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 

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 
...  currently not used 
object  object inheriting from class 'JointAI' 
start  the first iteration of interest
(see 
end  the last iteration of interest
(see 
thin  thinning interval (integer; see 
quantiles  posterior quantiles 
subset  subset of parameters/variables/nodes (columns in the MCMC
sample). Follows the same principle as the argument

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

mess  logical; should messages be given? Default is

parm  same as 
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
.
if (FALSE) { mod1 < lm_imp(y ~ C1 + C2 + M2, data = wideDF, n.iter = 100) summary(mod1, missinfo = TRUE) coef(mod1) confint(mod1) }