summary.RoBSA {RoBSA} | R Documentation |
Summarize fitted RoBSA object
Description
summary.RoBSA
creates a numerical
summary of the RoBSA object.
Usage
## S3 method for class 'RoBSA'
summary(
object,
type = "ensemble",
conditional = FALSE,
exp = FALSE,
parameters = FALSE,
probs = c(0.025, 0.975),
logBF = FALSE,
BF01 = FALSE,
transform_factors = TRUE,
short_name = FALSE,
remove_spike_0 = FALSE,
...
)
Arguments
object |
a fitted RoBSA object. |
type |
whether to show the overall RoBSA results ( |
conditional |
show the conditional estimates (assuming that the
alternative is true). Defaults to |
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 |
logBF |
show log of the BFs. Defaults to |
BF01 |
show BF in support of the null hypotheses. Defaults to
|
transform_factors |
Whether factors with orthonormal prior
distributions should be transformed to differences from the grand mean. Defaults
to |
short_name |
whether the prior distribution names should be
shortened. Defaults to |
remove_spike_0 |
whether prior distributions equal to spike
at 0 should be removed from the |
... |
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
## Not run:
# (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")
## End(Not run)