LNRT: Log-normal response time modelling

In LNIRT: LogNormal Response Time Item Response Theory Models

Description

Log-normal response time modelling

Usage

LNRT(
  RT,
  data,
  XG = 1000,
  burnin = 10,
  XGresid = 1000,
  residual = FALSE,
  td = TRUE,
  WL = FALSE,
  XPT = NULL,
  XIT = NULL
)

Arguments

RT	a Person-x-Item matrix of log-response times (time spent on solving an item).
data	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).
burnin	the percentage of MCMC iterations to discard as burn-in period (default: 10).
XGresid	the number of MCMC iterations to perform before residuals are computed (default: 1000).
residual	compute residuals, >1000 iterations are recommended (default: false).
td	estimate the time-discrimination parameter (default: true).
WL	define the time-discrimination parameter as measurement error variance parameter (default: false).
XPT	an optional matrix of predictors for the person speed parameters.
XIT	an optional matrix of predictors for the item time intensity parameters.

Value

an object of class LNRT.

Examples

## Not run: 
# Log-normal response time modelling
data <- simLNIRT(N = 500, K = 20, rho = 0.8, WL = FALSE)
out <- LNRT(RT = RT, data = data, XG = 1500, residual = TRUE, td = TRUE, WL = 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)

LNIRT documentation built on Jan. 20, 2021, 1:05 a.m.