assets_fullset | R Documentation |
This data set contains simulated household survey assets data including binary and multi-level categorical assets variables for use with function 'EC_vars'.
data(assets_fullset)
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
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'.
This data set was simulated by the package authors to demonstrate the functionality of the 'EconomicClusters' package.
EC_vars
#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'.
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