[R] Multiple Multivariate regression in R with 50 independent variables
Ranjan Maitra
maitra.mbox.ignored at inbox.com
Fri Apr 19 19:16:20 CEST 2013
First, do you know what a multivariate multiple (linear) regression
means? As opposed to (univariate) multiple (linear) regression. As
others have pointed out, the example referred to is of univariate
multiple linear regression.
Second, and more importantly, have you yourself tried doing the needed
regression with the data you have. If so, what are the results?
Is your response even multivariate? (Sorry, entering the thread late.)
Of course, you need to first know what you are trying to do. Any tool is
only so good as the workman handling it.
Ranjan
On Fri, 19 Apr 2013 17:21:39 +0530 Nilesh Gupta
<gupta.nilesh84 at gmail.com> wrote:
> I used this link
> http://r.789695.n4.nabble.com/model-frame-and-formula-mismatch-in-model-matrix-td4664093.html
>
> Regards
>
> The woods are lovely, dark and deep
> But I have promises to keep
> And miles before I go to sleep
> And miles before I go to sleep
> -----
>
>
> On Fri, Apr 19, 2013 at 2:19 PM, David Winsemius <dwinsemius at comcast.net>wrote:
>
> >
> > On Apr 19, 2013, at 12:40 AM, Nilesh Gupta wrote:
> >
> > > lm() does not accomodate more than 50 independent variables
> >
> > What is your source for this misinformation?
> >
> > > dat <- as.data.frame(matrix(rnorm(51000), ncol=51) )
> > > names(dat)
> > [1] "V1" "V2" "V3" "V4" "V5" "V6" "V7" "V8" "V9" "V10" "V11"
> > "V12" "V13" "V14" "V15" "V16" "V17" "V18"
> > [19] "V19" "V20" "V21" "V22" "V23" "V24" "V25" "V26" "V27" "V28" "V29"
> > "V30" "V31" "V32" "V33" "V34" "V35" "V36"
> > [37] "V37" "V38" "V39" "V40" "V41" "V42" "V43" "V44" "V45" "V46" "V47"
> > "V48" "V49" "V50" "V51"
> > > lm(V1 ~ ., dat=dat)
> >
> > Call:
> > lm(formula = V1 ~ ., data = dat)
> >
> > Coefficients:
> > (Intercept) V2 V3 V4 V5
> > V6 V7 V8
> > -0.0089517 -0.0427225 -0.0754946 -0.0002903 -0.0083482
> > 0.0324383 -0.0194980 -0.0151008
> > V9 V10 V11 V12 V13
> > V14 V15 V16
> > 0.0255324 -0.0167399 0.0476841 -0.0222229 0.0720990
> > -0.0174327 -0.0104261 0.0024625
> > V17 V18 V19 V20 V21
> > V22 V23 V24
> > -0.0086276 -0.0274867 -0.0345897 0.0209116 0.0368201
> > -0.0027364 0.0090916 0.0198854
> > V25 V26 V27 V28 V29
> > V30 V31 V32
> > -0.0083732 -0.0216937 0.0586361 -0.0530041 0.0402765
> > 0.0073514 0.0295976 -0.0641553
> > V33 V34 V35 V36 V37
> > V38 V39 V40
> > 0.0491071 -0.0261259 0.0364740 0.0070261 -0.0159851
> > -0.0373357 0.0506756 -0.0383495
> > V41 V42 V43 V44 V45
> > V46 V47 V48
> > 0.0054945 0.0089468 -0.0050151 -0.0184369 0.0019926
> > -0.0177631 0.0282828 0.0353523
> > V49 V50 V51
> > -0.0382634 0.0545654 0.0101398
> >
> > > dat <- as.data.frame(matrix(rnorm(101000), ncol=101) )
> > > lm(V1 ~ ., dat=dat)
> >
> > Call:
> > lm(formula = V1 ~ ., data = dat)
> >
> > Coefficients:
> > (Intercept) V2 V3 V4 V5
> > V6 V7 V8
> > 0.021065 -0.015988 -0.008273 0.049849 0.014874
> > 0.012352 -0.054584 0.004542
> > V9 V10 V11 V12 V13
> > V14 V15 V16
> > -0.017186 0.018006 -0.009707 -0.007382 0.044886
> > -0.051122 -0.026910 -0.048929
> > V17 V18 V19 V20 V21
> > V22 V23 V24
> > -0.008129 0.022129 -0.063525 0.026683 0.013424
> > -0.010145 -0.046046 0.024025
> > V25 V26 V27 V28 V29
> > V30 V31 V32
> > -0.003529 -0.038270 0.043657 0.049855 0.010691
> > 0.041217 -0.012596 0.018302
> > V33 V34 V35 V36 V37
> > V38 V39 V40
> > 0.040225 -0.012751 -0.062677 -0.002810 -0.002574
> > -0.024137 0.021324 -0.041520
> > V41 V42 V43 V44 V45
> > V46 V47 V48
> > -0.076482 0.009063 0.067097 -0.042554 -0.013789
> > 0.002865 0.017325 -0.076860
> > V49 V50 V51 V52 V53
> > V54 V55 V56
> > -0.007003 -0.007315 0.030270 0.022066 -0.002224
> > -0.056534 0.013705 -0.003609
> > V57 V58 V59 V60 V61
> > V62 V63 V64
> > -0.044580 -0.037543 0.015745 0.035250 -0.017117
> > 0.072470 0.004398 -0.015923
> > V65 V66 V67 V68 V69
> > V70 V71 V72
> > 0.012864 -0.062752 -0.038437 -0.019586 0.019871
> > -0.068398 -0.111778 0.021416
> > V73 V74 V75 V76 V77
> > V78 V79 V80
> > 0.036849 -0.009103 0.037790 0.021883 -0.034990
> > -0.014917 -0.003854 0.001760
> > V81 V82 V83 V84 V85
> > V86 V87 V88
> > -0.001812 0.003942 0.021810 -0.013984 -0.030446
> > 0.049187 0.008392 0.026965
> > V89 V90 V91 V92 V93
> > V94 V95 V96
> > 0.057301 0.004190 0.055505 -0.046006 -0.019080
> > -0.098889 -0.010891 -0.002729
> > V97 V98 V99 V100 V101
> > 0.024939 -0.029847 0.063578 -0.061667 -0.022163
> >
> > > system.time( lm(V1 ~ ., dat=dat) ) # with the 101 column dataframe
> > user system elapsed
> > 0.060 0.008 0.076
> >
> > Sorry to give you such a Frost-y reception, but you are being somewhat ...
> > what's the right word... sleepy?
> >
> > --
> > David.
> >
> >
> > >
> > > The woods are lovely, dark and deep
> > > But I have promises to keep
> > > And miles before I go to sleep
> > > And miles before I go to sleep
> > > -----
> > >
> > >
> > > On Fri, Apr 19, 2013 at 12:26 PM, peter dalgaard <pdalgd at gmail.com>
> > wrote:
> > >
> > >>
> > >> On Apr 18, 2013, at 21:24 , Nilesh Gupta wrote:
> > >>
> > >>> Hello all
> > >>>
> > >>> Is there a method/package in R in which I can do regressions for more
> > >> than
> > >>> 50 independent variables ?
> > >>
> > >> What's wrong with lm() et al.?
> > >>
> > >> --
> >
> >
> > David Winsemius
> > Alameda, CA, USA
> >
> > ______________________________________________
> > R-help at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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