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

• `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 funcion that is used for blockmodeling (for randomized networks).

### Author(s)

Marjan Cugmas and Ales Ziberna

### References

Cugmas, M., Žiberna, A., & Ferligoj, A. (2019). Mechanisms Generating Asymmetric Core-Cohesive Blockmodels. Metodološki Zvezki, 16(1), 17-41.

`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 * tclu, mean = 0, sd = 1)
net[clu == 1, clu == 2] <- rnorm(n = tclu * tclu, mean = 4, sd = 1)
net[clu == 2, clu == 1] <- rnorm(n = tclu * tclu, mean = 0, sd = 1)
net[clu == 2, clu == 2] <- rnorm(n = tclu * tclu, 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]