hmm_mcmc_normal {oHMMed}

MCMC Sampler for the Hidden Markov Model with Normal emission densities

Description

MCMC Sampler for the Hidden Markov Model with Normal emission densities

Usage

hmm_mcmc_normal(
  data,
  prior_T,
  prior_means,
  prior_sd,
  iter = 600,
  warmup = floor(iter/5),
  thin = 1,
  seed = sample.int(.Machine$integer.max, 1),
  init_T = NULL,
  init_means = NULL,
  init_sd = NULL,
  print_params = TRUE,
  verbose = TRUE
)

Arguments

data

(numeric) normal data

prior_T

(matrix) prior transition matrix

prior_means

(numeric) prior means

prior_sd

(numeric) a single prior standard deviation

iter

(integer) number of MCMC iterations

warmup

(integer) number of warmup iterations

thin

(integer) thinning parameter. By default, 1

seed

(integer) optional parameter; seed parameter

init_T

(matrix) optional parameter; initial transition matrix

init_means

(numeric) optional parameter; initial means

init_sd

(numeric) optional parameter; initial standard deviation

print_params

(logical) optional parameter; print parameters every iteration. By default, TRUE

verbose

(logical) optional parameter; print additional messages. By default, TRUE

Details

Please see supplementary information at doi:10.1186/s12859-024-05751-4 for more details on the algorithm.

For usage recommendations please see https://github.com/LynetteCaitlin/oHMMed/blob/main/UsageRecommendations.pdf.

Value

List with following elements:

• data: data used for simulation

• samples: list with samples

• estimates: list with various estimates

• idx: indices with iterations after the warmup period

• priors: prior parameters

• inits: initial parameters

• last_iter: list with samples from the last MCMC iteration

• info: list with various meta information about the object

References

Claus Vogl, Mariia Karapetiants, Burçin Yıldırım, Hrönn Kjartansdóttir, Carolin Kosiol, Juraj Bergman, Michal Majka, Lynette Caitlin Mikula. Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed). BMC Bioinformatics 25, 151 (2024). doi:10.1186/s12859-024-05751-4

Examples

# Simulate normal data
N <- 2^10
true_T <- rbind(c(0.95, 0.05, 0),
                c(0.025, 0.95, 0.025),
                c(0.0, 0.05, 0.95))

true_means <- c(-5, 0, 5)
true_sd <- 1.5

simdata_full <- hmm_simulate_normal_data(L = N, 
                                         mat_T = true_T, 
                                         means = true_means,
                                         sigma = true_sd)
simdata <- simdata_full$data
hist(simdata, 
     breaks = 40, 
     probability = TRUE,  
     main = "Distribution of the simulated normal data")
lines(density(simdata), col = "red")

# Set numbers of states to be inferred
n_states_inferred <- 3

# Set priors
prior_T <- generate_random_T(n_states_inferred)
prior_means <- c(-18, -1, 12)
prior_sd <- 3

# Simmulation settings
iter <- 50
warmup <- floor(iter / 5) # 20 percent
thin <- 1
seed <- sample.int(10000, 1)
print_params <- FALSE # if TRUE then parameters are printed in each iteration
verbose <- FALSE # if TRUE then the state of the simulation is printed

# Run MCMC sampler
res <- hmm_mcmc_normal(data = simdata,
                       prior_T = prior_T,
                       prior_means = prior_means,
                       prior_sd = prior_sd,
                       iter = iter,
                       warmup = warmup,
                       seed = seed,
                       print_params = print_params,
                       verbose = verbose)
res

summary(res) # summary output can be also assigned to a variable

coef(res) # extract model estimates

# plot(res) # MCMC diagnostics