Matlab: Query complicated structures - matlab

Question

I am using structures in Matlab to organize my results in an intuitive way. My analysis is quite complex and hierarchical, so this works well---logically. For example:
resultObj.multivariate.individual.distributed.raw.alpha10(1).classification(1). Each level of the structure has several fields. Each alpha field is a structured array, indexed for each dataset, and classification is also a structured array, one for each cross validation run on the data.
To simplify, consider the the classification field:
>> classification
ans =
1x8 struct array with fields:
bestLambda
bestBetas
scores
statObj
fitObj
In which statObj has fields (for example):
dprime: 6.5811
hit: 20
miss: 0
falseAlarms: 0
correctRejections: 30
Of course, the fields have different values for each subject and cross validation run. Given this structure, is there a good way to find the mean of dprime over cross validation runs (i.e. the elements of classification) without needing to construct a for loop to extract, store, and finally compute on?
I was hoping that reshape(struct2array(classification.statObj),5,8) would work, so I could construct a matrix with stats as rows and cross validations runs as columns, but this won't work. I put these items in their own structure specifically because the fields of classification hold elements of various types (matrices, structures, integers).
I am not opposed to restructuring my output entirely, but I'd like it to be done in such a way that the organization is fairly self-commenting, and I could say return to this structure a year from now and remember what and where everything is.

Answer 1

I came up with the following, although I'm not sure if it is what you are looking for:
%# create a structure hierarchy similar to yours
%# (I ignore everything before alpha10, and only create a part of it)
alpha10 = struct();
for a=1:5
alpha10(a).classification = struct();
for c=1:8
alpha10(a).classification(c).statObj = struct('dprime',rand());
end
end
%# matrix of 'dprime' for each alpha across each cross-validation run
st = [alpha10.classification];
st = [st.statObj];
dp = reshape([st.dprime], 8, 5)' %# result is 5-by-8 matrix
Next you can compute mean across the second dimension of this matrix dp

Answer 2

For anyone who happens across this post, and is wrestling with something similar, it is worth asking yourself if such a nested structure-of-structures is really your best option. It may be easier to flatten the hierarchy and include descriptive fields as labels. For instance
resultObj.multivariate.individual.distributed.raw.alpha10(1).classification(1)
might instead be
resultObj(1).
AnlaysisType = 'multivariate'
GroupSolution = false
SignalType = 'distributed'
Processing = 'raw'
alpha = 10
crossvalidation = 1
dprime = 6.5811
bestLambda = []
bestBetas = []
scores = []
fitObj = []
That's not valid Matlab syntax there, but it get's the point across. Rather than building a hierarchy out of nested structures, create a 1xN structure with labels and data. It is a more general solution that is easier to query and work with.