Package LNIRT. R topics documented: November 14, 2018
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1 Package LNIRT November 14, 2018 Type Package Title LogNormal Response Time Item Response Theory Models Version Author Jean-Paul Fox, Konrad Klotzke, Rinke Klein Entink Maintainer Konrad Klotzke Imports MASS, stats, utils Allows the simultaneous analysis of responses and response times in an Item Response Theory (IRT) modelling framework. Supports covariates for item and person (random) parameters. Parameter estimation is done with a MCMC algorithm. LNIRT replaces the package CIRT, which was written by Rinke Klein Entink. For reference, see the paper by Fox, Klein Entink and Van der Linden (2007), ``Modeling of Responses and Response Times with the Package cirt'', Journal of Statistical Software, <doi: /jss.v020.i07>. License GPL-2 GPL-3 LazyData TRUE RoxygenNote NeedsCompilation no Repository CRAN Date/Publication :50:03 UTC R topics documented: LNIRT LNRT simlnirt Index 5 1
2 2 LNIRT LNIRT Log-normal response time IRT modelling Log-normal response time IRT modelling LNIRT(RT, Y, data, XG = 1000, guess = FALSE, par1 = FALSE, residual = FALSE, = TRUE, = FALSE, alpha, beta, XPA = NULL, XPT = NULL, XIA = NULL, XIT = NULL) RT Y data a Person-x-Item matrix of log-response times (time spent on solving an item). a Person-x-Item matrix of responses. either a list or a simlnirt object containing the response time and response matrices and optionally the predictors for the item and person parameters. If a simlnirt object is provided, in the summary the simulated item and time parameters are shown alongside of the estimates. If the required variables cannot be found in the list, or if no data object is given, then the variables are taken from the environment from which LNIRT is called. XG the number of MCMC iterations to perform (default: 1000). guess par1 residual alpha beta XPA XPT XIA XIT include guessing parameters in the IRT model use alternative parameterization compute residuals, requires > 1000 iterations estimate the time-discrimination parameter(default: true). an optional vector of pre-defined item-discrimination parameters. an optional vector of pre-defined item-difficulty parameters. an optional matrix of predictors for the person ability parameters. an optional matrix of predictors for the person speed parameters. an optional matrix of predictors for the item-difficulty parameters. an optional matrix of predictors for the item time intensity parameters. an object of class LNIRT.
3 LNRT 3 Examples ## Not run: # Log-normal response time IRT modelling data <- simlnirt(n = 500, K = 20, rho = 0.8, = FALSE) out <- LNIRT(RT = RT, Y = Y, data = data, XG = 1500, residual = TRUE, = FALSE) summary(out) # Print results out$post.means$item.difficulty # Extract posterior mean estimates library(coda) mcmc.object <- as.mcmc(out$mcmc.samples$item.difficulty) # Extract MCMC samples for coda summary(mcmc.object) plot(mcmc.object) ## End(Not run) LNRT Log-normal response time modelling Log-normal response time modelling LNRT(RT, data, XG = 1000, residual = FALSE, = TRUE, = FALSE, XPT = NULL, XIT = NULL) RT data a Person-x-Item matrix of log-response times (time spent on solving an item). either a list or a simlnirt object containing the response time matrix. If a simlnirt object is provided, in the summary the simulated time parameters are shown alongside of the estimates. If the RT variable cannot be found in the list, or if no data object is given, then the RT variable is taken from the environment from which LNRT is called. XG the number of MCMC iterations to perform (default: 1000). residual XPT XIT an object of class LNRT. compute residuals, requires > 1000 iterations estimate the time-discrimination parameter (default: true). an optional matrix of predictors for the person speed parameters. an optional matrix of predictors for the item time intensity parameters.
4 4 simlnirt Examples ## Not run: # Log-normal response time modelling data <- simlnirt(n = 500, K = 20, rho = 0.8, = FALSE) out <- LNRT(RT = RT, data = data, XG = 1500, residual = TRUE, = TRUE, = FALSE) summary(out) # Print results out$post.means$time.intensity # Extract posterior mean estimates library(coda) mcmc.object <- as.mcmc(out$mcmc.samples$time.intensity) # Extract MCMC samples for coda summary(mcmc.object) plot(mcmc.object) ## End(Not run) simlnirt Simulate data for log-normal response time IRT modelling Simulate data for log-normal response time IRT modelling simlnirt(n, K, rho, = FALSE, = FALSE, kpa, kpt, kia, kit) N K rho kpa kpt kia kit the number of persons. the number of items. the correlation between the person ability and person speed parameter. set time-discrimination to one the number of predictors for the person ability parameters (optional). the number of predictors for the person speed parameters (optional). the number of predictors for the item-difficulty parameters (optional). the number of predictors for the item time intensity parameters (optional). an object of class simlnirt.
5 Index LNIRT, 2 LNRT, 3 simlnirt, 4 5
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