`R/helpfunctions_summary.R`

, `R/summary.JointAI.R`

`summary.JointAI.Rd`

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), ...)
```

- 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)

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`

.