summary {cglasso} | R Documentation |
‘summary
’ produces a summary of the sequence of fitted models.
## S3 method for class 'glasso' summary(object, ..., gof = c("BIC", "AIC", "eBIC"), par.gof, digits = 4L)
object |
an object of class ‘ |
gof |
string specifying the measure of goodness-of-fit used to evaluate the fitted models. Default is ‘ |
par.gof |
the parameter of the measure of goodness-of-fit used to evaluate the fitted models. |
digits |
the minimum number of significant digits to be used: see ‘print.default’. |
... |
further arguments passed to the method function ‘print.data.frame’. |
The method function summary.glasso
gives information about the sequence of fitted models. The output is divided in two sections.
First section shows the call that produced object
followed by a data.frame
reporting the values of the tuning parameter used to fit the model (rho
), the number of non-zero estimates (df
), the values of the pseudo R-squared (R2
) describted in glasso
, mglasso
and cglasso
, the values of the measure of goodness-of-fit used to evaluate the fitted models and the ranking of the fitted models (Rank
). The model with the lowest measure of goodness-of-fit is pointed out by an arrow.
Second section shows the details of the selected model plus the number of connected components and the number of vertices per component.
The function ‘summary.glasso
’ computes and returns a list of summary statistics with the following elements:
table |
a data.frame containing the summary statistics used to evaluate the sequence of fitted models. |
which.min |
the number of the model with the lowest measure of goodness-of-fit. |
Luigi Augugliaro (luigi.augugliaro@unipa.it)
link{glasso}
, mglasso
, cglasso
, mle
, aic
bic
and ebic
.
library("cglasso") set.seed(123) ################# # cglasso model # ################# n <- 100L p <- 5L mu <- rep.int(0L, times = p) X <- rdatacggm(n = n, mu = mu, probr = 0.05) out <- cglasso(X = X) summary(out, gof = "AIC") summary(out, gof = "BIC") summary(out, gof = "eBIC") ############## # cggm model # ############## out_mle <- mle(out) summary(out_mle, gof = "AIC") summary(out_mle, gof = "BIC") summary(out_mle, gof = "eBIC") ################# # cglasso model # ################# R <- event(X) X <- as.matrix(X) X[R == 1L] <- NA out <- mglasso(X = X) summary(out, gof = "AIC") summary(out, gof = "BIC") summary(out, gof = "eBIC") ############## # mggm model # ############## out_mle <- mle(out) summary(out_mle, gof = "AIC") summary(out_mle, gof = "BIC") summary(out_mle, gof = "eBIC") ################# # glasso model # ################# X <- MASS::mvrnorm(n = n, mu = mu, Sigma = diag(p)) out <- glasso(X) summary(out, gof = "AIC") summary(out, gof = "BIC") summary(out, gof = "eBIC") ############# # ggm model # ############# out_mle <- mle(out) summary(out_mle, gof = "AIC") summary(out_mle, gof = "BIC") summary(out_mle, gof = "eBIC")