Simpsons (version 0.1.0)

summary.Simpson: Summary method

Description

Matrix of all regression results: Each individual cluster and the whole dataset (Alldata) of all clusters, their sample size, and regression estimates (beta and intercept).

Usage

"summary"(object, ...)

Arguments

object

...

Not used.

Value

Returns list called 'Res'. The first object contains clusters numbers, their sample size, and regression estimates (beta and intercept) for variables X and Y defined in the object. The second object is an object of class Mclust, and contains all diagnostics of the cluster analysis. For more details, see package Mclust by Fraley & Raftery.

References

Kievit, R.A., Frankenhuis, W. E. , Waldorp, L. J. & Borsboom, D. (in preparation). Simpson's Paradox in Psychological Science: A Practical Guide. http://rogierkievit.com/simpsonsparadox.html

Fraley, C., & Raftery, A. E. (1998a) MCLUST: Software for model-based cluster and discriminant analysis. Department of Statistics, University of Washington: Technical Report No.342.

Fraley, C., & Raftery, A. E. (1998b). How many clusters? Which clustering method? - Answers via model-based cluster analysis. Department of Statistics, University of Washington: Technical Report no. 329.

Examples

## Not run: 
# #Example
# 	#Simulating 100 males 
# 	coffeem=rnorm(100,100,15)
# 	neuroticismm=(coffeem*.8)+rnorm(100,15,8)
# 	clusterid=rep(1,100)
# 	males=cbind(coffeem,neuroticismm,clusterid)
# 
# 	#Simulating 100 females
# 	coffeef=rnorm(100,100,15)
# 	neuroticismf=160+((coffeef*-.8)+rnorm(100,15,8))
# 	clusterid=rep(2,100)
# 	females=cbind(coffeef,neuroticismf,clusterid)
# 		data=data.frame(rbind(males,females))
# 	colnames(data) <- c("Coffee","Neuroticism","gender")
# example1=Simpsons(Coffee,Neuroticism,clusterid=gender, data=data) 
# summary(example1)
# ## End(Not run)