results.inla: The Monte Carlo simualtion results using INLA

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

Description

Data for fig 7 and 8 in Onkelinx et al

Usage

data(results.inla)

Format

A data frame with 87600 rows and 12 variables

Details

• Estimate The point estimate of the API index

• Std. Error The standard error of the API index

• Year The year of the index

• 2.5 % The lower confidence limit of the API index

• 97.5 % The upper confidence limit of the API index

• Run The id of the simulated dataset

• Pattern Pattern of missingness: 0 = missing completely at random (MCAR), 1 = observed

• Missing The proportions of missingness: 0 = observed, 1 = 1%, 2 = 5%, 3 = 25%, 4 = 50%, 5 = 75%

• Model The imputation model: 0 = basic, 1 = true mean, 3 = complex

• Imputations The number ofimputations

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