Hi all, I’m running some searchlights where (due to task mis-performance and the need to throw out some volumes) there are unbalanced categories, where the exact number of examples/target differ from subject to subject (and from chunk to chunk within subjects!). There aren’t -too- many of these bad volumes, so the categories are generally somewhat still close to 50%:50% category1:category2 (or 67/33 for a separate analysis).
My goal, as before, is to create for each subject an information map where each voxel’s value represents an accuracy-above-chance value for a local sphere. I plan on using these volumes in a top-level analysis in SPM. I’m wondering: 1. If it’s bad to have slight size-of-categories mismatches between subjects and between targets for any given subject. If so, I guess I could artificially prune some of the “good” volumes from the categories that have greater numbers of examples. However, I’m worried about making my dataset too small. 2. If there is a fairly simple and logical way to calculate the chance-level accuracy rate in a script. At the moment I use something like this to convert the searchlight-generated error numbers into accuracy maps: # [10] convert into percent-wise accuracies s1_map.samples *= -1 s1_map.samples += 1 s1_map.samples *= 100 s1_map.samples -= 50 # for 2-category classification with equal numbers of examples in each category Ideally, I’d like to automate whatever replaces the “50”. That said, I’m not sure if the number would always be obvious. (e.g. if I have 100 of one target and 50 of another, would the theoretical chance accuracy rate really be 66.7%? Or some value lower than this?) *Completely separately*, I was wondering if it were possible to add and account for a third row to the chunks/targets text file. One of my potential analyses involves training and testing on completely separate data (i.e. not LOOCV). I was, at first, thinking that I could hijack the “chunks” column for this purpose, giving dataA odd values, dataB even values, and then using an oddevensplitter. However, I still need the -real- chunks for linear detrending, etc. Thanks for your help! Mike Klein
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