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

## Description

`getStarts` generates starting values for `binIRT`.

## Usage

 `1` ``` getStarts(.N, .J, .D, .type = "zeros") ```

## Arguments

 `.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.

## Value

 `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.

## Author(s)

Kosuke Imai [email protected]

James Lo [email protected]

Jonathan Olmsted [email protected]

## 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.

 ``` 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] ```