I have one column which contains the group ID of each participant. There are three groups so every number in this column is 1, 2 or 3.
Then I have a second column which contains response scores for each participant. I want to calculate the mean/median response score within each group.
I have managed to do this by looping through every row but I sense this is a slow and suboptimal solution. Could someone please suggest a better way of doing things?
Use logic conditions, for example say your data is in matrix m as follows: the first col is ID the second col is the response scores,
翻译mean(m(m(:,1)==1,2)) median(m(m(:,1)==1,2))will give you the mean and median for 1 in the response score, etc
This is a good place to use accumarray (documentation and blog post):
result = accumarray(groupIDs, data, [], @median);You can of course give a row or column of a matrix instead of a variable called groupIDs and another for data. If you'd prefer the mean instead of the median, use @mean as the 4th arg.
Note: the documentation notes that you should sort the input parameters if you need to rely on the order of the output. I'll leave that exercise for another day though.
grpstats is a good function to be used ( documentation here )
This is a list of embedded statistics:
and it accepts as well function handles ( Ex: @mean, @skeweness)
>> groups = [1 1 1 2 2 2 3 3 3]'; >> data = [0 0 1 0 1 1 1 1 1]'; >> grpstats(data, groups, {'mean'}) ans = 0.3333 0.6667 1.0000 >> [mea, med] = grpstats(data, groups, {'mean', @median}) mea = 0.3333 0.6667 1.0000 med = 0 1 1