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) 1.042 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
#> ---
# }