Obtain and print the summary, (fixed effects) coefficients
(coef) and credible interval (confint).
summary(object, ...)
# S3 method for remiod
summary(object, start = NULL, end = NULL, thin = NULL,
quantiles = c(0.025, 0.975), outcome = NULL, exclude_chains = NULL,
warn = TRUE, mess = TRUE, ...)
# S3 method for summary.remiod
print(x, digits = 3, ...)
# S3 method for summary.remiod
coef(object, start = NULL, end = NULL,
thin = NULL, subset = NULL, exclude_chains = NULL, warn = TRUE,
mess = TRUE, ...)object inheriting from class 'remoid'
additional, optional arguments
truncnamed list specifying limits of truncation for the distribution of the named incomplete variables (see the vignette ModelSpecification)
hyperparslist of hyper-parameters, as obtained by
default_hyperpars()
scale_varsnamed vector of (continuous) variables that
will be centred and scaled (such that mean = 0 and sd = 1)
when they enter a linear predictor to improve
convergence of the MCMC sampling. Default is that all
numeric variables and integer variables with >20 different
values will be scaled.
If set to FALSE no scaling will be done.
customnamed list of JAGS model chunks (character strings) that replace the model for the given variable.
append_data_listlist that will be appended to the list
containing the data that is passed to rjags
(data_list). This may be necessary if additional data /
variables are needed for custom (covariate) models.
progress.barcharacter string specifying the type of
progress bar. Possible values are "text" (default), "gui",
and "none" (see update). Note: when
sampling is performed in parallel it is not possible to
display a progress bar.
quietlogical; if TRUE then messages generated by
rjags during compilation as well as the progress bar
for the adaptive phase will be suppressed,
(see jags.model)
keep_scaled_mcmcshould the "original" MCMC sample (i.e.,
the scaled version returned by coda.samples()) be
kept? (The MCMC sample that is re-scaled to the scale of the
data is always kept.)
modelnamecharacter string specifying the name of the model file (including the ending, either .R or .txt). If unspecified a random name will be generated.
modeldirdirectory containing the model file or directory in which the model file should be written. If unspecified a temporary directory will be created.
overwritelogical; whether an existing model file with
the specified <modeldir>/<modelname> should be
overwritten. If set to FALSE and a model already
exists, that model will be used. If unspecified (NULL)
and a file exists, the user is asked for input on how to
proceed.
keep_modellogical; whether the created JAGS model file
should be saved or removed from (FALSE; default) when
the sampling has finished.
the first iteration of interest
(see window.mcmc)
the last iteration of interest
(see window.mcmc)
thinning interval (integer; see window.mcmc).
For example, thin = 1 (default) will keep the MCMC samples
from all iterations; thin = 5 would only keep every 5th
iteration.
posterior quantiles
specify outcome variable to select imputation model(s) to summarize. Default generates summaries for all models.
optional vector of the index numbers of chains that should be excluded
logical; should warnings be given? Default is
TRUE.
logical; should messages be given? Default is
TRUE.
an object of class summary.remiod
the minimum number of significant digits to be printed in values.
subset of parameters/variables/nodes (columns in the MCMC
sample). Follows the same principle as the argument
monitor_params and selected_parms.
summary information, including parameter posterior mean, posterior SD,
quantiles, tail probability tail-prob, Gelman-Rubin criterion
GR-crit, the ratio of the Monte Carlo error and posterior standard
deviation) for specified parameters MCE/SD.
# \donttest{
# data(schizow)
test = remiod(formula = y6 ~ tx + y0 + y1 + y3, data = schizow,
trtvar = 'tx', algorithm = 'jags', method="MAR",
ord_cov_dummy = FALSE, n.adapt = 50, n.chains = 1,
n.iter = 50, thin = 2, warn = FALSE, seed = 1234)
#> NOTE: Stopping adaptation
#>
#>
summary(object = test, outcome = c("y6","y3"))
#>
#>
#> Call:
#> clm_imp_custom(formula = y6 ~ tx + y0 + y1 + y3, data = data,
#> trtvar = trtvar, n.chains = n.chains, n.adapt = n.adapt,
#> n.iter = n.iter, thin = thin, monitor_params = list(imps = TRUE,
#> other_models = TRUE, other = c(eta_monits)), refcats = refcats,
#> model_order = model_order, seed = seed, warn = warn, mess = mess,
#> ord_cov_dummy = ord_cov_dummy)
#>
#>
#> # --------------------------------------------------------------------- #
#> Bayesian cumulative logit model for "y6"
#> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
#>
#> Posterior summary:
#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
#> beta_y6_tx -1.407 0.2389 -1.879 -1.022 0
#> beta_y6_y0 -0.341 0.1000 -0.503 -0.145 0
#> beta_y6_y1 0.578 0.0895 0.435 0.767 0
#> beta_y6_y3 1.478 0.0817 1.329 1.621 0
#> gamma_y6[1] -1.792 0.2402 -2.232 -1.422 0
#> gamma_y6[2] -4.668 0.2673 -5.123 -4.173 0
#> gamma_y6[3] -6.070 0.3441 -6.603 -5.464 0
#>
#>
#> # --------------------------------------------------------------------- #
#> Bayesian cumulative logit model for "y3"
#> # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
#>
#> Posterior summary:
#> Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
#> beta_y3_tx -1.2169 0.2007 -1.670 -0.929 0.00
#> beta_y3_y0 0.0965 0.0827 -0.012 0.266 0.24
#> beta_y3_y1 1.5373 0.1372 1.342 1.773 0.00
#> gamma_y3[1] -1.3473 0.2092 -1.685 -0.891 0.00
#> gamma_y3[2] -3.8126 0.3566 -4.464 -3.136 0.00
#> gamma_y3[3] -5.8234 0.3733 -6.392 -5.226 0.00
#>
#>
#> # ----------------------------------------------------------- #
#>
#> MCMC settings:
#> Iterations = 52:100
#> Sample size per chain = 25
#> Thinning interval = 2
#> Number of chains = 1
#>
#> Number of observations: 437
# }