Phil Steitz a écrit :
> MATH114 and MATH138 propose support for correlation matrices. I have
> been working on these and would like to propose the following:
>
> Create a new package o.a.c.m.stat.correlation to house intially
> a) Covariance  creates variancecovariance matrix from a matrix
> whose columns represent covariates. Also includes convenience methods
> that work pairwise on double[] arrays (similar to VectorialCovariance,
> but requiring that the arrays be stored)
> b) PearsonCorrelation  creates Pearson's productmoment correlation
> matrix from either a covariance matrix or a matrix of covariates. Also
> includes methods to return matrices of correlation standard errors and
> pvalues (aka significances, i.e. pvalue for null hypothesis that the
> coefficient is 0).
> c) SpearmanRankCorrelation  like Pearson's but no covariance matrix
> constructor and using rank correlation.
> To implement c), we need a place for the RankingAlgorithm interface and
> implementations (see MATH138). Any suggestions on where to put
> these? Leaving in correlation may be awkward later on as we do more
> with rank transformations.
>
> I have a) implemented using a fairly stable twopass algorithm. I tried
> just using VectorialCovariance, but could not get the accuracy I wanted
> using the onepass algorithm there. We should probably at some point
> look at improving the updating formula used there along the lines of
> what we do for Variance, but it is a nice feature of that class that it
> does not require the input vectors to be stored and I would not want to
> see that changed. For b), similar to the patch in JIRA, I would use
> the R computation from SimpleRegression if working from a matrix, or
> just compute column sigmas and scale directly if working from a
> covariance matrix.
This seems good to me. Perhaps a dedicated "ranking" package under stat
would be fine.
Luc
>
> Does this sound good?
>
> If I don't hear any objections, I will commit some code along the lines
> above for us to look at.
>
> Phil
>
>
> 
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