Ondrej Zika

Thank you. If they have variable onset would estimating betas for each trial be a solution?

Dear Laure and Dominik, thank you. So, the glm got estimated using all regressors and it's only the visualisation that's inaccurate. Therefore the negative betas aren't likely a result wrong timing file. Dominik, by ordinal differences do you mean that I can look at the results using non-parametric statistics? Thank you, Ondrej

Dear all, I am struggling with the following issue. I have a state classification problem where an agent is going through series of trials and on which has to decide what's the current state and then based on that decision adjust information only for that state. The most effective way to do that is to use a multidimensional array where one of the dimensions is the state. This involves using the estimated state value as an idex in the other variables. Every time I try to do that, even in the simples...

Dear Dominik, thank you for your answer. I did a little digging and GLM results for another data set with larger ITI (3-5 s) and ISI (2-4s) seems to have the same issue: also negative betas but, interestingly, consistent results with study 1 (it's the same task). While I tripple checked the timings previously I went back to try and find any bug. The onset files seem fine but when I plot the events via PSPM (GLM review) the number of events doesn't match (there are way less events, approx. 1/4). I...

Dear Dominik, thank you for your answer. I did a little digging and GLM results for another data set with larger ITI (3-5 s) and ISI (2-4s) seems to have the same issue: also negative betas but, interestingly, consistent results with study 1 (it's the same task). While I tripple checked the timings previously I went back to try and find any bug. The onset files seem fine but when I plot the events via PSPM (GLM review) the number of events doesn't match (there are way less events, approx. 1/4). I...

I was wondering if you ever came across negative betas at the second level in the GLM model? I have a set of results that seems to make relatively sense, for example, there is a difference in between acquisition vs extinction, safe cue vs threatening cue, high anxious vs low anxious, etc, however the direction doesn't always make sense, most betas tend to be negative. This is a fear conditioning study with shocks and relatively short ISIs (1-2s) and ITIs (2-3s) and visual inspection of the data shows...

Which paper should I cite if I have used JAGS? Thank you, Ondrej

Hello, thank you for your response. I bypassed the NaN issue by including the code below in pspm_con1 for now: It's not quite as elegant but it does the job. I) You are right. It works. II) Thank you. That's a nice way of checking. Many thanks! All the best, Ondrej conval = conmat * data.stats; if sum(sum(isnan(conval)))>0 disp('Deviating from standard PsPM code!! Temporary fix in pspm_con1'); conval = []; for bc = 1:size(data.stats,2) for c = 1:size(conmat,1) conval(c, bc) = nansum(conmat(c,:)'...