anova.py 文件源码

def subtr_cellmeans(workd,subjslots):
        """
   Subtract all cell means when within-subjects factors are present ...
   i.e., calculate full-model using a D-variable.
   """
        # Get a list of all dims that are source and between-subj
        sourcedims = makelist(Bbetweens,Nfactors+1)

        # Now, fix this list by mapping the dims from the original source
        # to dims for a between-subjects variable (namely, subjslots)
        transidx = range(len(subjslots.shape))[1:] + [0] # put subj dim at end
        tsubjslots = N.transpose(subjslots,transidx) # get all Ss for this idx
        tworkd = N.transpose(workd) # swap subj. and variable dims
        errors = 1.0 * tworkd

        if len(sourcedims) == 0:
            idx = [-1]
            loopcap = [0]
        if len(sourcedims) <> 0:
            btwsourcedims = map(Bscols.index,sourcedims)
            idx = [0] * len(btwsourcedims)
            idx[0] = -1 # compensate for pre-increment of 1st slot in incr()

            # Get a list of the maximum values each factor can handle
            loopcap = N.take(N.array(Nlevels),sourcedims)-1

### WHILE STILL MORE GROUPS, CALCULATE GROUP MEAN FOR EACH D-VAR
        while incr(idx,loopcap) <> -1:  # loop through source btw level-combos
            mask = tsubjslots[idx]
            thisgroup = tworkd*mask[N.NewAxis,:]
            groupmns = amean(N.compress(mask,thisgroup),1)

### THEN SUBTRACT THEM FROM APPROPRIATE SUBJECTS
            errors = errors - N.multiply.outer(groupmns,mask)
        return errors