Palamedes Toolbox Forum

Ah, yes, I see. Thanks for pointing that out. The palamedestoolbox.org/weibullandfriends.html page was created before the PFHB routines even existed. The page demonstrates things using the maximum-likelihood fitting scheme (i.e., using the PAL_PFML routines). And these do use a different syntax ...

You can specify the form of the psychometric function by using the 'PF' argument to the PAL_PFHB_fitModel function. Type 'help PAL_PFHB_fitModel' to see how to use the optional arguments. We very strongly suggest to log-transform stimulus values and using the Gumbel instead of using the Weibull on ...

The assumption to test here is not normality of the data. The difference between the parametric bootstrap and the non-parametric bootstrap is that the parametric bootstrap assumes that the fitted psychometric function (PF) is accurate. The non-parametric bootstrap does not make that assumption ...

Glad to hear I was helpful. Correct, Palamedes currently does not support including between-subject factors in a factorial (or other) design. Effects across multiple factors and their interactions can be defined but only across within-subject conditions.

Hmmm... There's a lof information here. Where is the unexpected estimate coming from? From the [P]AL_PFBA_Fit line in the code? Or the value of PM.threshold after running the PSI method? Or somewhere else yet? What would really help is some code that demonstrates the issue and that needs only Matlab ...

Hi Gerard,
Glad to hear you find Palamedes useful and gives you good fits.
Currently, the PFHB routines do not allow comparing groups of observers. You can compare individual observers against other individual observers but that’s it. You can of course run two separate analyses, one for each group ...

Yes, I did misunderstand. I thought you were trying to keep the data but make the fits acceptable, one way or another. There really is no ‘clean’ way to exclude bad data from an analysis. There is no rule that says that if your goodness-of-fit p is less than 0.05 (or any other number) you can simply ...

Ask twelve people for their opinion on this and you’re likely to get 13 different answers. Maybe others will join in to this discussion but here’s my two cents. Your model makes assumptions, e.g., ‘the true relation between stimulus intensity and the probability that the participant will make ...

The recommended way to specify custom priors is demonstrated in the code below. It puts an ex-Gaussian prior on alpha (ex-Gaussian is often used to model skewed distributions including RTs) and puts a Normal prior on slopes. Strategy can be extended to put any custom prior on any of the PF's ...

By the time the error occurs, you have already modeled your data (using the first call to PAL_PFHB_fitModel in the demo). The crash you report happens during an alternative fit that uses a model matrix. The crash happens because the model matrix M is specific to a three-condition setup (and I gather ...