EZ2 diffusion modeling of Response Time and Accuracy

Description

EZ2 fits a simplified version of the diffusion model for human and monkey response times and accuracy to the means and variances of the observables.

Details

Package:	EZ2
Type:	Package
Version:	1.0
Date:	2007-08-31
License:	GPL version 2 (or later)

You need a data frame containing (at least) the moments (RT means, RT variances, and proportions correct) that you would like to model.

Specify for each used moment a formula of the form vrt1 ~ EZ2.vrt(v1, z, a).

Supply the model with the data and a startingpoint for all unknown parameters to EZ2 or EZ2batch to calculate method of moment estimators or least squares estimators of the unknown parameters in the model (that is, v1, z, and a in vrt1 ~ EZ2.vrt(v1, z, a)).

Your model can be complex and extensive; for instance

vrt1 ~ EZ2.vrt(v1, z, a) + p0*(maxRT-minRT)^2/12+p0*(1-p0)*(EZ2.mrt(v1, z, a)-(maxRT+minRT)/2)^2

is equaly valid.

Author(s)

Raoul Grasman

Maintainer: Raoul Grasman <rgrasman@uva.nl>

References

Grasman, Wagenmakers & van der Maas (2007) "EZ2: An extention of the EZ-diffusion model for Response Time and Accuracy, Manuscript submitted for publication in J. Math. Psych.

See Also

Examples

## create some data (theoretical values, not simulated) Needless to say, in reality you would like to fit real data!
A = seq(.08,.13,len=6)
X2 = data.frame(A=A)
X2$vrt0 = sapply(A, function(a) EZ2.vrt(.1,.05,a))
X2$pe0 = sapply(A, function(a) EZ2.pe(.1,.05,a))
X2$vrt1 = sapply(A, function(a) EZ2.vrt(.2,a-.05,a))
X2$pe1 = sapply(A, function(a) EZ2.pe(.2,a-.05,a))

X2 = as.data.frame(X2)          # now pretend that X2 is the data frame that you may have computed from real data

## fit an EZ2 model on each row
#  method 1:
EZ2batch(c(v0=.11,v1=.21,z=.05,a=.09), 
 vrt0 ~ EZ2.vrt(v0,z,a), 
  pe0 ~ EZ2.pe(v0,z,a), 
 vrt1 ~ EZ2.vrt(v1,a-z,a), 
  pe1 ~ EZ2.pe(v1, a-z, a), data=X2)

# method 2 (eventually less typing):
mdl <- list( vrt0 ~ EZ2.vrt(v0,z,a), 
              pe0 ~ EZ2.pe(v0,z,a), 
             vrt1 ~ EZ2.vrt(v1,a-z,a), 
              pe1 ~ EZ2.pe(v1, a-z, a)
           )
EZ2batch(c(v0=.11,v1=.21,z=.05,a=.09), mdl, data=X2)