Summarary Method for Multivariate or Univarate Regression
Source:R/regression_summary.R
regression_summary.Rd
Summarary Method for Multivariate or Univarate 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) 1.057 0.130 0.806 1.297
#> gender -0.513 0.060 -0.628 -0.399
#> as.factor(education)2 0.131 0.118 -0.094 0.355
#> as.factor(education)3 -0.125 0.097 -0.316 0.044
#> as.factor(education)4 -0.419 0.107 -0.611 -0.199
#> as.factor(education)5 -0.545 0.113 -0.764 -0.355
#> ---
#> A2
#> Post.mean Post.sd Cred.lb Cred.ub
#> (Intercept) -0.873 0.110 -1.068 -0.669
#> gender 0.486 0.050 0.387 0.575
#> as.factor(education)2 -0.028 0.104 -0.231 0.153
#> as.factor(education)3 0.113 0.087 -0.057 0.276
#> as.factor(education)4 -0.042 0.097 -0.230 0.151
#> as.factor(education)5 0.085 0.096 -0.085 0.269
#> ---
#> Residual Correlation Matrix:
#> A1 A2
#> A1 1.000 -0.313
#> A2 -0.313 1.000
#> ---
# }