fungible (version 2.4.4)

summary.monte: Summary Method for an Object of Class Monte

Description

summary method for class ``monte"

Usage

# S3 method for monte
summary(
  object,
  digits = 3,
  compute.validities = FALSE,
  Total.stats = TRUE,
  ...
)

Value

Various descriptive statistics will be computed within groups including"

clus.size

Number of objects within each group.

centroids

Group centroids.

var.matrix

Within group variances.

cor.list

Expected within group correlations.

obs.cor

Observed within group correlations.

skew.list

Expected within group indicator skewness values.

obs.skew

Observed within group indicator skewness values.

kurt.list

Expected within group indicator kurtosis values.

obs.kurt

Observed within group indicator kurtosis values.

validities

Observed indicator validities.

Total.cor

Total sample correlation matrix.

Total.skew

Total sample indicator skewness.

Total.kurt

Total sample indicator kurtosis.

Arguments

object

An object of class monte, usually, a result of a call to monte.

digits

Number of digits to print. Default = 3.

compute.validities

Logical: If TRUE then the program will calculate the indicator validities (eta^2) for the generated data.

Total.stats

Logical: If TRUE then the program will return the following statistics for the total sample: (1) indicator correlation matrix, (2) indicator skewness, (3) indicator kurtosis.

...

Optional arguments.

Examples

## set up a 'monte' run for the Fisher iris data

sk.lst <- list(c(0.120,  0.041,  0.106,  1.254),                     #
                c(0.105, -0.363, -0.607, -0.031),
                c(0.118,  0.366,  0.549, -0.129) )
              
              
kt.lst <- list(c(-0.253, 0.955,  1.022,  1.719),
                c(-0.533,-0.366,  0.048, -0.410),
                c( 0.033, 0.706, -0.154, -0.602))
                
cormat <- lapply(split(iris[,1:4],iris[,5]), cor)

my.iris <- monte(seed = 123, nvar = 4, nclus = 3, cor.list = cormat, 
	              clus.size = c(50, 50, 50),
                eta2 = c(0.619, 0.401, 0.941, 0.929), 
                random.cor = FALSE,
                skew.list = sk.lst, kurt.list = kt.lst, 
                secor = .3, 
                compactness = c(1, 1, 1), 
                sortMeans = TRUE)
summary(my.iris)