ohiovoters: Voters in Ohio

In divoRce: Diagnosing Separation Phenomena in Categorical Response Models

Voters in Ohio

Description

This is a cleaned-up and anonymized random snapshot (N=6544) of the data set used in Rusch, Lee, Hornik, Jank and Zeileis (2013).

Usage

data(ohiovoters)

Format

A 6544 x 83 data frame including: * gender : Factor w/ 2 levels "F" for female,"M" for male * partyAffiliation : Factor w/ 3 levels "D" for Democrat,"R" for Republican,"U" for unaffiliated/independent * partyMix : Factor w/ 8 levels "unknown" for unkown,"allR" for all are Republican, "allD" all are Democr#' ats, "noneRorD" none are Republican or Democrat, "noneD" none are Democrat, "onlyRorD" only #' Republican or Democrat, "noneR" none are Republican, "allRorDorLegal" all are either Republi#' can or Democrats or any other legal value (e.g., other party affiliation; describes the hous#' ehold composition in terms of party affiliation * householdHead : Factor w/ 2 levels "H" for household head,"M" for household member * householdRank : Factor w/ 3 levels "3+" < "2" < "1" rank in the household * income : Factor w/ 12 levels Level of estimated household income from census or estimated using demog#' raphic and census data. "A" - Under 15,000; "B" - 15,000-24,999; "C" - 25,000-34,999; "D" #' - 35,000-49,999; "E"- 50,000-74,999; "F" - 75,000-99,999; "G" - 100,000-124,999; "H" - 125,0#' 00-149,999; "I" - 150,000-174,999; "J" - 175,000-199,999; "K" - 200,000-249,999; "L"- 250,00#' 0+ * homeOwner : Factor w/ 5 levels "1" renter,"A" renter,"B" probable renter,"C" probable homeowner,"D" home#' owner (note: it is not clear how 1 is different from A) * mailOrder : Factor w/ 2 levels "0" no ,"1" yes; indicates mail order buyer * compOwner : Factor w/ 2 levels "0"no ,"1" yes; owns a computer * educationLevel : Factor w/ 7 levels "A","B","C","D","E","F","G" after ISCED (http://www.unesco.org/education/#' information/nfsunesco/doc/isced_1997.htm; A=0 and G=6) * donateXXX : Factor w/ 2 levels "0" no,"1" yes, donated to cause XXX (arts, politics, children, animals, hea#' lth, poverty, environment, religion) * fec_XX_YY : Factor w/ 2 levels "0" for no,"1" for yes; federal contributor in years XX and YY * dontPhone : Factor w/ 3 levels "N" for no ,"U" for unknown,"Y" for yes; person is on the federal do not call list * ageD : age in days * cnt : number of attended elections * genXX : Factor w/ 2 levels "0" for no,"1" for yes; attended general elections in year XX * priXX : Factor w/ 2 levels "0" for no,"1" for yes; attended primary elections in year XX * othXX : Factor w/ 2 levels "0" for no,"1" for yes; attended other elections in year XX * pppXX : Factor w/ 2 levels "0" for no,"1" for yes; attended presidential primary elections in year XX (is the priXX variable for years with presidential elections) * cntEligible : Number of election the person was eligible to vote * percentAttended : cnt/cntEligible * ageY : age in years (ageD/365.25) * pProb : predicted probabilities from the y~s|e model * pNodes : predicted node number * ageC : Factor w/ 4 levels; age category * incC : Factor w/ 3 levels "< 35k" less than 35,000 USD a year, "35k-75k" 35,001 to 75,000 USD per y#' ear ">75k more than 75,000 USD per year"; income categories * eduC : Factor w/ 3 levels "primary" primary education,"secondary" secondary education,"postsec" pos#' tsecondary eucation; education categories (collapsed educationLevel) * pSegs : Factor w/ 7 levels; predicted segment number

Source

Rusch, Lee, Hornik, Jank, Zeileis (2013). Influencing elections with statistics: Targeting voters with logistic regression trees. The Annals of Applied Statistics, 7(3), pp. 1612-1639. doi: 10.1214/13-AOAS648

divoRce documentation built on April 28, 2026, 3:01 a.m.