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,
  state_labels = 1:results$Num_States,
  cov_levels = NULL,
  decimal_pts = 2,
  include_plot = TRUE
)

Arguments

results

results of transition probabilities, i.e., results$results_trans

cov_labels

a matrix such that row i represents the labels for covariate i; default labels for covariate i is 1:i

state_labels

a vector of strings that represent the state labels; default is 1:Num_States

cov_levels

a matrix such that each row is a combination of covariate levels; default is all possible combinations of covariates

decimal_pts

specify the number of decimal points of the results; default is 2

include_plot

display plot if TRUE; default is TRUE

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]