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 > ------------------------------------------------------- > > _______________________________________________ > Gretl-devel mailing list > Gretl-devel(a)lists.wfu.edu > http://lists.wfu.edu/mailman/listinfo/gretl-devel >
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 <[email protected]> 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)
 [email protected]
 http://www2.econ.univpm.it/servizi/hpp/lucchetti
-------------------------------------------------------
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