hmm_mcmc_normal: MCMC Sampler for the Hidden Markov Model with Normal emission...

In oHMMed: HMMs with Ordered Hidden States and Emission Densities

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 \Sexpr[results=rd]{tools:::Rd_expr_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). \Sexpr[results=rd]{tools:::Rd_expr_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

oHMMed documentation built on May 29, 2024, 2:20 a.m.