lmm.theta {PerMallows} | R Documentation |
Compute the MLE for the dispersion parameter (theta) given a sample of n permutations and a central permutation
lmm.theta(data, sigma_0 = identity.permutation(dim(data)[2]), dist.name = "kendall", disk = FALSE)
data |
the matrix with the permutations to estimate |
sigma_0 |
optional the consensus permutation. If not given it is assumed to be the identity permutation |
dist.name |
optional the name of the distance used by the model. One of: kendall (default), cayley, hamming, ulam |
disk |
optional can only be true if estimating a MM under the Ulam distance. Insted of generating the whole set of SYT and count of permutations per distance, it loads the info from a file in the disk |
The MLE for the dispersion parameter
data <- matrix(c(1,2,3,4, 1,4,3,2, 1,2,4,3), nrow = 3, ncol = 4, byrow = TRUE) lmm.theta(data, dist.name="kendall") lmm.theta(data, dist.name="cayley") lmm.theta(data, dist.name="cayley", sigma_0=c(1,4,3,2)) lmm.theta(data, dist.name="hamming") lmm.theta(data, dist.name="ulam")