makePriors: Generate Priors for 'binIRT'

In HJ08003/HJemIRT: EM Algorithms for Estimating Item Response Theory Models

Description

makePriors generates diffuse priors for binIRT.

Usage

  makePriors(.N = 20, .J = 100, .D = 1)

Arguments

.N	integer, number of subjects/legislators to generate priors for.
.J	integer, number of items/bills to generate priors for.
.D	integer, number of dimensions.

Value

• x$mu A (D x D) prior means matrix for respondent ideal points x_i.

• x$sigma A (D x D) prior covariance matrix for respondent ideal points x_i.

• beta$mu A ( D+1 x 1) prior means matrix for α_j and β_j.

• beta$sigma A ( D+1 x D+1 ) prior covariance matrix for α_j and β_j.

Author(s)

Kosuke Imai [e-mail]

James Lo [e-mail]

Jonathan Olmsted [e-mail]

References

Kosuke Imai, James Lo, and Jonathan Olmsted “Fast Estimation of Ideal Points with Massive Data.” Working Paper. Available at http://imai.princeton.edu/research/fastideal.html.

See Also

'binIRT', 'getStarts', 'convertRC'.

Examples

## Data from 109th US Senate
data(s109)

## Convert data and make starts/priors for estimation
rc <- convertRC(s109)
p <- makePriors(rc$n, rc$m, 1)
s <- getStarts(rc$n, rc$m, 1)

## Conduct estimates
lout <- binIRT(.rc = rc,
                .starts = s,
                .priors = p,
                .control = {
                    list(threads = 1,
                         verbose = FALSE,
                         thresh = 1e-6
                         )
                }
                )

## Look at first 10 ideal point estimates
lout$means$x[1:10]

HJ08003/HJemIRT documentation built on May 6, 2019, 9:47 p.m.