hierIRT: Hierarchichal IRT estimation via Variational Inference

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

View source: R/hierIRT.R

Description

hierIRT estimates an hierarchical IRT model with two response categories, allowing the use of covariates to help determine ideal point estimates. Estimation is conducted using the variational EM algorithm described in the reference paper below. A special case of this model occurs when time/session is used as the covariate — this allows legislator ideal points to vary over time with a parametric time trend. Notably, the popular DW-NOMINATE model (Poole and Rosenthal, 1997) is one such example, in which legislator ideal points shift by a constant amount each period, and the error term in the hierarchical model is set to 0. In contrast to other functions in this package, this model does not assume a ‘rectangular’ roll call matrix, and all data are stored in vector form.

Usage

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  hierIRT(.data, .starts = NULL, .priors = NULL, .control = NULL) 

Arguments

.data

a list with the following items:

  • y A (L x 1) matrix of observed votes. ‘1’ and ‘-1’ are the yea and nay codes.

  • i A (L x 1) integer matrix of indexes of the ideal point i[l] linked to each observed vote l = 0 L. Indexes begin at 0 and reach a maximum value of I - 1.

  • j A (L x 1) integer matrix of indexes of the bill/item j[l] linked to each observed vote l = 0 L. Indexes begin at 0 and reach a maximum value of J - 1.

  • g A (I x 1) integer matrix of indexes of the group membership g[i[l]] linked to each ideal point i = 0 I. Indexes begin at 0 and reach a maximum value of G - 1.

  • z A (I x D) numeric matrix of observed covariates. Rows correspond to ideal points i = 0 I. The columns correspond to the D different covariates. Typically, the first column will be an intercept and fixed to 1, while other columns represent ideal point-specific covariates such as session.

.starts

a list containing several matrices of starting values for the parameters. The list should contain the following matrices:

  • alpha A (J x 1) matrix of starting values for the item difficulty parameter α_j.

  • beta A (J x 1) matrix of starting values for the item discrimination parameter β_j.

  • gamma An (I x D) matrix of starting values for the group level coefficients γ_{m}.

  • eta An (I x 1) matrix of starting values for the ideal point error term η_n.

  • sigma An (G x 1) matrix of starting values for the group level variance parameter σ^2_m.

.priors

list, containing several matrices of starting values for the parameters. The list should contain the following matrices:

  • gamma.mu A (D x 1) prior means matrix for all group level coefficients γ_{m}.

  • gamma.sigma A (D x D) prior covariance matrix for all group level coefficients γ_{m}.

  • beta.mu A (2 x 1) prior means matrix for all bill parameters α_j and β_j.

  • beta.sigma A (2 x 2) prior covariance matrix for all bill parameters α_j and β_j.

  • sigma.v A (1 x 1) matrix containing the group level variance prior parameter ν_{σ}.

  • sigma.s A (1 x 1) matrix containing the group level variance prior parameter s^2_{σ}.

.control

list, specifying some control functions for estimation. Options include the following:

  • threads integer, indicating number of cores to use. Default is to use a single core, but more can be supported if more speed is desired.

  • verbose boolean, indicating whether output during estimation should be verbose or not. Set FALSE by default.

  • thresh numeric. Algorithm will run until all parameters correlate at 1 - thresh across consecutive iterations. Set at 1e-6 by default.

  • maxit integer. Sets the maximum number of iterations the algorithm can run. Set at 500 by default.

  • checkfreq integer. Sets frequency of verbose output by number of iterations. Set at 50 by default.

Value

An object of class hierIRT.

means

list, containing several matrices of point estimates for the parameters corresponding to the inputs for the priors. The list should contain the following matrices.

  • alpha A (J x 1) matrix of point estimates for the item difficulty parameter α_j.

  • beta A (J x 1) matrix of point estimates for the item discrimination parameter β_j.

  • gamma An (I x D) matrix of point estimates for the group level coefficients γ_{m}.

  • eta An (I x 1) matrix of point estimates for the ideal point error term η_n.

  • sigma An (G x 1) matrix of point estimates for the group level variance parameter σ^2_m.

  • x_implied An (I x 1) matrix of the implied ideal point x_i, calculated as a function of gamma, z, and eta using the point estimates for those parameters.

vars

list, containing several matrices of variance estimates for several parameters of interest for diagnostic purposes. Note that these variances are those recovered via variational approximation, and in most cases they are known to be far too small and generally unusable. The list should contain the following matrices:

  • eta A (I x 1) matrix of variance estimates for the ideal point noise parameter η_n.

  • gamma A (G x D x D) cube of covariance estimates for the gamma parameters for each group. Each of the G items is a matrix with a single covariance matrix for the m-th group's D gamma parameters.

  • beta2 A (J x 2 x 2) cube of covariance estimates for the item parameters α_j and β_j. Each of the J items is a matrix with a single covariance matrix for the j-th item.

runtime

A list of fit results, with elements listed as follows:

N

A list of counts of various items:

call

Function call used to generate output.

Author(s)

Kosuke Imai [email protected]

James Lo [email protected]

Jonathan Olmsted [email protected]

References

Variational model is described in 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.

See Also

'dwnom'.

Examples

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### Real data example of US Senate 80-110 (not run)
### Based on voteview.com example of DW-NOMINATE (ftp://voteview.com/dw-nominate.htm)
### We estimate a hierarchical model without noise and a linear time covariate
### This model corresponds very closely to the DW-NOMINATE model

## Not run: 
data(dwnom)

## This takes about 10 minutes to run on 8 threads
## You may need to reduce threads depending on what your machine can support
lout <- hierIRT(.data = dwnom$data.in,
                    .starts = dwnom$cur,
                    .priors = dwnom$priors,
                    .control = {list(
                    threads = 8,
                    verbose = TRUE,
                    thresh = 1e-4,
				    maxit=200,
				    checkfreq=1
                        )})

## Bind ideal point estimates back to legislator data
final <- cbind(dwnom$legis, idealpt.hier=lout$means$x_implied)

## These are estimates from DW-NOMINATE as given on the Voteview example
## From file "SL80110C21.DAT"
nomres <- dwnom$nomres

## Merge the DW-NOMINATE estimates to model results by legislator ID
## Check correlation between hierIRT() and DW-NOMINATE scores
res <- merge(final, nomres, by=c("senate","id"),all.x=TRUE,all.y=FALSE)
cor(res$idealpt.hier, res$dwnom1d)


## End(Not run)

emIRT documentation built on May 29, 2017, 7:10 p.m.