getStarts | R Documentation |
binIRT
getStarts
generates starting values for binIRT
.
getStarts(.N, .J, .D, .type = "zeros")
.N |
integer, number of subjects/legislators to generate starts for. |
.J |
integer, number of items/bills to generate starts for. |
.D |
integer, number of dimensions. |
.type |
“zeros” and “random” are the only valid types, will generate starts accordingly. |
|
A (J x 1) matrix of starting values for the item difficulty parameter alpha. |
|
A (J x D) matrix of starting values for the item discrimination parameter β. |
|
An (N x D) matrix of starting values for the respondent ideal points x_i. |
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', 'makePriors', 'convertRC'.
## 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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