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

# S3 method for JointAI
summary(object, start = NULL, end = NULL,
  thin = NULL, quantiles = c(0.025, 0.975), subset = 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, 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, 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 lm_imp, glm_imp, clm_imp, lme_imp, glme_imp, survreg_imp and coxph_imp.

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

mod1 <- lm_imp(y~C1 + C2 + M2, data = wideDF, n.iter = 100)
#> This is new software. Please report any bugs to the package maintainer.
summary(mod1)
#> #> Linear model fitted with JointAI #> #> Call: #> lm_imp(formula = y ~ C1 + C2 + M2, data = wideDF, n.iter = 100) #> #> Posterior summary: #> Mean SD 2.5% 97.5% tail-prob. GR-crit #> (Intercept) 35.9266 25.078 -13.167 85.0785 0.1667 1.02 #> C1 -27.0295 17.506 -61.589 7.2831 0.1467 1.02 #> C2 0.8441 0.635 -0.335 2.0417 0.1933 1.03 #> M22 -0.4536 0.680 -1.779 0.7489 0.5467 1.00 #> M23 0.0231 0.630 -1.232 1.1619 0.9667 1.06 #> M24 -1.1790 0.680 -2.572 0.0179 0.0667 1.01 #> #> Posterior summary of residual std. deviation: #> Mean SD 2.5% 97.5% GR-crit #> sigma_y 2.24 0.165 1.94 2.59 1.05 #> #> #> MCMC settings: #> Iterations = 101:200 #> Sample size per chain = 100 #> Thinning interval = 1 #> Number of chains = 3 #> #> Number of observations: 100
coef(mod1)
#> (Intercept) C1 C2 M22 M23 M24 #> 35.92662356 -27.02953400 0.84411974 -0.45364779 0.02310191 -1.17901466
confint(mod1)
#> 2.5% 97.5% #> (Intercept) -13.1672406 85.07847609 #> C1 -61.5887206 7.28310117 #> C2 -0.3350643 2.04173199 #> M22 -1.7793492 0.74889976 #> M23 -1.2324534 1.16193443 #> M24 -2.5724005 0.01788788 #> sigma_y 1.9425775 2.58765691