Yes, the variance. But that doesn't explain why you can't get the covariance matrix of an array of vectors.
On Friday, May 8, 2015 at 4:51:13 PM UTC-4, Andreas Noack wrote: > > Calculating the covariance requires two sequences of data points. Either > from two vectors or between the columns of a matrix. The mean is different > as it requires one sequence. What did you expect to get from the covariance > function of a vector? The variance? > > 2015-05-08 16:01 GMT-04:00 JPi <[email protected] <javascript:>>: > >> Hello, >> >> 1. I can apply mean to an array of vectors, but doing the same for cov >> produces an error. >> >> 2. I can apply cov to a matrix, which produces the covariance matrix >> treating each row as an observation. Applying mean to the same matrix >> produces a scalar average of all elements in the matrix. >> >> This asymmetric treatment is counter-intuitive. What is the rationale? >> >> Thanks! >> >> n=10 >> A=Array(Vector,n) >> >> for i=1:n >> A[i]=randn(3) >> end >> >> println(mean(A)) >> println(cov(A)) # >> produces an error >> >> B=Array(Float64,n,3) >> for i=1:n >> B[i,:]=A[i] >> end >> >> println("mean:",mean(B)) # >> produces average of all elements in B >> println("covariance matrix:",cov(B)) # >> produces covariance matrix of columns of B >> >> >> >> >
