An emc object with a limited number of samples and subjects of the Forstmann dataset. The object is a nested list of lenght three, each list containing the MCMC samples of the respective chain. The MCMC samples are stored in the samples element.
Format
An emc object. An emc object is a list with a specific structure and elements, as outlined below.
- data
A list of dataframes, one for each subject included
- par_names
A character vector containing the model parameter names
- n_pars
The number of parameters in the model
- n_subjects
The number of unique subject ID's in the data
- model
A list containing the model functions
- nuisance
A logical vector indicating which parameters are nuisance parameters
- subjects
A vector containing the unique subject ID's
- type
The type of model e.g., "standard" or "diagonal"
- prior
A list that holds the prior for
theta_mu(the model parameters). Contains the mean (theta_mu_mean), covariance matrix (theta_mu_var), degrees of freedom (v), and scale (A) and inverse covariance matrix (theta_mu_invar)- samples
A list with defined structure containing the samples, see the Samples Element section for more detail
- sampler_nuis
A sampler list for nuisance parameters (in this case there are none), similarly structured to the overall samples list of one of the MCMC chains.
Samples Element
The samples element of a emc object contains the different types of samples
estimated by EMC2. These include the three main types of samples
theta_mu, theta_var and alpha as well as a number of
other items which are detailed here.
- theta_mu
samples used for estimating the model parameters (group level), an array of size (n_pars x n_samples)
- theta_var
samples used for estimating the parameter covariance matrix, an array of size (n_pars x n_pars x n_samples)
- alpha
samples used for estimating the subject random effects, an array of size (n_pars x n_subjects x n_samples)
- stage
A vector containing what PMwG stage each sample was drawn in
- subj_ll
The winning particles log-likelihood for each subject and sample
- a_half
Mixing weights used during the Gibbs step when creating a new sample for the covariance matrix
- last_theta_var_inv
The inverse of the last samples covariance matrix
- idx
The index of the last sample drawn