Summarize fitted 'RoBTT' object

summary.RoBTT creates summary tables for a RoBTT object.

# S3 method for RoBTT
summary(
  object,
  type = "ensemble",
  conditional = FALSE,
  group_estimates = FALSE,
  probs = c(0.025, 0.975),
  logBF = FALSE,
  BF01 = FALSE,
  short_name = FALSE,
  remove_spike_0 = FALSE,
  ...
)

Arguments

object

a fitted 'RoBTT' object

type

whether to show the overall 'RoBTT' results ("ensemble"), an overview of the individual models ("models"), an overview of the individual models MCMC diagnostics ("diagnostics"), or a detailed summary of the individual models ("individual"). Can be abbreviated to first letters.

conditional

show the conditional estimates (assuming that the alternative is true). Defaults to FALSE. Only available for type == "conditional".

group_estimates

show the model-averaged mean and standard deviation estimates for each group.

probs

quantiles of the posterior samples to be displayed. Defaults to c(.025, .975)

logBF

show log of Bayes factors. Defaults to FALSE.

BF01

show Bayes factors in support of the null hypotheses. Defaults to FALSE.

short_name

whether priors names should be shortened to the first (couple) of letters. Defaults to FALSE.

remove_spike_0

whether spike prior distributions with location at zero should be omitted from the summary. Defaults to FALSE.

...

additional arguments

Value

summary.RoBTT returns a list of tables of class 'BayesTools_table'.

See also

RoBTT()

Examples

if (FALSE) {
# using the example data from Darwin
data("fertilization", package = "RoBTT")
fit <- RoBTT(
  x1       = fertilization$Self,
  x2       = fertilization$Crossed,
  prior_delta = prior("cauchy", list(0, 1/sqrt(2))),
  prior_rho   = prior("beta",   list(3, 3)),
  seed        = 1, 
  chains      = 1,
  warmup      = 1000,
  iter        = 2000,
  control     = set_control(adapt_delta = 0.95)
)

# summary can provide many details about the model
summary(fit)

# estimates from the conditional models can be obtained with
summary(fit, conditional = TRUE)

# overview of the models and their prior and posterior probability, marginal likelihood,
# and inclusion Bayes factor can be obtained with
summary(fit, type = "models")

# diagnostics overview, containing the maximum R-hat, minimum ESS, maximum MCMC error, and
# maximum MCMC error / sd across parameters for each individual model can be obtained with
summary(fit, type = "diagnostics")

# summary of individual models and their parameters can be further obtained by
summary(fit, type = "individual")
}