oc: Optimal Classification Roll Call Scaling

In oc: Optimal Classification Roll Call Analysis Software

Optimal Classification Roll Call Scaling

Description

oc is the function that takes a rollcall object and estimates nonmetric Optimal Classification scores with them.

Usage

oc(rcObject, dims=2, minvotes=20, lop=0.025, polarity, verbose=FALSE)

Arguments

rcObject	An object of class rollcall, from Simon Jackman's pscl package.
dims	integer, number of dimensions to estimate. Must be nonnegative and cannot exceed 10 dimensions.
minvotes	minimum number of votes a legislator must vote in for them to be analyzed.
lop	A proportion between 0 and 1, the cut-off used for excluding lopsided votes, expressed as the proportion of non-missing votes on the minority side. The default, lop=0.025, eliminates votes where the minority is smaller than 2.5 overwrites the lopsided attribute in the RC object inputted.
polarity	a vector specifying the legislator in the data set who is conservative on each dimension. For example, c(3,5) indicates legislator 3 is conservative on dimension 1, and legislator 5 is conservative on dimension 2. Alternatively, polarity can be specified as a string for legislator names found in legis.names (ie. c("Bush", "Gore")) if every legislative name in the data set is unique. Finally, polarity can be specified as a list (ie. list("cd",c(4,5))) where the first list item is a variable from the roll call object's legis.data, and the second list item is a conservative legislator on each dimension as specified by the first list item. list("cd",c(4,5)) thus specifies the legislators with congressional district numbers of 4 and 5.
verbose	logical, indicates whether bills and legislators to be deleted should be printed while data is being checked before ideal points are estimated.

Value

An object of class OCobject, with elements as follows:

legislators	data frame, containing all data from the old perf25.dat file about legislators. For a typical ocObject run with an ORD file read using readKH, it will contain the following: stateState name of legislator. icpsrStateICPSR state code of legislator. cdCongressional District number. icpsrLegisICPSR code of legislator. partyParty of legislator. partyCodeICPSR party code of legislator. rankRank ordering of legislator on the first dimension, from lowest to highest. correctYeaPredicted Yeas and Actual Yeas. wrongYeaPredicted Yeas and Actual Nays. wrongNayPredicted Nays and Actual Yeas. correctNayPredicted Nays and Actual Nays. volumeMeasure of the legislator's polytope size. coord1DFirst dimension OC score, with all subsequent dimensions numbered similarly.
rollcalls	data frame, containing all data from the old perf21.dat file about bills. For a typical OCobject object run with an ORD file read using readKH, it will contain the following: correctYeaPredicted Yeas and Actual Yeas. wrongYeaPredicted Yeas and Actual Nays. wrongNayPredicted Nays and Actual Yeas. correctNayPredicted Nays and Actual Nays. PREProportional Reduction In Error. normvector1DFirst dimension of the unit normal vector, with all subsequent dimensions numbered similarly. midpointsThe projection of the normal vector needed to get the midpoint.
dimensions	integer, number of dimensions estimated.
eigenvalues	A vector of roll call eigenvalues.
fits	A vector of length 2 with the classic measures of fit, containing the percent correct classification and the APRE.

Author(s)

Keith Poole [e-mail]

Jeffrey Lewis [e-mail]

James Lo [e-mail]

Royce Carroll [e-mail]

References

Keith Poole. 2000. 'Non-parametric Unfolding of Binary Choice Data.' Political Analysis, 8(3):211-237

Keith Poole. 2005. 'Spatial Models of Parliamentary Voting.' Cambridge: Cambridge University Press.

Keith Poole. https://legacy.voteview.com/

See Also

'plot.OCobject','summary.OCobject'.

Examples

    #This data file is the same as reading file using:
    ## Not run: 
    sen90 <- readKH("https://voteview.com/static/data/out/votes/S090_votes.ord")
    
## End(Not run)
    #All ORD files can be found on voteview.com
    data(sen90)
    summary(sen90)

	#Small executable example
	sen90.1d <- oc(sen90,dims=1,polarity=c(7))

    #Output file identical to one produced by command below
    ## Not run: 
    sen90oc <- oc(sen90,dims=2,polarity=c(7,2))
	
## End(Not run)
    data(sen90oc)
    summary(sen90oc)
    plot(sen90oc) 

oc documentation built on Feb. 16, 2023, 8:17 p.m.