View source: R/nemSymBMinout.R
nemSymBMinout | R Documentation |
Generate a symmetric network with a selected blockmodel type and partition with a specified number of incomers and outgoers. Considers local network mechanisms when creating links within blocks.
nemSymBMinout( X = X, partition = partition, M = M, formula = NULL, theta = NULL, nin = 5, nout = 20, minClusterSize = 5, k = 1000, loops = FALSE, randomizeP = 0, randomSD = 0.02 )
X |
Initial binary network; of class |
partition |
A desired partition in a vector format. Each unique value (positive integers) represents one cluster. |
M |
Desired image matrix with block densities. |
formula |
The list of local netork mechanisms to be considered. |
theta |
A vector with the mechanisms' weights/strengths. |
nin |
Number of incomers. |
nout |
Number of outgoers. |
minClusterSize |
Minimum cluster size. |
k |
Number of iterations. |
loops |
Wheter loops are allowed or not (default |
randomizeP |
The share of units to be randomly relocated between clusters. |
randomSD |
The srandard deviation of a normal distribution form which the random part of weighed network statistics is sampled. |
The list with the following elements:
initialNetwork
- Initial network; of class matrix
.
finalNetwork
- Final (generated) network; of class matrix
.
initialPartition
- Initial partition.
finalPartition
- Final partition (i.e., partition after randomization and after incomers and outgoers).
M
- The desired (specified) image matrix.
k
- The number of iterations.
combinedPartitions
- Data frame with initial and final partition.
whenIncomers
- A vector of which elements tells us at which iterations the incomers were added.
whenOutgoers
- A vector of which elements tells us at which iterations the outgoers were removed.
ERR
- Sum of squared differences between the desired and empirical densities across blocks; for each iteration.
linkERR
- The difference in the number of links between the generated number of links and desired number of links; for each iteration.
Marjan Cugmas and Aleš Žiberna
formula <- list(mutuality, popularity, OTPtransitivity) X <- matrix(sample(c(0,1), size = 9**2, replace = TRUE), nrow = 9) diag(X) <- 0 M <- matrix(c(0.1, 0.8, 0.8, 0.1), nrow = 2) partition <- c(1, 2, 2, 1, 1, 2, 2, 2, 1) nemSymBMinout(X = X, partition = partition, formula = formula, theta = c(1, 1, 1), M = M, k = 100, minClusterSize = 2, nin = 10, nout = 5, loops = FALSE)
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