Fit DPMM — runModel • DPMM

This function fits the Dirichlet Process Mixture Model (DPMM).

Usage

runModel(
  dataset,
  mcmc_iterations = 2500,
  thinning = 1,
  L = 10,
  mcmc_chains = 2,
  standardise = TRUE,
  seed = NULL
)

Arguments

dataset: dataframe with continuous and/or categorical variables.
mcmc_iterations: Number of MCMC iterations.
thinning: Interval for collecting MCMC samples.
L: Number of DPMM components fitted.
mcmc_chains: Number of MCMC chains fitted.
standardise: Should continuous variables be standardised. default = TRUE
seed: specify seed to be used for reproducibility. (default = NULL)

Value

Output: List of class 'dpmm_fit'

dataset: dataframe with 1 row but defined the same way as the dataset fitted.
L: Number of DPMM components fitted.
mcmc_chains: Number of MCMC chains fitted.
samples: MCMC samples.
standardise: TRUE or FALSE whether values were standardised.
mean_values: Mean values for covariates standardised.
sd_values: SD values for covariates standardised.

Examples

if (FALSE) { # \dontrun{
## load dataset
data(dataset_1)

## fit model
posteriors <- runModel(dataset_1,
                       mcmc_iterations = 100,
                       L = 6,
                       mcmc_chains = 2,
                       standardise = TRUE,
                       seed = 1)
} # }