catdap1 {catdap}

Categorical Data Analysis Program Package 01

Description

Calculates the degree of association between all the possible pairs of categorical variables.

Usage

catdap1(cdata, response.names = NULL, plot = 1, gray.shade = FALSE, ask = TRUE)
catdap1c(ctable, response.names = NULL, plot = 1, gray.shade = FALSE, ask = TRUE)

Arguments

cdata

categorical data matrix with variable names on the first row.

ctable

cross-tabulation table with a list of variable names.

response.names

variable names of response variables. If NULL (default), all variables are regarded as response variables.

plot

split directions for each level of the mosaic:

0 :

no plot,

1 :

horizontal (default),

2 :

alternating directions, beginning with a vertical split.

gray.shade

A logical value indicating whether the gamma-corrected grey palette should be used. If FALSE (default), any color palette is used.

ask

logical; if TRUE (default), the user is asked to confirm before a new page is started. If FALSE, each new plot create a new page.

Details

This function is an R-function style clone of Sakamoto's CATDAP-01 program for categorical data analysis. CATDAP-01 calculates the degree of association between all the possible pairs of categorical variables.

The degree of association is evaluated by AIC value. See help(catdap2) for details about AIC.

catdap2 should be used when the best subset and categorization of explanatory variables are sought for. Continuous explanatory variables could be explanatory variables in case of catdap2.

Value

tway.table

two-way tables and ratio.

total

total number of data with corresponding code of variables.

aic

AIC's of explanatory variables for each response variable.

aic.order

list of explanatory variable numbers arranged in ascending order of AIC.

References

Y.Sakamoto and H.Akaike (1978) Analysis of Cross-Classified Data by AIC. Ann. Inst. Statist. Math., 30, pp.185-197.

K.Katsura and Y.Sakamoto (1980) Computer Science Monograph, No.14, CATDAP, A Categorical Data Analysis Program Package. The Institute of Statistical Mathematics.

Y.Sakamoto, M.Ishiguro and G.Kitagawa (1983) Information Statistics Kyoritsu Shuppan Co., Ltd., Tokyo. (in Japanese)

Y.Sakamoto (1985) Categorical Data Analysis by AIC. Kluwer Academic publishers.

Examples

## example 1 (The Japanese National Character)
data(JNcharacter)
response <- c("born.again", "difficult", "pleasure", "women.job", "money")
catdap1(JNcharacter, response)

# or, simply  
data(JNcharacter)
catdap1(JNcharacter)

## example 2 (Titanic data)
# A data set with 2201 observations on 4 variables (Class, Sex, Age and Survived)
# cross-tabulating data
catdap1c(Titanic, "Survived")

# individual data
x <- data.frame(Titanic)
y <- data.matrix(x)
n <- dim(y)[1]
nc <- dim(y)[2]
z <- array(, dim = c(nc-1, sum(y[, 5])))
k <- 1
for (i in 1:n)
  if (y[i, nc] != 0) {
    np <- y[i, nc]
    for (j in 1:(nc-1))
      z[j, k:(k+np-1)] <- dimnames(Titanic)[[j]][[y[i, j]]]
    k <- k + np
  }
data <- data.frame(aperm(array(z, dim = c(4,2201)), c(2,1)),
                   stringsAsFactors = TRUE)
names(data) <- names(dimnames(Titanic))
catdap1(data, "Survived")