Binary Response

summary

R Documentation

Summarize the results from an object of class remiod

Description

Obtain and print the summary, (fixed effects) coefficients (coef) and credible interval (confint).

Usage

summary(object, ...)

## S3 method for class '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 class 'summary.remiod'
print(x, digits = 3, ...)

## S3 method for class 'summary.remiod'
coef(object, start = NULL, end = NULL,
  thin = NULL, subset = NULL, exclude_chains = NULL, warn = TRUE,
  mess = TRUE, ...)

Arguments

`object`	object inheriting from class 'remoid'
`...`	additional, optional arguments `trunc` named list specifying limits of truncation for the distribution of the named incomplete variables (see the vignette ModelSpecification) `hyperpars` list of hyper-parameters, as obtained by `default_hyperpars()` `scale_vars` named 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. `custom` named list of JAGS model chunks (character strings) that replace the model for the given variable. `append_data_list` list 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.bar` character 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. `quiet` logical; 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_mcmc` should 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.) `modelname` character string specifying the name of the model file (including the ending, either .R or .txt). If unspecified a random name will be generated. `modeldir` directory containing the model file or directory in which the model file should be written. If unspecified a temporary directory will be created. `overwrite` logical; 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_model` logical; whether the created JAGS model file should be saved or removed from (`FALSE`; default) when the sampling has finished.
`start`	the first iteration of interest (see `window.mcmc`)
`end`	the last iteration of interest (see `window.mcmc`)
`thin`	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.
`quantiles`	posterior quantiles
`outcome`	specify outcome variable to select imputation model(s) to summarize. Default generates summaries for all models.
`exclude_chains`	optional vector of the index numbers of chains that should be excluded
`warn`	logical; should warnings be given? Default is `TRUE`.
`mess`	logical; should messages be given? Default is `TRUE`.
`x`	an object of class `summary.remiod`
`digits`	the minimum number of significant digits to be printed in values.
`subset`	subset of parameters/variables/nodes (columns in the MCMC sample). Follows the same principle as the argument `monitor_params` and `selected_parms`.

Value

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.

Examples


# 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)

summary(object = test, outcome = c("y6","y3"))

xsswang/remiod documentation built on Feb. 20, 2023, 9:29 a.m.