LogisticReg

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).

test.data

Missing response indicators in the data (e.g. "NA", "8", "9", etc.). Mvalue="NA" by default.

Mvalue

The max possible response value in test data. By default max.score=1 (i.e.,binary test data).

max.score

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 &amp; Groothuis-Oudshoorn, 2011 &lt;doi: 10.18637/jss.v045.i03&gt;) is used 
to impute for the missing responses.

Functions for imputing missing item responses for dichotomous and
polytomous test and assessment data. This package enables missing imputation
methods that are suitable for test and assessment data, including:
listwise (LW) deletion (see De Ayala et al. 2001 <doi:10.1111/j.1745-3984.2001.tb01124.x>),
treating as incorrect (IN, see Lord, 1974 <doi: 10.1111/j.1745-3984.1974.tb00996.x>;
Mislevy & Wu, 1996 <doi: 10.1002/j.2333-8504.1996.tb01708.x>;
Pohl et al., 2014 <doi: 10.1177/0013164413504926>), person mean imputation (PM),
item mean imputation (IM), two-way (TW) and response function (RF) imputation,
(see Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>),
logistic regression (LR) imputation, predictive mean matching (PMM), and expectation<e2><80><93>maximization (EM)
imputation (see Finch, 2008 <doi: 10.1111/j.1745-3984.2008.00062.x>).

LogisticReg: Logistic Regression (LR) Imputation

Description

Usage

Arguments

Value

References

Examples