mirt (version 1.3)

fscores: Methods for Function fscores

Description

Computes MAP, EAP, ML (Embretson & Reise, 2000), EAP for sum-scores (Thissen et al., 1995), or WLE (Warm, 1989) factor scores with a multivariate normal prior distribution using equally spaced quadrature. Additionally, multiple imputation variants are possible for each estimator if a parameter information matrix was computed, which are useful if the sample size/number of items were small.

Usage

fscores(object, rotate = "", full.scores = FALSE, method = "EAP",
  quadpts = NULL, response.pattern = NULL, returnER = FALSE,
  return.acov = FALSE, mean = NULL, cov = NULL, verbose = TRUE,
  scores.only = TRUE, full.scores.SE = FALSE, theta_lim = c(-4, 4),
  MI = 0)

Arguments

object

a computed model object of class ExploratoryClass, ConfirmatoryClass, or MultipleGroupClass

full.scores

if FALSE (default) then a summary table with factor scores for each unique pattern is displayed. Otherwise the original data matrix is returned with the computed factor scores appended to the rightmost column

rotate

rotation declaration to be used when estimating the factor scores. If "" then the object@rotate default value is used (only applicable to ExploratoryClass objects)

method

type of factor score estimation method. Can be expected a-posteriori ("EAP"), Bayes modal ("MAP"), weighted likelihood estimation ("WLE"), maximum likelihood ("ML"), or expected a-posteriori for

quadpts

number of quadratures to use per dimension. If not specified, the value will be determined from the input model object, otherwise a suitable one will be created which decreases as the number of dimensions increases (and therefore for estimates suc

theta_lim

lower and upper range to evaluate latent trait integral for each dimension

mean

a vector for custom latent variable means. If NULL, the default for 'group' values from the computed mirt object will be used

cov

a custom matrix of the latent variable covariance matrix. If NULL, the default for 'group' values from the computed mirt object will be used

MI

a number indicating how many multiple imputation draws to perform. Default is 0, indicating that no MI draws will be performed

response.pattern

an optional argument used to calculate the factor scores and standard errors for a given response vector or matrix

returnER

logical; return empirical reliability (also known as marginal reliability) estimates as a numeric values?

return.acov

logical; return a list containing covariance matricies instead of factors scores? impute = TRUE not supported with this option

full.scores.SE

logical; when full.scores == TRUE, also return the standard errors associated with each respondent? Default is FALSE

verbose

logical; print verbose output messages?

scores.only

logical; return only the factor scores (only applicable when full.scores = TRUE)

Details

The function will return either a table with the computed scores and standard errors, the original data matrix with scores appended to the rightmost column, or the scores only. By default the latent means and covariances are determined from the estimated object, though these can be overwritten. Iterative estimation methods can be estimated in parallel to decrease estimation times if a mirtCluster object is available.

References

Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Erlbaum.

Thissen, D., Pommerich, M., Billeaud, K., & Williams, V. S. L. (1995). Item Response Theory for Scores on Tests Including Polytomous Items with Ordered Responses. Applied Psychological Measurement, 19, 39-49

Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54, 427-450.

Examples

mod <- mirt(Science, 1)
tabscores <- fscores(mod)
head(tabscores)
fullscores <- fscores(mod, full.scores = TRUE)
fullscores_with_SE <- fscores(mod, full.scores = TRUE, full.scores.SE=TRUE)
head(fullscores)
head(fullscores_with_SE)

#chage method argument to use MAP estimates
fullscores <- fscores(mod, full.scores = TRUE, method='MAP')
head(fullscores)

#calculate MAP for a given response vector
fscores(mod, method='MAP', response.pattern = c(1,2,3,4))
#or matrix
fscores(mod, method='MAP', response.pattern = rbind(c(1,2,3,4), c(2,2,1,3)))

#use custom latent variable properties (diffuse prior for MAP is very close to ML)
fscores(mod, method='MAP', cov = matrix(1000))
fscores(mod, method='ML')

#WLE estimation, run in parallel using available cores
mirtCluster()
fscores(mod, method='WLE')

#multiple imputation using 30 draws for EAP scores. Requires information matrix
mod <- mirt(Science, 1, SE=TRUE)
fscores(mod, MI = 30)