makePriors {emIRT}

Generate Priors for binIRT

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 \alpha_j and \beta_j.

beta$sigma

A ( D+1 x D+1 ) prior covariance matrix for \alpha_j and \beta_j.

Author(s)

Kosuke Imai [e-mail]

James Lo [e-mail]

Jonathan Olmsted [e-mail]

References

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

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]