For classical statistics, I'd recommend GLM.jl over MultivariateStatsjl.
The former is written by a statistician (and R Core member) and the latter
is written by a machine learner who is very focused on speed. The main
reason for this recommendation is not speed, but that GLM.jl is quite
similar to the lm/glm functions in R when used with the DataFrames.jl
package.
The difference in precision is the difference between using a Cholesky and
a QR based solution for the least squares problem. Personally, I'm not too
worried about the loss of precision in the Cholesky solution because the
statistical error is so much larger than the numerical error.
On Sat, Jan 16, 2016 at 12:38 PM, Riccardo (Jack) Lucchetti <
r.lucchetti(a)univpm.it> wrote:
On Sat, 16 Jan 2016, Sven Schreiber wrote:
Am 15.01.2016 um 20:39 schrieb Allin Cottrell:
>
>> Following up Jack's comment at
>>
>>
http://lists.wfu.edu/pipermail/gretl-devel/2016-January/006467.html
>>
>> in current git there's a basic "preview" of Julia support in
gretl.
>>
>
> exciting!
>
>
> # NIST's certified coefficient values
>> matrix nist_b = {-3482258.63459582, 15.0618722713733,
>> -0.358191792925910E-01, -2.02022980381683,
>> -1.03322686717359, -0.511041056535807E-01,
>> 1829.15146461355}'
>>
>>
> Since I don't have it installed yet, could you comment on whether the
> results match (between gretl/Julia/NIST)?
>
These are the results I get
<output>
Log-relative errors, Longley coefficients:
gretl julia
12.228 8.0224
10.920 7.5300
11.797 7.5697
12.528 8.1421
13.169 8.3801
11.770 7.2368
12.235 8.0333
Column means
12.092 7.8449
</output>
So it would seem that the MultivariateStats julia module leaves a bit to
be desired for the moment, at lest in terms of precision.
-------------------------------------------------------
Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
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