simdiffT {diffIRT} | R Documentation |

This function simulates responses and response time data according to the traditional diffusion model for a single subject on a given number of trails. The parameters of the traditional diffusion model include: boundary separation, mean drift rate, standard deviation of drift rate, variance of the process, and ter.

simdiffT(N,a,mv,sv,ter,vp,max.iter=19999,eps=1e-15)

`N` |
number of trails. |

`a` |
boundary separation. |

`mv` |
mean of the normally distributed drift rates across trails. |

`sv` |
standard deviation of the normally distributed drift rate across trails. |

`ter` |
non-decision time. |

`vp` |
variance of the process, which is a scaling parameter. Default equals 1. |

`max.iter` |
Maximum number of iterations for the rejection algorithm. See |

`eps` |
Convergence criterion for the rejection algorithm. See |

Function `simdiffT`

is an application of the rejection algorithm outlined in Tuerlinckx et al. (2001) subject
to normally distributed inter-trail variability in drift. 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.

Returns a list with the following entries:

`rt` |
the simulated matrix of response times |

`x` |
the simulated matrix of responses |

Dylan Molenaar d.molenaar@uva.nl

Molenaar, D., Tuerlinkcx, F., & van der Maas, H.L.J. (2015). Fitting Diffusion Item Response Theory Models for Responses and Response Times Using the R Package diffIRT.
*Journal of Statistical Software*, **66(4)**, 1-34. URL http://www.jstatsoft.org/v66/i04/.

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.

`diffIRT`

for fitting diffusion IRT models.

## Not run: # simulate data accroding to the traditional diffusion model set.seed(1310) a=2 v=1 ter=2 sdv=.3 N=10000 data=simdiffT(N,a,v,sdv,ter) rt=data$rt x=data$x # fit the traditional diffusion model (i.e., a restricted D-diffusion model, # see application 3 of the paper by Molenaar et al., 2013) diffIRT(rt,x,model="D",constrain=c(1,2,3,0,4),start=c(rep(NA,3),0,NA)) # this constrained model is a traditional diffusion model # please note that the estimated a[i] value = 1/a # and that the estimated v[i] value = -v ## End(Not run)

[Package *diffIRT* version 1.5 Index]