JointAI (version 0.6.1)

summary.JointAI: Summary of an object of class JointAI

Description

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

Usage

# S3 method for JointAI
summary(
  object,
  start = NULL,
  end = NULL,
  thin = NULL,
  quantiles = c(0.025, 0.975),
  subset = NULL,
  exclude_chains = NULL,
  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

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 (see window.mcmc)

quantiles

posterior quantiles

subset

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

exclude_chains

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

warn

logical; should warnings be given? Default is TRUE. (Note: this applies only to warnings given directly by JointAI.)

mess

logical; should messages be given? Default is TRUE. (Note: this applies only to messages given directly by JointAI.)

…

currently not used

x

an object of class summary.JointAI or JointAI

digits

minimal number of significant digits, see print.default.

parm

same as subset

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

# NOT RUN {
mod1 <- lm_imp(y ~ C1 + C2 + M2, data = wideDF, n.iter = 100)

summary(mod1)
coef(mod1)
confint(mod1)


# }