A simulated dataset to test the functions of the OTrecod package

The first 300 rows belong to the database A, while the next 400 rows belong to the database B. Five covariates: Gender, Treatment, Dosage, Smoking and Age are common to both databases (same encodings). Gender is the only complete covariate. The variables Yb1 and Yb2 are the target variables of A and B respectively, summarizing a same information encoded in two different scales. that summarize a same information saved in two distinct encodings, that is why, Yb1 is missing in the database B and Yb2 is missing in the database A.

Usage

simu_data

Format

A data.frame made of 2 overlayed databases (A and B) with 700 observations on the following 8 variables.

DB

the database identifier, a character with 2 possible classes: A or B

Yb1

the target variable of the database A, stored as factor and encoded in 3 ordered levels: [20-40], [40-60[,[60-80] (the values related to the database B are missing)

Yb2

the target variable of the database B, stored as integer (an unknown scale from 1 to 5) in the database B (the values related to A are missing)

Gender

a factor with 2 levels (Female or Male) and no missing values

Treatment

a covariate of 3 classes stored as a character with 2% of missing values: Placebo, Trt A, Trt B

Dosage

a factor with 4 levels and 5% of missing values: from Dos 1 to dos 4

Smoking

a covariate of 2 classes stored as a character and 10% of missing values: NO for non smoker, YES otherwise

Age

a numeric corresponding to the age of participants in years. This variable counts 5% of missing values

Source

randomly generated

Details

The purpose of the functions contained in this package is to predict the missing information on Yb1 and Yb2 in database A and database B using the Optimal Transportation Theory.

Missing information has been simulated to some covariates following a simple MCAR process.