summary.BMRMM: Summary Method for Objects of Class `BMRMM`

Description

Summarizing an object of class BMRMM, including results for transition probabilities and duration times, if applicable.

Usage

# S3 method for BMRMM
summary(object, delta = 0.02, digits = 2, ...)

Value

An object of class BMRMMsummary with the following elements:

`trans.global`	global test results for transition probabilities (see Details).
`trans.probs.mean`	mean for the posterior transition probabilities.
`trans.probs.sd`	standard deviation for the posterior transition probabilities.
`trans.local.mean.diff`	the absolute difference in transition probabilities for a pair of covariate levels (see Details).
`trans.local.null.test`	probability for the null hypothesis that the difference between two covariate levels is not significant (see Details).
`dur.global`	global test results for duration times (see Details).
`dur.mix.params`	mixture parameters taken from the last MCMC iteration if duration times follow a mixture gamma distribution.
`dur.mix.probs`	mixture probabilities for each covariate taken from the last MCMC iteration if duration times follow a mixture gamma distribution.

Arguments

object: an object of class BMRMM.
delta: threshold for the null hypothesis for the local tests of transition probabilities (see Details). Default is 0.02.
digits: integer used for number formatting. Default is 2.
...: further arguments for the summary function.

Details

We give more explanation for the global tests and local tests results.

Global tests (for both transition probabilities and duration times)

Global tests are presented as a matrix, where the row denote the number of clusters and the column represents covariates. For each row i and column j, the matrix entry is the percentage of the number of the clusters within the stored MCMC samples for this covariate, i.e., an estimation for Pr(# clusters for covariate j == i). We note that the probability Pr(# clusters for covariate j > 1) would be the probability for the null hypothesis that the covariate j is significant.
Local tests (for transition probabilities only)

Local tests focus on a particular covariate and compare the influence among its levels when the other covariates values are fixed.
Given a pair of levels of covariate j, say j_1 and j_2, and given the levels of other covariates, the null hypothesis is that the difference between j_1 and j_2 is not significant for transition probabilities. It is calculated as the percentage of the samples with absolute difference less than delta.The local tests provide two matrices of size d0 x d0 where d0 is the number of states:
1. mean.diff -- the mean of the absolute difference in each transition type between levels j_1 and j_2;
2. null.test -- the probability of the null hypothesis that j_1 and j_2 have the same significance for each transition type.

Examples

Run this code

results <- BMRMM(foxp2sm, num.cov = 2, simsize = 50, 
                 cov.labels = list(c("F", "W"), c("U", "L", "A")),
                 duration.distr = list('mixgamma',shape=rep(1,3),rate=rep(1,3)))
sm <- summary(results)
sm