assets_fullset: Simulated Household Survey Assets Data - Full Set

In Lauren-Eyler/EconomicClusters: Population-Specific Economic Groups Defined by Few Asset Variables

Simulated Household Survey Assets Data - Full Set

Description

This data set contains simulated household survey assets data including binary and multi-level categorical assets variables for use with function 'EC_vars'.

Usage

data(assets_fullset)

Format

A data frame with 100 observations on the following 13 variables coded as factors.

V1

a binary 0/1 variable with probability 0.05 of value 1

V2

a binary 0/1 variable with probability 0.4 of value 1

V3

a binary 0/1 variable with probability 0.05 of value 1

V4

a binary 0/1 variable with probability 0.05 of value 1

V5

a binary 0/1 variable with probability 0.6 of value 1

V6

a binary 0/1 variable with probability 0.8 of value 1

V7

a categorical variable with the following probabilities: p(V7=1)=0.4, p(V7=2)=0.3, p(V7=3)=0.2, p(V7=4)=0.1

V8

a categorical variable with the following probabilities: p(V8=1)=0.4, p(V8=2)=0.3, p(V8=3)=0.2, p(V8=4)=0.1

V9

a categorical variable with the following probabilities: p(V9=1)=0.4, p(V9=2)=0.3, p(V9=3)=0.2, p(V9=4)=0.1. V9 is highly correlated to V11 (correlation coefficient=0.95)

V10

a categorical variable with the following probabilities: p(V10=1)=0.4, p(V10=2)=0.3, p(V10=3)=0.2, p(V10=4)=0.1

V11

a categorical variable with the following probabilities: p(V11=1)=0.4, p(V11=2)=0.3, p(V11=3)=0.2, p(V11=4)=0.1. V11 is highly correlated to V9 (correlation coefficient=0.95)

V12

a categorical variable with the following probabilities: p(V12=1)=0.4, p(V12=2)=0.3, p(V12=3)=0.2, p(V12=4)=0.1

V13

a categorical variable with the following probabilities: p(V13=1)=0.4, p(V13=2)=0.3, p(V13=3)=0.2, p(V13=4)=0.1

Details

This data set was simulated in a format similar to assets data collected in a large-scale household survey. Such data sets generally include binary variables (e.g. does your household own a cell phone?) and multi-level categorical variables (e.g. what type of water source does your household use?). In 'assets_fullset', each row represents a household, and each household's responses to the assets questions are coded as factors. Binary variables were generated using function 'rbinom' with varying probabilities. Multi-level categorical variables were generated using function 'ordsample' from package 'GenOrd'.

Source

This data set was simulated by the package authors to demonstrate the functionality of the 'EconomicClusters' package.

See Also

EC_vars

Examples

#Let's say our household survey dataset has 13 asset variables.
#We want to ask 5 questions to determine patient economic status in our trauma registry.
#If we include all 13 variables, we will have 1,287 possible combinations to assess!
#We will use EC_vars to narrow down the variables we consider to only relatively common assets.
#Note: The first asset variable with >2 levels is listed in Column 7 
#of our data set 'assets_fullset'.

data(assets_fullset)
assets<-EC_vars(assets_fullset, 0.10)

#By selecting assets owned by at least 10 percent of the population, 
#we now have 10 variables and 252 possible combinations. 
#We can determine whether or not this number of combinations 
#will result in a reasonable computing time using function 'EC_time'.

Lauren-Eyler/EconomicClusters documentation built on March 22, 2022, 1:21 a.m.