamma and prior_emiss_cat for specifying informative hyper-priors for the the multilevel hidden Markov model and mHMM to fit a multilevel hidden Markov model.

Examples

# example for transition probability matrix gamma with 3 states
m <- 3
gamma_int <- matrix(c(-1, -1,
                       3,  0,
                       0,  2), ncol = m-1, nrow = m, byrow = TRUE)
gamma_prob <- int_to_prob(gamma_int)
gamma_prob