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
#> --- 
# }