qha (version 0.0.8)

hqa: Combination of Qualitative Harmonic and Multiple Factor Analyses and Clustering

Description

It realizes the combination between two methods for the processing of longitudinal categorical data: the Qualitative Harmonic and the Multiple Factorial Analysis.

Usage

hqa(base, conteos=FALSE, units=NULL, durat=FALSE, periodos=NULL, pesos = NULL, ilustra=NULL,ilustc = NULL,ilust.type = NULL, nfact=5, nfcl=5, k.clust=NULL,  combinat=TRUE, vector, tableclass=FALSE, clasifica=TRUE)

Arguments

base

object of type data frame or matrix

conteos

TRUE if you want to do data frame ID,MOD,DURATION. Default TRUE

units

time: "secs", "mins", "hours", "days", "weeks", "months", "years". Default = NULL

durat

TRUE if you want to calculate the DURATION by the function. Default FALSE

periodos

a vector containing the duration of each period of time

pesos

a vector of row weights

ilustra

object of type data frame or matrix with the illustrative variables. Default NULL

ilustc

a vector containing the number of variables in each ilustrative group

ilust.type

the type of variable in each ilustrative group: "c" for quantitative variables, "s" for quantitative variables scales to unit variance, "n" for qualitative variables. By default all variables are qualitative

nfact

number of axes to use into the factorial analysis . Default nfact=5

nfcl

number of axes to use in the classification. Default nfcl=5

k.clust

number of classes to work. Default k.clust= NULL

combinat

TRUE if you want to do combination HQA and MFA, FALSE if you want to do only AAC. Default TRUE)

vector

a vector containing the number of categories for each fuzzy variable

tableclass

TRUE if you want a function to Suggest you the number of axes to use in the classificacion. Default FALSE

clasifica

TRUE if you want to do the classificacion. Default TRUE

Value

Details

A new statistical methodology is proposed in order to analyze longitudinal categorical data. This methodology considers the use of two methods: Qualitative Harmonic and Multiple Factor Analysis. The analysis is complemented by an analysis of classification using the first coordinates factoriales of the data.

References

Corrales, M. L., & Pardo, C. E. (2015). Analisis de datos longitudinales cualitativos con analisis de correspondencias y clasificacion. Comunicaciones en Estadistica, 8(1), 11-32. http://dx.doi.org/10.15332/s2027-3355.2015.0001.01

Examples

#data(ratingTV)
#rating <- hqa(base=ratingTV$tab,ilustra=ratingTV$ilus, #vector=c(15,15,15,15,15,15), ilustc=c(4))
#10
#rating$HQA
#rating$Clases
#rating$Active
#rating$Ilust