Summarary Method for Multivariate or Univarate Regression

regression_summary(object, cred = 0.95, ...)

Arguments

object

An object of class estimate

cred

Numeric. The credible interval width for summarizing the posterior distributions (defaults to 0.95; must be between 0 and 1).

...

Currently ignored

Value

A list of length p including the summaries for each regression.

Examples

# \donttest{ # note: iter = 250 for demonstrative purposes # data Y <- bfi Y <- subset(Y, select = c("E5", "N5", "gender", "education")) fit_mv_ordinal <- estimate(Y, formula = ~ gender + as.factor(education), type = "ordinal", iter = 250, progress = FALSE)
#> Warning: imputation during model fitting is #> currently only implemented for 'continuous' data.
regression_summary(fit_mv_ordinal)
#> BGGM: Bayesian Gaussian Graphical Models #> --- #> Type: ordinal #> Formula: ~ gender + as.factor(education) #> --- #> Coefficients: #> #> E5 #> Post.mean Post.sd Cred.lb Cred.ub #> (Intercept) 1.852 0.533 1.049 3.142 #> gender 0.169 0.066 0.065 0.295 #> as.factor(education)2 0.215 0.109 0.024 0.437 #> as.factor(education)3 0.271 0.104 0.089 0.445 #> as.factor(education)4 0.206 0.103 0.019 0.404 #> as.factor(education)5 0.345 0.128 0.120 0.593 #> --- #> N5 #> Post.mean Post.sd Cred.lb Cred.ub #> (Intercept) 0.210 0.114 -0.012 0.434 #> gender 0.502 0.140 0.291 0.835 #> as.factor(education)2 -0.127 0.103 -0.345 0.058 #> as.factor(education)3 -0.104 0.081 -0.258 0.034 #> as.factor(education)4 -0.218 0.104 -0.427 -0.024 #> as.factor(education)5 -0.229 0.103 -0.449 -0.038 #> --- #> Residual Correlation Matrix: #> E5 N5 #> E5 1.00 -0.18 #> N5 -0.18 1.00 #> ---
# }