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

In fungible: Psychometric Functions from the Waller Lab

Summary Method for an Object of Class Monte

Description

summary method for class “monte"

Usage

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

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.

Value

Various descriptive statistics will be computed within groups including"

1. clus.size Number of objects within each group.

2. centroids Group centroids.

3. var.matrix Within group variances.

4. Ratio of within group variances (currently printed but not saved.

5. cor.list Expected within group correlations.

6. obs.cor Observed within group correlations.

7. skew.list Expected within group indicator skewness values.

8. obs.skew Observed within group indicator skewness values.

9. kurt.list Expected within group indicator kurtosis values.

10. obs.kurt Observed within group indicator kurtosis values.

11. validities Observed indicator validities.

12. Total.cor Total sample correlation matrix.

13. Total.skew Total sample indicator skewness.

14. Total.kurt Total sample indicator kurtosis.

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)

fungible documentation built on March 31, 2023, 5:47 p.m.