Summarize network histogram fits

Detailed summary of a fitted network histogram. The print methods of the fitted objects give a short identification only; the model fit statistics are reported here.

Usage

# S3 method for class 'network_histogram'
summary(object, ...)

# S3 method for class 'multinethist'
summary(object, ...)

# S3 method for class 'hnethist'
summary(object, ...)

# S3 method for class 'summary.network_histogram'
print(x, digits = getOption("digits"), ...)

Arguments

object

a network_histogram object, that is, a fit returned by nethist, multinethist, or hnethist.

...

additional arguments. Currently unused.

x

a summary.network_histogram object.

digits

number of significant digits used when printing numeric quantities.

Value

summary() returns an object inheriting from summary.network_histogram: a list holding the fitted object together with the number of nodes, the number of blocks, the log-likelihood, the degrees of freedom, AIC, and BIC. Multilayer fits add n_layers, and hnethist fits add the selected number of shapes s and the number of candidate models n_candidate. The print method returns its input invisibly.

Details

The summary method is defined on the common base class, so it applies to nethist, multinethist, and hnethist objects alike. The subclass methods add the fields specific to them and are otherwise deferred to the base method via NextMethod(); see network_histogram-class.

The estimated probabilities themselves are not printed, since the matrix grows with the number of blocks. Use object$thetahat or fitted() to obtain them.

See also

network_histogram-class, print.nethist, logLik.network_histogram

Examples

# \donttest{
set.seed(42)
data(polblog)
fit <- suppressMessages(nethist(polblog))
fit
#> 
#> Network histogram fit (nethist)
#> 
#> Nodes: 1224   Blocks: 16   Bandwidth: 73
#> Method: Profile Likelihood
#> thetahat: 16 x 16 probability matrix
#> Normalized log-likelihood: -2.969964
#> 
#> Use summary() for details.
summary(fit)
#> 
#> Network histogram fit (nethist)
#> 
#> Nodes: 1224   Blocks: 16   Bandwidth: 73
#> Method: Profile Likelihood
#> Edge density (rho_hat): 0.02233205
#> 
#> Model fit:
#>   Normalized log-likelihood: -2.969964
#>   Log-likelihood: -49642.94  (df = 136)
#>   AIC: 99557.89   BIC: 101125.4
#>   Mean squared error: 0.01769046
#> 
#> thetahat: 16 x 16 probability matrix (use fitted() or $thetahat)
# }