gnm (version 1.1-5)

House2001: Data on twenty roll calls in the US House of Representatives, 2001

Description

The voting record of every representative in the 2001 House, on 20 roll calls selected by Americans for Democratic Action. Each row is the record of one representative; the first column records the representative's registered party allegiance.

Usage

House2001

Format

A data frame with 439 observations on the following 21 variables.

party

a factor with levels D I N R

HR333.BankruptcyOverhaul.Yes

a numeric vector

SJRes6.ErgonomicsRuleDisapproval.No

a numeric vector

HR3.IncomeTaxReduction.No

a numeric vector

HR6.MarriageTaxReduction.Yes

a numeric vector

HR8.EstateTaxRelief.Yes

a numeric vector

HR503.FetalProtection.No

a numeric vector

HR1.SchoolVouchers.No

a numeric vector

HR1836.TaxCutReconciliationBill.No

a numeric vector

HR2356.CampaignFinanceReform.No

a numeric vector

HJRes36.FlagDesecration.No

a numeric vector

HR7.FaithBasedInitiative.Yes

a numeric vector

HJRes50.ChinaNormalizedTradeRelations.Yes

a numeric vector

HR4.ANWRDrillingBan.Yes

a numeric vector

HR2563.PatientsRightsHMOLiability.No

a numeric vector

HR2563.PatientsBillOfRights.No

a numeric vector

HR2944.DomesticPartnerBenefits.No

a numeric vector

HR2586.USMilitaryPersonnelOverseasAbortions.Yes

a numeric vector

HR2975.AntiTerrorismAuthority.No

a numeric vector

HR3090.EconomicStimulus.No

a numeric vector

HR3000.TradePromotionAuthorityFastTrack.No

a numeric vector

Details

Coding of the votes is as described in ADA (2002).

References

Americans for Democratic Action, ADA (2002). 2001 voting record: Shattered promise of liberal progress. ADA Today 57(1), 1--17.

deLeeuw, J (2006). Principal component analysis of binary data by iterated singular value decomposition. Computational Statistics and Data Analysis 50, 21--39.

Examples

if (FALSE) {
## This example takes some time to run!
summary(House2001)
## Put the votes in a matrix, and discard members with too many NAs etc:
House2001m <- as.matrix(House2001[-1])
informative <- apply(House2001m, 1, function(row){
    valid <- !is.na(row)
    validSum <- if (any(valid)) sum(row[valid]) else 0
    nValid <- sum(valid)
    uninformative <- (validSum == nValid) || (validSum == 0) || (nValid < 10)
    !uninformative})
House2001m <- House2001m[informative, ]
## Make a vector of colours, blue for Republican and red for Democrat:
parties <- House2001$party[informative]
partyColors <- rep("black", length(parties))
partyColors <- ifelse(parties == "D", "red", partyColors)
partyColors <- ifelse(parties == "R", "blue", partyColors)
## Expand the data for statistical modelling:
House2001v <- as.vector(House2001m)
House2001f <- data.frame(member = rownames(House2001m),
                         party = parties,
                         rollCall = factor(rep((1:20),
                                           rep(nrow(House2001m), 20))),
                         vote = House2001v)
## Now fit an "empty" model, in which all members vote identically:
baseModel <- glm(vote ~ -1 + rollCall, family = binomial, data = House2001f)
## From this, get starting values for a one-dimensional multiplicative term:
Start <- residSVD(baseModel, rollCall, member)
##
## Now fit the logistic model with one multiplicative term.
## For the response variable, instead of vote=0,1 we use 0.03 and 0.97,
## corresponding approximately to a bias-reducing adjustment of p/(2n),
## where p is the number of parameters and n the number of observations.
##
voteAdj <- 0.5 + 0.94*(House2001f$vote - 0.5)
House2001model1 <- gnm(voteAdj ~ Mult(rollCall, member),
              eliminate = rollCall,
              family = binomial, data = House2001f,
              na.action = na.exclude, trace = TRUE, tolerance = 1e-03,
              start = -Start)
## Deviance is 2234.847, df = 5574
##
## Plot the members' positions as estimated in the model:
##
memberParameters <- pickCoef(House2001model1, "member")
plot(coef(House2001model1)[memberParameters], col = partyColors,
     xlab = "Alphabetical index (Abercrombie 1 to Young 301)",
     ylab = "Member's relative position, one-dimensional model")
## Can do the same thing with two dimensions, but gnm takes around 40
## slow iterations to converge (there are more than 600 parameters): 
Start2 <- residSVD(baseModel, rollCall, member, d = 2)
House2001model2 <- gnm(
              voteAdj ~ instances(Mult(rollCall - 1, member - 1), 2),
              eliminate = rollCall,
              family = binomial, data = House2001f,
              na.action = na.exclude, trace = TRUE, tolerance = 1e-03,
              start = Start2, lsMethod = "qr")
## Deviance is 1545.166, df = 5257
##
memberParameters1 <- pickCoef(House2001model2, "1).member")
memberParameters2 <- pickCoef(House2001model2, "2).member")
plot(coef(House2001model2)[memberParameters1],
     coef(House2001model2)[memberParameters2],
     col = partyColors,
     xlab = "Dimension 1",
     ylab = "Dimension 2",
     main = "House2001 data: Member positions, 2-dimensional model")
##
## The second dimension is mainly due to rollCall 12, which does not
## correlate well with the rest -- look at the coefficients of
## House2001model1, or at the 12th row of
cormat <- cor(na.omit(House2001m))
}