LogisticReg: Logistic Regression (LR) Imputation

In TestDataImputation: Missing Item Responses Imputation for Test and Assessment Data

Description

This function imputes for all missing responses using logistic regression (for binary responses) or polytomous regression (for polytomous responses). The mice () function with default settings from the mice package (Van Buuren & Groothuis-Oudshoorn, 2011 <doi: 10.18637/jss.v045.i03>) is used to impute for the missing responses.

Usage

LogisticReg(test.data, Mvalue = "NA", max.score = 1)

Arguments

test.data	Test data set (a data frame or a matrix) containing missing responses. Missing values are coded as NA or other values (e.g., 8, 9).
Mvalue	Missing response indicators in the data (e.g. "NA", "8", "9", etc.). Mvalue="NA" by default.
max.score	The max possible response value in test data. By default max.score=1 (i.e.,binary test data).

Value

A data frame with all missing responses replaced by integrated imputed values.

References

Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). "mice: Multivariate imputation by chained equations in R." Journal of statistical software, 45(1), 1-67. DOI: 10.18637/jss.v045.i03.

Examples

 
        LogisticReg(test.data, Mvalue="NA",max.score=1)

TestDataImputation documentation built on Oct. 19, 2021, 1:07 a.m.