RF {blockmodeling}R Documentation

Calculate the value of the Relative Fit function

Description

The function calculates the value of the Relative Fit function. Currently implemented only for one-relational one-mode or two-mode networks.

Usage

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

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 treated the same as any other values or not.

Details

The function randomizes an empirical network to compute the value of the Relative Fit function. The networks are randomized in such a way that the values on the links are randomly relocated. Other approaches to randomization also exist and might be more appropriate in some cases, see Cugmas et al. (2021).

Value

Author(s)

Marjan Cugmas and Aleš Žiberna

References

Cugmas, M., Žiberna, A., & Ferligoj, A. (2021). The Relative Fit measure for evaluating a blockmodel. Statistical Methods & Applications, 30(5), 1315-1335. doi:10.1007/s10260-021-00595-1

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)

res <- optRandomParC(M = net, k = 2, rep = 10, approaches = "hom", homFun = "ss", blocks = "com")
RF(res = res, m = 100, loops = TRUE)

[Package blockmodeling version 1.1.4 Index]