catdap2 {catdap} | R Documentation |
Categorical Data Analysis Program Package 02
Description
Search for the best single explanatory variable and detect the best subset of explanatory variables.
Usage
catdap2(data, pool = NULL, response.name, accuracy = NULL, nvar = NULL,
additional.output = NULL, missingmark = NULL, pa1 = 1, pa2 = 4, pa3 = 10,
print.level = 0, plot = 1, gray.shade = FALSE)
Arguments
data |
data matrix with variable names on the first row. |
pool |
the ways of pooling to categorize each variable must be specified by integer parameters:
|
response.name |
variable name of the response variable. |
accuracy |
minimum width for the discretization for each variable. |
nvar |
number of variables to be retained for the analysis of
multidimensional tables. Default is the number of variables in |
additional.output |
list of sets of explanatory variable names for additional output. |
missingmark |
positive number for handling missing value. See 'Details'. |
pa1 , pa2 , pa3 |
control parameter for size of the working area. If error message is output, please change the value of parameter according to it. |
print.level |
this argument determines the level of output printing. The
default value of ' |
plot |
split directions of the mosaic plot for single explanatory models and minimum AIC model:
|
gray.shade |
A logical value indicating whether the gamma-corrected grey
palette should be used. If |
Details
This function is an R-function style clone of Sakamoto's CATDAP-02 program for categorical data analysis. CATDAP-02 can be used to search for the best subset of explanatory variables which have the most effective information on a specified response variable. Continuous explanatory variables could be explanatory variables. In that case CATDAP-02 searches for optimal categorization of continuous values.
The basic statistic adopted is obtained by the application of the statistic AIC to the models.
denotes the response variable and
denotes candidate
explanatory variable, and their cell frequencies by
and
. The cross frequency is denoted by
. To measure
the strength of dependence of a specific set of response variables
on
the explanatory variable
, we use the following statistic:
where and
denote the total number of categories of the
corresponding sets of variables, respectively.
The selection of the best subset of explanatory variables is realized by the
search for which gives the minimum
.
In case of , the formula (1) reduces to
Here it is assumed that and
.
Sakamoto's original CATDAP outputs as the AIC
value instead of
. By this way the positive value of AIC
indicates that the variable
is judged to be useless as the explanatory
variable of the
.
On the other hand, this policy make impossible to compare the goodness of the CATDAP model with other models, logit models for example.
Considering the convenience of users, present "R version CATDAP" provides not
only , but
, either. The
latter value is given as base_AIC in the output.
Users could recover by adding AIC and base_AIC.
missingmark
enables missing value handling.
When a positive values, say , is set here, any value, say
,
greater than or equal to
is treated as a missing value. If
,
is treated as a missing
value of the 1st type. If
,
is treated as a missing value of the 2nd type, and so on. Generally speaking,
any
that
is
treated as the
-th type missing value. Users are referred to the
reference for the technical details of the missing value handling procedure.
For continuous variables, we assume that
are boundary values
of
bins. Output value ranges
are
defined as follows :
Specifically, for continuous response variable ,
where and
are the minimum and the
maximums of variable V respectively and
.
Value
tway.table |
two-way tables. |
total |
total number of data with corresponding code of variables. |
interval |
class interval for continuous and discrete explanatory variables. |
base.aic |
base_AIC. |
aic |
AIC's of single explanatory variables. |
aic.order |
list of explanatory variable numbers arranged in ascending order of AIC. |
nsub |
number of subsets of explanatory variables. |
subset |
list of subsets of explanatory variables in ascending order of AIC with the following components:
|
ctable |
list of contingency table constructed by the best subset and additional
subsets if any variables is specified by
|
missing |
number of types of the missing values for each variable. |
References
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 (1985) Model Analysis of Categorical Data. Kyoritsu Shuppan Co., Ltd., Tokyo. (in Japanese)
Y.Sakamoto (1985) Categorical Data Analysis by AIC. Kluwer Academic publishers.
An AIC-based Tool for Data Visualization (2015), NTT DATA Mathematical Systems Inc. (in Japanese)
Examples
# Example 1 (medical data "HealthData")
# as additional output, contingency tables for explanatory variable sets
# c("aortic.wav","min.press") and c("ecg","age") are obtained.
data(HealthData)
catdap2(HealthData, c(2, 2, 2, 0, 0, 0, 0, 2), "symptoms",
c(0., 0., 0., 1., 1., 1., 0.1, 0.), ,
list(c("aortic.wav", "min.press"), c("ecg", "age")))
# Example 2 (Edgar Anderson's Iris Data)
# continuous response variable handling and the usage of Barplot2WayTable
# function to visualize the result in shape of stacked histogram.
data(iris)
resvar <- "Petal.Width"
z <- catdap2(iris, c(0, 0, 0, -7, 2), resvar, c(0.1, 0.1, 0.1, 0.1, 0))
z
exvar <- c("Sepal.Length", "Petal.Length")
Barplot2WayTable(z, exvar)
# Example 3 (in the case of a large number of variables)
data(HelloGoodbye)
pool <- rep(2, 56)
## using the default values of parameters pa1, pa2, pa3
## catdap2(HelloGoodbye, pool, "Isay", nvar = 10, print.level = 1, plot = 0)
## Error : Working area for contingency table is too short, try pa1 = 12.
### According to the error message, set the parameter p1 at 12, then ..
catdap2(HelloGoodbye, pool, "Isay", nvar = 10, pa1 = 12, print.level = 1,
plot = 0)
# Example 4 (HealthData with missing values)
data(MissingHealthData)
catdap2(MissingHealthData, c(2, 2, 2, 0, 0, 0, 0, 2), "symptoms",
c(0., 0., 0., 1., 1., 1., 0.1, 0.), missingmark = 300)