get_estimated_post_mean_and_sd {BMRMM} R Documentation

## Transition Probabilities: Posterior Mean and Standard Deviation

### Description

Print and plot the posterior mean and standard deviation for transition probabilities from MCMC samples under given different combinations of covariate levels.

### Usage

get_estimated_post_mean_and_sd(
results,
cov_labels = NULL,

### Details

For each row of 'cov_levels', the function returns two matrices of size d0xd0 where d0 is the number of states: (1) the posterior mean and (2) the posterior standard deviation of transition probabilities, computed from MCMC samples after burn-ins and thinning. The default for 'cov_levels' is all possible combinations of covariate levels.

### Value

No return value, called for printing and plotting posterior distribution of transition probabilities.

### Examples


# Examples using the shortened version of the simulated Foxp2 data set, foxp2_sm

# get results for all combinations of covariate levels
results <- BMRMM(foxp2_sm,num_cov=2,duration_type='None',simsize=50)
get_estimated_post_mean_and_sd(results$results_trans) # get results for covariate levels ("HET","U") and ("WT","U") cov_labels <- matrix(c("HET","WT","","U","L","A"),nrow=2,byrow=TRUE) cov_levels <- matrix(c(1,1,2,1),nrow=2,byrow=TRUE) get_estimated_post_mean_and_sd(results$results_trans,cov_labels,cov_levels=cov_levels)



[Package BMRMM version 0.0.1 Index]