dwnom {emIRT}

Poole-Rosenthal DW-NOMINATE data and scores, 80-110 U.S. Senate

Description

This data set contains materials related to the Poole-Rosenthal DW-NOMINATE measure of senator ideology. The software (and other materials) is available at https://voteview.com/, which includes a simpler example of an application to the 80-110 U.S. Senate. The data set here is derived from the data and estimates from that example, but are formatted to be run in hierIRT(). In particular, start values for estimation are identical to those provided by the example.

Usage

data(dwnom)

Format

list, containing the following elements:

data.in

Legislator voting data, formatted for input to hierIRT().

cur

Start values, formatted for input to hierIRT().

priors

Priors, formatted for input to hierIRT().

legis

data frame, containing contextual information about the legislators estimated.

nomres

data frame, containing estimates from DW-NOMINATE on the same data. These are read from the file SL80110C21.DAT.

References

DW-NOMINATE is described in Keith T. Poole and Howard Rosenthal. 1997. Congress: A Political Economic History of Roll Call Voting. Oxford University Press. See also https://voteview.com/.

Variational model is described in Kosuke Imai, James Lo, and Jonathan Olmsted (2016). “Fast Estimation of Ideal Points with Massive Data.” American Political Science Review, Vol. 110, No. 4 (December), pp. 631-656.

See Also

'hierIRT'.

Examples

### Real data example of US Senate 80-110 (not run)
### Based on voteview.com example of DW-NOMINATE 
### We estimate a hierarchical model without noise and a linear time covariate
### This model corresponds very closely to the DW-NOMINATE model

## Not run: 
data(dwnom)

## This takes about 10 minutes to run on 8 threads
## You may need to reduce threads depending on what your machine can support
lout <- hierIRT(.data = dwnom$data.in,
                    .starts = dwnom$cur,
                    .priors = dwnom$priors,
                    .control = {list(
                    threads = 8,
                    verbose = TRUE,
                    thresh = 1e-4,
				    maxit=200,
				    checkfreq=1
                        )})

## Bind ideal point estimates back to legislator data
final <- cbind(dwnom$legis, idealpt.hier=lout$means$x_implied)

## These are estimates from DW-NOMINATE as given on the Voteview example
## From file "SL80110C21.DAT"
nomres <- dwnom$nomres

## Merge the DW-NOMINATE estimates to model results by legislator ID
## Check correlation between hierIRT() and DW-NOMINATE scores
res <- merge(final, nomres, by=c("senate","id"),all.x=TRUE,all.y=FALSE)
cor(res$idealpt.hier, res$dwnom1d)


## End(Not run)