# ggs

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##### Import MCMC samples into a ggs object than can be used by all ggs_* graphical functions.

This function manages MCMC samples from different sources (JAGS, MCMCpack, STAN -both via rstan and via csv files-) and converts them into a data frame tbl. The resulting data frame has four columns (Iteration, Chain, Parameter, value) and six attributes (nChains, nParameters, nIterations, nBurnin, nThin and description). The ggs object returned is then used as the input of the ggs_* functions to actually plot the different convergence diagnostics.

##### Usage
ggs(S, family = NA, description = NA, burnin = TRUE,
par_labels = NA, sort = TRUE, inc_warmup = FALSE,
stan_include_auxiliar = FALSE)
##### Arguments
S

Either a mcmc.list object with samples from JAGS, a mcmc object with samples from MCMCpack, a stanreg object with samples from rstanarm, a brmsfit object with samples from brms, a stanfit object with samples from rstan, or a list with the filenames of csv files generated by stan outside rstan (where the order of the files is assumed to be the order of the chains). ggmcmc guesses what is the original object and tries to import it accordingly. rstan is not expected to be in CRAN soon, and so coda::mcmc is used to extract stan samples instead of the more canonical rstan::extract.

family

Name of the family of parameters to process, as given by a character vector or a regular expression. A family of parameters is considered to be any group of parameters with the same name but different numerical value between square brackets (as beta[1], beta[2], etc).

description

Character vector giving a short descriptive text that identifies the model.

burnin

Logical or numerical value. When logical and TRUE (the default), the number of samples in the burnin period will be taken into account, if it can be guessed by the extracting process. Otherwise, iterations will start counting from 1. If a numerical vector is given, the user then supplies the length of the burnin period.

par_labels

data frame with two colums. One named "Parameter" with the same names of the parameters of the model. Another named "Label" with the label of the parameter. When missing, the names passed to the model are used for representation. When there is no correspondence between a Parameter and a Label, the original name of the parameter is used. The order of the levels of the original Parameter does not change.

sort

Logical. When TRUE (the default), parameters are sorted first by family name and then by numerical value.

inc_warmup

Logical. When dealing with stanfit objects from rstan, logical value whether the warmup samples are included. Defaults to FALSE.

stan_include_auxiliar

Logical value to include "lp__" parameter in rstan, and "lp__", "treedepth__" and "stepsize__" in stan running without rstan. Defaults to FALSE.

##### Value

D A data frame tbl with the data arranged and ready to be used by the rest of the ggmcmc functions. The data frame has four columns, namely: Iteration, Chain, Parameter and value, and six attributes: nChains, nParameters, nIterations, nBurnin, nThin and description. A data frame tbl is a wrapper to a local data frame, behaves like a data frame and its advantage is related to printing, which is compact. For more details, see tbl_df() in package dplyr.

##### References

Fern<U+00E1>ndez-i-Mar<U+00ED>n, Xavier (2016) ggmcmc: Analysis of MCMC Samples and Bayesian Inference. Journal of Statistical Software, 70(9), 1-20. doi:10.18637/jss.v070.i09

• ggs
##### Examples
# NOT RUN {
# Assign 'S' to be a data frame suitable for \code{ggmcmc} functions from
# a coda object called s
data(linear)
S <- ggs(s)        # s is a coda object

# Get samples from 'beta' parameters only
S <- ggs(s, family = "beta")
# }

Documentation reproduced from package ggmcmc, version 1.2, License: GPL-2

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