getStarts: Generate Starts for 'binIRT'
In kosukeimai/emIRT: EM Algorithms for Estimating Item Response Theory Models

Description Usage Arguments Value Author(s) References See Also Examples

getStarts generates starting values for binIRT.

1	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.

`alpha`	A (J x 1) matrix of starting values for the item difficulty parameter alpha.
`beta`	A (J x D) matrix of starting values for the item discrimination parameter β.
`x`	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]