Obtain and print the summary, (fixed effects) coefficients (coef) and credible interval (confint) for an object of class 'JointAI'.

# S3 method for Dmat
print(x, digits = getOption("digits"), scientific = getOption("scipen"), ...)

# S3 method for JointAI
summary(object, start = NULL, end = NULL, thin = NULL,
  quantiles = c(0.025, 0.975), subset = NULL, exclude_chains = NULL,
  outcome = NULL, missinfo = FALSE, warn = TRUE, mess = TRUE, ...)

# S3 method for summary.JointAI
print(x, digits = max(3, .Options$digits - 4), ...)

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

# S3 method for JointAI
confint(object, parm = NULL, level = 0.95,
  quantiles = NULL, start = NULL, end = NULL, thin = NULL,
  subset = NULL, exclude_chains = NULL, warn = TRUE, mess = TRUE, ...)

# S3 method for JointAI
print(x, digits = max(4, getOption("digits") - 4), ...)

Arguments

x

an object of class summary.JointAI or JointAI

digits

the minimum number of significant digits to be printed in values.

scientific

A penalty to be applied when deciding to print numeric values in fixed or exponential notation, by default the value obtained from getOption("scipen")

...

currently not used

object

object inheriting from class 'JointAI'

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

subset

subset of parameters/variables/nodes (columns in the MCMC sample). Follows the same principle as the argument monitor_params in *_imp.

exclude_chains

optional vector of the index numbers of chains that should be excluded

outcome

optional; vector identifying for which outcomes the summary should be given, either by specifying their indices, or their names (LHS of the respective model formulas as character string).

missinfo

logical; should information on the number and proportion of missing values be included in the summary?

warn

logical; should warnings be given? Default is TRUE.

mess

logical; should messages be given? Default is TRUE.

parm

same as subset (for consistency with confint method for other types of objects)

level

confidence level (default is 0.95)

See also

The model fitting functions lm_imp, glm_imp, clm_imp, lme_imp, glme_imp, survreg_imp and coxph_imp, and the vignette Parameter Selection for examples how to specify the parameter subset.

Examples


if (FALSE) {
mod1 <- lm_imp(y ~ C1 + C2 + M2, data = wideDF, n.iter = 100)

summary(mod1, missinfo = TRUE)
coef(mod1)
confint(mod1)
}