RF {blockmodeling}R Documentation

Calculate the value of the Relative Fit function

Description

The function calculates the value of the Relative Fit function.

Usage

RF(res, m = 10, loops = TRUE)

Arguments

res

An object returned by the function optRandomParC.

m

The number of randomized networks for the estimation of the expected value of a criterion function. It has to be as high as possible. Defaults to 10.

loops

Whether loops are allowed in randomized networks or not, default TRUE.

Details

The function randomizes an empirical network to compute the value of the Relative Fit function. The networks are ranomized in such a way that the values on the links are randomly relocated.

Value

Author(s)

Marjan Cugmas and Ales Ziberna

References

Cugmas, M., ┼Żiberna, A., & Ferligoj, A. (2019). Mechanisms Generating Asymmetric Core-Cohesive Blockmodels. Metodolo┼íki Zvezki, 16(1), 17-41.

See Also

optRandomParC

Examples

n <- 8 # If larger, the number of partitions increases 
# dramatically as does if we increase the number of clusters
net <- matrix(NA, ncol = n, nrow = n)
clu <- rep(1:2, times = c(3, 5))
tclu <- table(clu)
net[clu == 1, clu == 1] <- rnorm(n = tclu[1] * tclu[1], mean = 0, sd = 1)
net[clu == 1, clu == 2] <- rnorm(n = tclu[1] * tclu[2], mean = 4, sd = 1)
net[clu == 2, clu == 1] <- rnorm(n = tclu[2] * tclu[1], mean = 0, sd = 1)
net[clu == 2, clu == 2] <- rnorm(n = tclu[2] * tclu[2], mean = 0, sd = 1)
# Install package blockmodeling and then run the following lines.
res <- optRandomParC(M = net, k = 2, rep = 10, approaches = "hom", homFun = "ss", blocks = "com")
RF(res = res, m = 100, loops = TRUE)

[Package blockmodeling version 1.0.5 Index]