# MCMC Sampler sampler for the Hidden Markov with Gamma-Poisson emission densities ```r hmm_mcmc_gamma_poisson( data, prior_T, prior_betas, prior_alpha = 1, iter = 5000, warmup = floor(iter/1.5), thin = 1, seed = sample.int(.Machine$integer.max, 1), init_T = NULL, init_betas = NULL, init_alpha = NULL, print_params = TRUE, verbose = TRUE ) ``` ## Arguments - `data`: (numeric) data - `prior_T`: (matrix) prior transition matrix - `prior_betas`: (numeric) prior beta parameters - `prior_alpha`: (numeric) a single prior alpha parameter. By default, `prior_alpha=1` - `iter`: (integer) number of MCMC iterations - `warmup`: (integer) number of warmup iterations - `thin`: (integer) thinning parameter. By default, `1` - `seed`: (integer) seed parameter - `init_T`: (matrix) `optional parameter`; initial transition matrix - `init_betas`: (numeric) `optional parameter`; initial beta parameters - `init_alpha`: (numeric) `optional parameter`; initial alpha parameter - `print_params`: (logical) `optional parameter`; print estimated parameters every iteration. By default, `TRUE` - `verbose`: (logical) `optional parameter`; print additional messages. By default, `TRUE` ## Returns 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 ## Details Please see [supplementary information](https://static-content.springer.com/esm/art%3A10.1186%2Fs12859-024-05751-4/MediaObjects/12859_2024_5751_MOESM1_ESM.pdf) at 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](https://github.com/LynetteCaitlin/oHMMed/blob/main/UsageRecommendations.pdf). ## Examples ```r # Simulate Poisson-Gamma 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_betas <- c(2, 1, 0.1) true_alpha <- 1 simdata_full <- hmm_simulate_gamma_poisson_data(L = N, mat_T = true_T, betas = true_betas, alpha = true_alpha) simdata <- simdata_full$data hist(simdata, breaks = 40, probability = TRUE, main = "Distribution of the simulated Poisson-Gamma 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_betas <- c(1, 0.5, 0.1) prior_alpha <- 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_gamma_poisson(data = simdata, prior_T = prior_T, prior_betas = prior_betas, prior_alpha = prior_alpha, iter = iter, warmup = warmup, thin = thin, 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 ``` ## 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). tools:::Rd_expr_doi("10.1186/s12859-024-05751-4")

hmm_mcmc_gamma_poisson function