| simu.olbm {ordinalLBM} | R Documentation |
Simulate OLBM data
Description
It simulates an ordinal data matrix according to OLBM.
Usage
simu.olbm(M, P, Pi, rho, delta, mu, sd, thresh)
Arguments
M |
The number of rows of the ordinal matrix Y. |
P |
The number of columns of the ordinal matrix Y. |
Pi |
A Q x L connectivity matrix to manage missing data (coded az zeros in Y). |
rho |
A vector of length Q, containing multinomial probabilities for row cluster assignments. |
delta |
A vector of length L, containing multinomial probabilities for column cluster assignments. |
mu |
A Q x L matrix containing the means of the latent Gaussian distributions. |
sd |
A Q x L matrix containing the standard deviations of the latent Gaussian distributions. |
thresh |
A K+1 vector containing the sorted tresholds used to simulate the ordinal entries in Y, where K is the number of ordinal modalities. The first entry in tresh must be -Inf, the last entry +Inf. |
Value
It returns a list containing:
Y |
An M x P matrix. The observed ordinal entries are integers between 1 and K. Missing data are coded as zeros. |
Rclus |
A vector of length M containing the row cluster memberships. |
Cclus |
A vector of length P containing the column cluster memberships. |
References
Corneli M.,Bouveyron C. and Latouche P. (2019) Co-Clustering of ordinal data via latent continuous random variables and a classification EM algorithm. (https://hal.archives-ouvertes.fr/hal-01978174)
Examples
M <- 150
P <- 100
Q <- 3
L <- 2
## connectivity matrix
Pi <- matrix(.7, nrow = Q, ncol = L)
Pi[1,1] <- Pi[2,2] <- Pi[3,2] <- .5
## cluster memberships proportions
rho <- c(1/3, 1/3 ,1/3)
delta <- c(1/2, 1/2)
# Thresholds
thresh <- c(-Inf, 2.37, 2.67, 3.18, 4.33, Inf) # K = 5
## Gaussian parameters
mu <- matrix(c(0, 3.4, 2.6, 0, 2.6, 3.4), nrow = Q, ncol = L)
sd <- matrix(c(1.2,1.4,1.0,1.2,1.4,1.0), nrow = Q, ncol = L)
## Data simulation
dat <- simu.olbm(M, P, Pi, rho, delta, mu, sd, thresh)