Palamedes Toolbox Forum

Hello,
I am new to Palamedes and I believe the data I have can work with the single subject multiple conditions demo. However I am changing the number of conditions. I see in the demo the function, PAL_PFHB_inspectParam is used for conditions 1 and 2. Do I need to add extra lines of this function if I am adding more conditions?

Many thanks
Aoibheann

It all depends on what you're interested to see. The only thing needed to run the analysis itself is PAL_PFHB_fitModel. All the rest is essentially just visualization of results. PAL_PFHB_drawViolins is probably most useful to get a quick look at whether your location parameter (aka threshold, PSE, etc.) or slope parameter differs between conditions.
The line:
PAL_PFHB_inspectParam(pfhb,'a','condition',1,'a','condition',2);
is useful to compare the location parameter ('a') between conditions 1 and 2. Essentially a pairwise comparison. To compare location parameters between conditions 2 and 3 you would use:
PAL_PFHB_inspectParam(pfhb,'a','condition',2,'a','condition',3);
Make sure to check out https://www.palamedestoolbox.org/hierar ... esian.html and the help in the functions. Type e.g., 'help PAL_PFHB_inspectParam' (without quotes) to learn more about what that function does.

Thank you for your reply.

We were trying to fit our data into the Single subject multiple condition demo and this is the error message we get:

Index in position 2 exceeds array bounds. Index must not exceed 3.

Error in PAL_PFHB_setupModel (line 382)
data.ac(contrast,data.c == cond) = model.a.cTtoP(contrast,cond);

Error in PAL_PFHB_fitModel (line 277)
pfhb = PAL_PFHB_setupModel(data, varargin{:});

Error in PAL_PFHB_SingleSubjectMultipleConditions_edited (line 77)
pfhb = PAL_PFHB_fitModel(data,'gammaEQlambda',true,'engine',engine,'a',M,'nsamples',10000);


We were also hoping to clarify how the 'M' values are decided and based on what parameters:

M = [1 1 1; -2 1 1; 0 -1 1] %Reparameterizes location parameters. E.g., pfhb.summStats.a.mean(1)
%will contain the mean of posterior on the sum of location parameters,
%pfhb.summStats.a.mean(2) will contain the mean of the posterior on
%the difference between location parameters.
%pfhb.summStats.a_actual.mean(1) will contain the mean
%of the posterior of the location parameter in
%condition 1. This posterior was not directly sampled
%but rather derived from reparameterized parameters.

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 you have more than three conditions in your data).

All that is required to perform a basic fit is this:

pfhb = PAL_PFHB_fitModel(data); %add arguments as needed

Then, to view the data plus fits for all conditions and subjects (if there are several):

PAL_PFHB_inspectFit(pfhb,’all’); %cycle through using ‘enter’

To visualise posterior and summary statistics for parameter estimates::

PAL_PFHB_drawViolins(pfhb);

Model matrices are quite useful to target specific research questions. A tutorial on model matrices and how to create model matrices that target specific research questions can be found here: www.palamedestoolbox.org/modelmatrix.html