# Categorical Data Analysis Program Package 01 Calculates the degree of association between all the possible pairs of categorical variables. ```r 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. ## Returns - **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 ```r ## 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") ```

catdap1 function