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.ignored@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.nilesh84@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@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@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]]
> >
> > ______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-help
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> >
>
>
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