results.underhill: The Monte Carlo simualtion results using Underhill

In ThierryO/multimput: Using Multiple Imputation to Address Missing Data

Description

Data for fig 3 in Onkelinx et al

Usage

data(results.underhill)

Format

A data frame with 19200 rows and 9 variables

Details

• Estimate The point estimate of the API index

• Std. Error The standard error of the API index

• 2.5 % The lower confidence limit of the API index

• 97.5 % The upper confidence limit of the API index

• Year The year of the index

• Run The id of the simulated dataset

• Pattern Pattern of missingness: 0 = missing completely at random (MCAR)

• Missing The proportions of missingness: 0 = observed

• Method The algorithm: UHM = original Underhill with mean as start, UH0, original Underhill with 0 as start, UAM = altered Underhill with mean as start, UA0 = altered Underhill with 0 as start

ThierryO/multimput documentation built on May 9, 2019, 4:42 p.m.