print.longclust: Brief overview of the longclust object

In longclust: Model-Based Clustering and Classification for Longitudinal Data

Brief overview of the longclust object

Description

Prints the number of components, probabily matrix, degrees of freedom and the component means of the computed best model.

Usage

  ## S3 method for class 'longclust'
print(x, ...)

Arguments

x	An object of type longclust, computed by longclustEM.
...	Default Arguments

Author(s)

Paul D. McNicholas, K. Raju Jampani and Sanjeena Subedi

Examples

library(mvtnorm)
m1 <- c(23,34,39,45,51,56)
S1 <- matrix(c(1.00, -0.90, 0.18, -0.13, 0.10, -0.05, -0.90, 
1.31, -0.26, 0.18, -0.15, 0.07, 0.18, -0.26, 4.05, -2.84, 
2.27, -1.13, -0.13, 0.18, -2.84, 2.29, -1.83, 0.91, 0.10, 
-0.15, 2.27, -1.83, 3.46, -1.73, -0.05, 0.07, -1.13, 0.91, 
-1.73, 1.57), 6, 6)
m2 <- c(16,18,15,17,21,17)
S2 <- matrix(c(1.00, 0.00, -0.50, -0.20, -0.20, 0.19, 0.00, 2.00, 
0.00, -1.20, -0.80, -0.36,-0.50, 0.00, 1.25, 0.10, -0.10, -0.39, 
-0.20, -1.20, 0.10, 2.76, 0.52, -1.22,-0.20, -0.80, -0.10, 0.52, 
1.40, 0.17, 0.19, -0.36, -0.39, -1.22, 0.17, 3.17), 6, 6)
m3 <- c(8, 11, 16, 22, 25, 28)
S3 <- matrix(c(1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00, 
-0.20, -0.64, 0.26, 0.00, 0.00, -0.20, 1.04, -0.17, -0.10, 0.00, 
0.00, -0.64, -0.17, 1.50, -0.65, 0.00, 0.00, 0.26, -0.10, -0.65, 
1.32, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00), 6, 6)
m4 <- c(12, 9, 8, 5, 4 ,2)
S4 <- diag(c(1,1,1,1,1,1))
data <- matrix(0, 40, 6)
data[1:10,] <- rmvnorm(10, m1, S1)
data[11:20,] <- rmvnorm(10, m2, S2)
data[21:30,] <- rmvnorm(10, m3, S3)
data[31:40,] <- rmvnorm(10, m4, S4)
clus <- longclustEM(data, 3, 5, linearMeans=TRUE)
print(clus)

## The function is currently defined as
function (tch, ...) 
{
    cat("Number of Clusters:", tch$Gbest, "\n")
    cat("z:\n")
    print(tch$zbest)
    cat("\n")
    for (g in 1:tch$Gbest) {
        cat("Cluster: ", g, "\n")
        cat("v: ", tch$nubest[g], "\n")
        cat("mean:", tch$mubest[g, ], "\n\n")
    }
  }

longclust documentation built on Oct. 20, 2023, 9:08 a.m.