Summarary Method for Multivariate or Univarate Regression

Usage

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("A1", "A2", 
                          "gender", "education"))

fit_mv_ordinal <- estimate(Y, formula = ~ gender + as.factor(education),
                           type = "continuous",
                           iter = 250,
                           progress = TRUE)
#> BGGM: Posterior Sampling 
#> BGGM: Finished

regression_summary(fit_mv_ordinal)
#> BGGM: Bayesian Gaussian Graphical Models 
#> --- 
#> Type: continuous 
#> Formula: ~ gender + as.factor(education) 
#> --- 
#> Coefficients: 
#>  
#> A1 
#>                       Post.mean Post.sd Cred.lb Cred.ub
#> (Intercept)               1.042   0.132   0.769   1.294
#> gender                   -0.510   0.061  -0.634  -0.403
#> as.factor(education)2     0.159   0.117  -0.058   0.381
#> as.factor(education)3    -0.115   0.098  -0.294   0.059
#> as.factor(education)4    -0.409   0.114  -0.639  -0.223
#> as.factor(education)5    -0.535   0.111  -0.737  -0.325
#> --- 
#> A2 
#>                       Post.mean Post.sd Cred.lb Cred.ub
#> (Intercept)              -0.857   0.116  -1.051  -0.630
#> gender                    0.484   0.053   0.382   0.583
#> as.factor(education)2    -0.054   0.101  -0.229   0.144
#> as.factor(education)3     0.104   0.084  -0.060   0.252
#> as.factor(education)4    -0.050   0.093  -0.245   0.107
#> as.factor(education)5     0.073   0.098  -0.130   0.258
#> --- 
#> Residual Correlation Matrix: 
#>        A1     A2
#> A1  1.000 -0.312
#> A2 -0.312  1.000
#> --- 
# }