[SciPy-user] linear regression
Wed May 27 16:04:27 CDT 2009
> On Wed, May 27, 2009 at 9:35 AM, <email@example.com> wrote:
>> Is there a recommended way now of calculating the slope of a linear
>> regression? Using the scipy.stats.linregress function gives a
>> deprecation warning, apparently because that function uses the
>> scipy.mean function:
>> sage: import numpy
>> sage: import scipy.stats
>> sage: scipy.stats.linregress(numpy.asarray([4,3,2,1,2,3,4]),
>> DeprecationWarning: scipy.stats.mean is deprecated; please update your
>> code to use numpy.mean.
>> Please note that:
>> - numpy.mean axis argument defaults to None, not 0
>> - numpy.mean has a ddof argument to replace bias in a more general
>> scipy.stats.mean(a, bias=True) can be replaced by numpy.mean(x,
>> axis=0, ddof=1).
>> axis=0, ddof=1).""", DeprecationWarning)
>> (-1.0, 5.0, -1.0, 1.9206748078018268e-50, 0.0)
>> This is scipy 0.7.0.
> I backported a fix for incorrect slopes standard error
> together with the switch to using numpy versions of the depreciated
> stats function.
Thanks. I tested the fixes, and it's slower than np.polyfit, so for now
(unless there is good reason not to), I'm moving the one call over to
> However, not all usage of the depreciated functions has been
> backported to 0.7.1, but all are (supposed to be) fixed in the trunk
> for 0.8.
> So, these kind of depreciation warnings in 0.7.0 and 0.7.1 are just
> the result of unfinished conversion to numpy stats functions.
Thanks. I already fixed a lot of the deprecation warnings (by switching
to the numpy functions) we received from the Sage doctests regarding the
mean, variance, and std stats functions.
Dare I ask for what the give-or-take-a-million-years deadline for 0.8 is?
Thanks for a great project!
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