There is a direct correspondence between the inverse covariance matrix and multiple regression kwan2014regression,Stephens1998BGGM. This readily allows for converting the GGM parameters to regression coefficients. All data types are supported.
Usage
# S3 method for class 'explore'
coef(object, iter = NULL, progress = TRUE, ...)
Value
An object of class coef
, containting two lists.
betas
A list of length p, each containing a p - 1 byiter
matrix of posterior samplesobject
An object of classexplore
(the fitted model).
Examples
# \donttest{
# note: iter = 250 for demonstrative purposes
# data
Y <- ptsd[,1:4]
##########################
### example 1: ordinal ###
##########################
# fit model (note + 1, due to zeros)
fit <- explore(Y + 1,
type = "ordinal",
iter = 250,
progress = FALSE)
# summarize the partial correlations
reg <- coef(fit, progress = FALSE)
# summary
summ <- summary(reg)
summ
#> BGGM: Bayesian Gaussian Graphical Models
#> ---
#> Type: ordinal
#> Formula: ~ 1
#> ---
#> Call:
#> explore(Y = Y + 1, type = "ordinal", iter = 250, progress = FALSE)
#> ---
#> Coefficients:
#>
#> B1:
#> Node Post.mean Post.sd Cred.lb Cred.ub
#> B2 0.276 0.112 0.062 0.485
#> B3 0.102 0.139 -0.182 0.358
#> B4 0.408 0.088 0.222 0.544
#>
#> B2:
#> Node Post.mean Post.sd Cred.lb Cred.ub
#> B1 0.244 0.095 0.062 0.419
#> B3 0.565 0.095 0.357 0.727
#> B4 -0.027 0.107 -0.202 0.206
#>
#> B3:
#> Node Post.mean Post.sd Cred.lb Cred.ub
#> B1 0.083 0.108 -0.116 0.270
#> B2 0.498 0.092 0.296 0.645
#> B4 0.306 0.101 0.099 0.498
#>
#> B4:
#> Node Post.mean Post.sd Cred.lb Cred.ub
#> B1 0.402 0.087 0.221 0.567
#> B2 -0.031 0.118 -0.256 0.213
#> B3 0.386 0.121 0.125 0.590
#>
# }