predictQAS: Function for categorization after QAS.func

In LisaMaag/QAS: Quasi-analytical solution for logit

Description

predictQAS is used to categorize numeric variables in a dataset the same way as the QAS.func-Function for calculating the coefficients.

Usage

predictQAS(QAS.res, data, same = FALSE)

Arguments

QAS.res	The results of the QAS.func-Function
data	A data frame containing the variables in the model for QAS.func
same	a logical vector which indicates whether prediction of the dependent variable is applied for the same dataset used for QAS.func. Its default is FALSE.

Value

An object of class predictQAS is a list containing at least the following components:

yhat

the predicted values of the dependent variable based on the coefficients of QAS.func and the dataset used for categorization

transformed_data

the dataset with categorized numeric variables

AIC

A version of Akaike's Information Criterion

BIC

A version if Bayesian Information Criterion

Examples

# generate Data
y <- as.integer(c(1,0,0,0,1,1,1,0,0,1))
x <- c(15,88,90,60,24,30,26,57,69,18)
z <- as.integer(c(3,2,2,1,3,3,2,1,1,3))
example_data <- data.frame(y,x,z)

# deploy QAS.func-Function
result1 <- QAS.func(y~x+z, data=example_data, weights=NULL, seed=NULL, tau = NULL)

# deploy predictQas-Function
result2  <- predictQAS(QAS.res = result1, data=example_data)

LisaMaag/QAS documentation built on May 9, 2019, midnight