[RsR] MASS:rlm() and robustbase::lmrob() are both breaking

[Gabor Grothendieck]

Try quantreg (using the interior point method):

fo <- da.g1 ~ -1 + f.year + major.industry + i.x + lta.l1 + I(lta.l1^2)
library(quantreg)
rq(fo, data = long, method = "fn")


On Sun, Sep 7, 2008 at 10:13 AM, Ajay Shah <ajayshah using mayin.org> wrote:
> I have a regression where the lm() goes through fine. This mailing
> list has always encouraged me to worry about how a robust regression
> might do things differently. I tried two approaches and both don't
> work.
>
> First I need to give you the dataset:
>
>> load(url("http://www.mayin.org/ajayshah/tmp/long.rda"))
>
> This gives you a data frame named "long". Here's the simple lm():
>
>> summary(lm(da.g1 ~ -1 + f.year +
> +            major.industry +
> +            i.x +
> +            lta.l1 + I(lta.l1^2), data=long))
>
> Call:
> lm(formula = da.g1 ~ -1 + f.year + major.industry + i.x + lta.l1 +
>    I(lta.l1^2), data = long)
>
> Residuals:
>     Min       1Q   Median       3Q      Max
> -632.563  -15.405   -5.090    7.797  543.972
>
> Coefficients:
>                          Estimate Std. Error t value Pr(>|t|)
> f.year2002                15.94994    4.52330   3.526 0.000424 ***
> f.year2003                15.89005    4.50107   3.530 0.000418 ***
> f.year2004                19.38506    4.48749   4.320 1.58e-05 ***
> f.year2005                23.65796    4.49146   5.267 1.43e-07 ***
> f.year2006                32.07334    4.48707   7.148 9.72e-13 ***
> f.year2007                35.88498    4.51369   7.950 2.16e-15 ***
> major.industryDiversified  1.74538    3.19979   0.545 0.585452
> major.industryElectricity  3.61036    6.16091   0.586 0.557887
> major.industryFood         1.52626    1.70112   0.897 0.369637
> major.industryMachinery   -0.15078    1.40149  -0.108 0.914329
> major.industryMetals       5.94554    1.66175   3.578 0.000349 ***
> major.industryMiscManuf    1.76956    2.17527   0.813 0.415965
> major.industryNonMetalMin  1.49889    1.92084   0.780 0.435224
> major.industryServ.IT      8.62764    1.86841   4.618 3.95e-06 ***
> major.industryServ.Other   6.43315    1.70598   3.771 0.000164 ***
> major.industryTextiles     0.07868    1.56312   0.050 0.959859
> major.industryTransportEq  4.81549    1.76354   2.731 0.006338 **
> i.xTRUE                    4.15376    0.97944   4.241 2.26e-05 ***
> lta.l1                    -3.91434    1.58494  -2.470 0.013546 *
> I(lta.l1^2)                0.23105    0.13922   1.660 0.097045 .
> ---
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Residual standard error: 34.48 on 6829 degrees of freedom
>  (1649 observations deleted due to missingness)
> Multiple R-squared: 0.2266,  Adjusted R-squared: 0.2244
> F-statistic: 100.1 on 20 and 6829 DF,  p-value: < 2.2e-16
>
> In this, f.year is a factor and major.industry is a factor. i.x is a
> boolean. lta.l1 is a real number. The left hand side variable (da.g1)
> is a real number. I put a -1 on the regression to make space for the
> dummy variables.
>
> On to my woes with robust regressions. MASS:rlm() breaks:
>
>> library(MASS)
>> summary(rlm(da.g1 ~ -1 + f.year +
> +            major.industry +
> +            i.x +
> +            lta.l1 + I(lta.l1^2), method="MM", data=long))
> Error in rlm.default(x, y, weights, method = method, wt.method = wt.method,  :
>  'x' is singular: singular fits are not implemented in rlm
>
> robustbase::lmrob() breaks:
>
>> library(robustbase)
>> summary(lmrob(da.g1 ~ -1 + f.year +
> +            major.industry +
> +            i.x +
> +            lta.l1 + I(lta.l1^2), data=long))
>
> Too many singular resamples
> Aborting fast_s_w_mem()
>
> Error in lmrob.S(x = x, y = y, control = control) :
>  C function R_lmrob_S() exited prematurely
>
>
> If you could guide me on what I'm doing wrong, that'll be great. How
> would I do the above specification as a robust regression? I googled
> around and I found a few others asking these same questions in the
> past, but it didn't look like there was a clear answer.
>
> --
> Ajay Shah                                      http://www.mayin.org/ajayshah
> ajayshah using mayin.org                             http://ajayshahblog.blogspot.com
> <*(:-? - wizard who doesn't know the answer.
>
> _______________________________________________
> R-SIG-Robust using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-robust
>