While I am at it, lets add the bootstrap estimate of the standard error
as well.
from numpy import mean, std, sum, sqrt, sort, corrcoef, tanh, arctanh
from numpy.random import randint
def bootstrap_correlation(x,y):
idx = randint(len(x),size=(1000,len(x)))
bx = x[idx]
by = y[idx]
robert wrote:
> > t = r * sqrt( (n-2)/(1-r**2) )
> yet too lazy/practical for digging these things from there. You obviously got
> it - out of that, what would be a final estimate for an error range of r (n
> big) ?
> that same "const. * (1-r**2)/sqrt(n)" which I found in that other document ?
robert wrote:
> I remember once I saw somewhere a formula for an error range of the corrcoef.
> but cannot find it anymore.
There is no such thing as "a formula for an error range" in a vacuum like that.
Each formula has a model attached to it. If your data does not follow that
model, then any
sturlamolden wrote:
> Robert Kern wrote:
>
>> The difference between the two models is that the first places no
>> restrictions
>> on the distribution of x. The second does; both the x and y marginal
>> distributions need to be normal. Under the first model, the correlation
>> coefficient has no
On 11/12/06, robert <[EMAIL PROTECTED]> wrote:
> Robert Kern wrote:
> > robert wrote:
(...)
> One would expect the error range to drop simply with # of points. Yet it
> depends more complexly on the mean value of the coef and on the distribution
> at all.
> More interesting realworld cases: For
sturlamolden wrote:
> First, are you talking about rounding error (due to floating point
> arithmetics) or statistical sampling error?
About measured data. rounding and sampling errors with special distrutions are
neglegible. Thus by default assuming gaussian noise in x and y.
(This may explain
First, are you talking about rounding error (due to floating point
arithmetics) or statistical sampling error?
If you are talking about the latter, I suggest you look it up in a
statistics text book. E.g. if x and y are normally distributed, then
t = r * sqrt( (n-2)/(1-r**2) )
has a Student t-d
robert wrote:
> Robert Kern wrote:
> http://links.jstor.org/sici?sici=0162-1459(192906)24%3A166%3C170%3AFFPEOC%3E2.0.CO%3B2-Y
>
>
> tells:
> probable error of r = 0.6745*(1-r**2)/sqrt(N)
>
> A simple function of r and N - quite what I expected above roughly for
> the N-only dep.. But thus it
Robert Kern wrote:
> The difference between the two models is that the first places no restrictions
> on the distribution of x. The second does; both the x and y marginal
> distributions need to be normal. Under the first model, the correlation
> coefficient has no meaning.
That is not correct.
Robert Kern wrote:
> robert wrote:
>> Is there a ready made function in numpy/scipy to compute the correlation
>> y=mx+o of an X and Y fast:
>> m, m-err, o, o-err, r-coef,r-coef-err ?
>
> And of course, those three parameters are not particularly meaningful
> together.
> If your model is truly
robert wrote:
> Is there a ready made function in numpy/scipy to compute the correlation
> y=mx+o of an X and Y fast:
> m, m-err, o, o-err, r-coef,r-coef-err ?
And of course, those three parameters are not particularly meaningful together.
If your model is truly "y is a linear response given x w
Robert Kern wrote:
> robert wrote:
>> Is there a ready made function in numpy/scipy to compute the correlation
>> y=mx+o of an X and Y fast:
>> m, m-err, o, o-err, r-coef,r-coef-err ?
> scipy.optimize.leastsq() can be told to return the covariance matrix of the
> estimated parameters (m and o in
robert wrote:
> Is there a ready made function in numpy/scipy to compute the correlation
> y=mx+o of an X and Y fast:
> m, m-err, o, o-err, r-coef,r-coef-err ?
numpy and scipy questions are best asked on their lists, not here. There are a
number of people who know the capabilities of numpy and s
Is there a ready made function in numpy/scipy to compute the correlation y=mx+o
of an X and Y fast:
m, m-err, o, o-err, r-coef,r-coef-err ?
Or a formula to to compute the 3 error ranges?
-robert
PS:
numpy.corrcoef computes only the bare coeff:
>>> numpy.corrcoef((0,1,2,3.0),(2,5,6,7.0),)
arra
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