Compute Regression Parameters for estimate Objects

There is a direct correspondence between the inverse covariance matrix and multiple regression (Kwan 2014; Stephens 1998) . This readily allows for converting the GGM parameters to regression coefficients. All data types are supported.

Usage

# S3 method for class 'estimate'
coef(object, iter = NULL, progress = TRUE, ...)

Arguments

object

An Object of class estimate

iter

Number of iterations (posterior samples; defaults to the number in the object).

progress

Logical. Should a progress bar be included (defaults to TRUE) ?

...

Currently ignored.

Value

An object of class coef, containting two lists.

• betas A list of length p, each containing a p - 1 by iter matrix of posterior samples

• object An object of class estimate (the fitted model).

References

Kwan CC (2014). “A regression-based interpretation of the inverse of the sample covariance matrix.” Spreadsheets in Education, 7(1), 4613.

Stephens G (1998). “On the Inverse of the Covariance Matrix in Portfolio Analysis.” The Journal of Finance, 53(5), 1821–1827.

Examples

# \donttest{
# note: iter = 250 for demonstrative purposes

#########################
### example 1: binary ###
#########################
# data
Y = matrix( rbinom(100, 1, .5), ncol=4)

# fit model
fit <- estimate(Y, type = "binary",
                iter = 250,
                progress = TRUE)
#> BGGM: Posterior Sampling 
#> BGGM: Finished

# summarize the partial correlations
reg <- coef(fit, progress = FALSE)

# summary
summ <- summary(reg)

summ
#> BGGM: Bayesian Gaussian Graphical Models 
#> --- 
#> Type: binary 
#> Formula: ~ 1 
#> --- 
#> Call: 
#> estimate(Y = Y, type = "binary", iter = 250, progress = TRUE)
#> --- 
#> Coefficients: 
#>  
#> 1: 
#>  Node Post.mean Post.sd Cred.lb Cred.ub
#>     2    -0.092   0.310  -0.690   0.556
#>     3    -0.079   0.333  -0.634   0.685
#>     4     0.014   0.336  -0.586   0.620
#> 
#> 2: 
#>  Node Post.mean Post.sd Cred.lb Cred.ub
#>     1    -0.101   0.271  -0.651   0.387
#>     3     0.160   0.346  -0.585   0.798
#>     4     0.264   0.266  -0.296   0.734
#> 
#> 3: 
#>  Node Post.mean Post.sd Cred.lb Cred.ub
#>     1    -0.074   0.310  -0.613   0.600
#>     2     0.158   0.368  -0.645   0.811
#>     4     0.157   0.286  -0.500   0.670
#> 
#> 4: 
#>  Node Post.mean Post.sd Cred.lb Cred.ub
#>     1     0.022   0.310  -0.539   0.602
#>     2     0.290   0.289  -0.282   0.814
#>     3     0.149   0.294  -0.541   0.647
#> 
# }