Description Usage Arguments Value Author(s) References See Also Examples
makePriors
generates diffuse priors for binIRT
.
1 | makePriors(.N = 20, .J = 100, .D = 1)
|
.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. |
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.
Kosuke Imai kimai@princeton.edu
James Lo jameslo@princeton.edu
Jonathan Olmsted jpolmsted@gmail.com
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.
'binIRT', 'getStarts', 'convertRC'.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## 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]
|
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