Summarize fitted RoBSA object

summary.RoBSA creates a numerical summary of the RoBSA object.

# S3 method for RoBSA
summary(
  object,
  type = "ensemble",
  conditional = FALSE,
  exp = FALSE,
  parameters = FALSE,
  probs = c(0.025, 0.975),
  logBF = FALSE,
  BF01 = FALSE,
  transform_orthonormal = TRUE,
  short_name = FALSE,
  remove_spike_0 = FALSE,
  ...
)

Arguments

object

a fitted RoBSA object.

type

whether to show the overall RoBSA results ("ensemble"), an overview of the individual models ("models"), or detailed summary for the individual models ("individual").

conditional

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

exp

whether exponents of the regression estimates should be also presented

parameters

character vector of parameters (or a named list with of character vectors for summary and diagnostics tables) specifying the parameters (and their grouping) for the summary table

probs

quantiles of the posterior samples to be displayed. Defaults to c(.025, .50, .975)

logBF

show log of the BFs. Defaults to FALSE.

BF01

show BF in support of the null hypotheses. Defaults to FALSE.

transform_orthonormal

Whether factors with orthonormal prior distributions should be transformed to differences from the grand mean. Defaults to TRUE.

short_name

whether the prior distribution names should be shortened. Defaults to FALSE.

remove_spike_0

whether prior distributions equal to spike at 0 should be removed from the prior_list

...

additional arguments

Value

summary of a RoBSA object

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

Note

See diagnostics() for visual convergence checks of the individual models.

See also

RoBSA(), diagnostics(), check_RoBSA()

Examples

if (FALSE) {
# (execution of the example takes several minutes)
# example from the README (more details and explanation therein)
data(cancer, package = "survival")
priors <- calibrate_quartiles(median_t = 5, iq_range_t = 10, prior_sd = 0.5)
df <- data.frame(
  time         = veteran$time / 12,
  status       = veteran$status,
  treatment    = factor(ifelse(veteran$trt == 1, "standard", "new"), levels = c("standard", "new")),
  karno_scaled = veteran$karno / 100
)
RoBSA.options(check_scaling = FALSE)
fit <- RoBSA(
  Surv(time, status) ~ treatment + karno_scaled,
  data   = df,
  priors = list(
    treatment    = prior_factor("normal", parameters = list(mean = 0.30, sd = 0.15),
                                truncation = list(0, Inf), contrast = "treatment"),
    karno_scaled = prior("normal", parameters = list(mean = 0, sd = 1))
  ),
  test_predictors = "treatment",
  prior_intercept = priors[["intercept"]],
  prior_aux       = priors[["aux"]],
  parallel = TRUE, seed = 1
)

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

# note that the summary function contains additional arguments
# that allow to obtain a specific output, i.e, the conditional estimates
# (assuming that the non-null models are true) can be obtained
summary(fit, conditional = TRUE)

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

# and the model diagnostics overview, containing maximum R-hat and minimum ESS across parameters
# but see '?diagnostics' for diagnostics plots for individual model parameters
summary(fit, type = "diagnostics")

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

}