diffIRT (version 1.5)

simdiff: Simulate data according to the D-diffusion or Q-diffusion IRT model.

Description

This function simulates responses and response time data according to the D-diffusion or Q-diffusion IRT model.

Usage

simdiff(N,nit,ai=NULL,vi=NULL,gamma=NULL,theta=NULL,ter=NULL, model="D",max.iter=19999,eps=1e-15)

Arguments

N

number of subjects.

nit

number of items.

ai

a vector of length nit containing the true values for the item boundary separation, a[i].

vi

a vector of length nit containing the true values for the item drift rate, v[i].

gamma

a vector of length N containing the true values for the person boundary separation, gamma[p].

theta

a vector of length N containing the true values for the person drift rate, theta[p].

ter

a vector of length nit containing the true values for the item non-decision time, ter[i].

model

string; Either "D" to fit the D-diffusion IRT model or "Q" to fit the Q-diffusion IRT model.

max.iter

maximum number of iterations for the rejection algorithm. See Details.

eps

convergence criterion for the rejection algorithm. See Details

Value

Returns a list with the following entries:

rt

the simulated matrix of response times

x

the simulated matrix of responses

ai

true values for ai[i]

vi

true values for vi[i]

gamma

true values for gamma[p]

theta

true values for theta[p]

ter

true values for ter[i]

Details

Function simdiff is an extension of the rejection algorithm outlined in Tuerlinckx et al. (2001). In this algorithm, a proposal response time is sampled from an exponential distribution. This proposal is accepted as actual response time when a specific condition is satisfied (see Eq. 16 in Tuerlinckx, 2001). As this condition requires the approximation of an infinite sum, a convergence criterion needs to be specified (see the argument eps). When the condition is not satisfied, a new proposal response time is sampled. This is repeated until the proposal response time is accepted or when max.iter has been reached.

References

Tuerlinckx, F., Maris, E., Ratcliff, R., & De Boeck, P. (2001). A comparison of four methods for simulating the diffusion process. Behavior Research Methods, Instruments & Computers, 33, 443-456.

See Also

diffIRT for fitting diffusion IRT models.

Examples

## Not run: 
# # simulate data accroding to D-diffusion model
# data=simdiff(N=100,nit=10,model="D")                   
# 
# ## End(Not run)