| 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 |
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
-
RF- The value of the Relative Fit function. -
err- The value of a criterion function that is used for blockmodeling (for empirical network). -
rand.err- A vector with the values of the criterion function that is used for blockmodeling (for randomized networks).
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)