Ranjan I have multiple y's(stocks,2300 odd) which i want to run on 3 variables. for 500 months. I assume this is multivariate regression for there are multiple y's and multiple X's as compared to univariate multiple regression wherein there is only one y and there are more than X's. If I were to pool these returns into one big data ( keeping apart the part where that makes sense from the theory point of view) then that would be an example of univariate multiple regression As a further clarification my Y matrix is of order 500X2398 whereas my X matrix is of order 500X4. I am expecting a beta matrix of 4X2398, assuming Y = X*Beta formulation
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 10:46 PM, Ranjan Maitra < maitra.mbox.igno...@inbox.com> wrote: > 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.niles...@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 <dwinsem...@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 <pda...@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@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@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. > > > > > -- > Important Notice: This mailbox is ignored: e-mails are set to be > deleted on receipt. 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