computeEVs: Compute expected values for each observation

In saharaja/imputeORS: Impute Missing Values in the BLS Occupational Requirements Survey

Description

To compare occupations across requirements, we developed an expected value measure we called the "Expected Level of Effort" (ELE). This measure is a weighted average of the frequency and intensity times the population estimate for the various requirements. A low frequency/low intensity/low population estimate results in a low level of effort, and the converse for high.

Usage

computeEVs(blended.results)

Arguments

blended.results

Blended predictions from imputation models, calculated at convergence iterations and blending proportions computed by computeBlendingRatio() (output of blendImputations())

Details

For each occupational group, we calculate ELE as an expected value of frequency times intensity as follows, where μ_j is the mean population prediction across all the simulations for the jth observation, and F_j and I_j are the frequency and intensity of the jth observation, respectively:

E=∑(μ_j * F_j * I_j)

Value

A data frame containing ELEs of each occupational group, arranged with requirement (additive groups) as rows and occupation as columns

See Also

blendImputations()

saharaja/imputeORS documentation built on Feb. 4, 2022, 12:27 a.m.