R help - Oct 2003 - question about predictions with linear models

Hi,
  this question is probably very obvious but I just cant see where I
might be going wrong. 

I'm using the lm() function to generate a linear model and then make
predictions using a different set of data.

To generate the model I do (tdata & pdata are matrices of observations
and parameters, tdepv, pdepv are response vectors)

x <- as.data.frame(tdata)
x$tdepv <- tdepv
lnegth(tdepv) = 140

model <- lm(x$tdepv ~ x$V1 + x$V2 + x$V3 + x$V4, x)

pred <- predict(model, x)
length(pred) = 140

y <- as.data.frame(pdata)
y$pdepv <- pdepv
length(pdepv) = 16

pred <- predict(model, y)
length(pred) = 140

But I expect that length(pred) = 16

Why do I get a different length? Furthermore, the original formula
specified the variable tdepv which is not in the dataframe that I send
to predict() - should I also make a variable called tdepv in the
dataframe y?

Thanks,

-------------------------------------------------------------------
Rajarshi Guha <rxg218 at psu.edu> <http://jijo.cjb.net>
GPG Fingerprint: 0CCA 8EE2 2EEB 25E2 AB04 06F7 1BB9 E634 9B87 56EE
-------------------------------------------------------------------
The way to love anything is to realize that it might be lost.

>>>>> "Rajarshi" == Rajarshi Guha <rxg218 at
psu.edu>
>>>>>     on 01 Oct 2003 12:59:16 -0400 writes:
    Rajarshi> Hi,
    Rajarshi> this question is probably very obvious but I just cant see
where I
    Rajarshi> might be going wrong. 

    Rajarshi> I'm using the lm() function to generate a linear
    Rajarshi> model and then make predictions using a different
    Rajarshi> set of data.

    Rajarshi> To generate the model I do (tdata & pdata are
    Rajarshi> matrices of observations and parameters, tdepv,
    Rajarshi> pdepv are response vectors)

This is not reproducible for us, since we don't have your data,
and the following is partly definitely not the R code you used.
(you didn't use "lnegth()" nor did you ***set** 
"length(pred) = 140",
 did you?)
All we can guess is that you had missing values in your data,
and you did *not* consider setting  options(na.action = "na.exclude")
{read  ?na.exclude  first}.


    Rajarshi> x <- as.data.frame(tdata)
    Rajarshi> x$tdepv <- tdepv
    Rajarshi> lnegth(tdepv) = 140

    Rajarshi> model <- lm(x$tdepv ~ x$V1 + x$V2 + x$V3 + x$V4, x)

    Rajarshi> pred <- predict(model, x)
    Rajarshi> length(pred) = 140

    Rajarshi> y <- as.data.frame(pdata)
    Rajarshi> y$pdepv <- pdepv
    Rajarshi> length(pdepv) = 16

    Rajarshi> pred <- predict(model, y)
    Rajarshi> length(pred) = 140

    Rajarshi> But I expect that length(pred) = 16

    Rajarshi> Why do I get a different length? Furthermore, the original
formula
    Rajarshi> specified the variable tdepv which is not in the dataframe that
I send
    Rajarshi> to predict() - should I also make a variable called tdepv in
the
    Rajarshi> dataframe y?

    Rajarshi> Thanks,

    Rajarshi>
-------------------------------------------------------------------
    Rajarshi> Rajarshi Guha <rxg218 at psu.edu>
<http://jijo.cjb.net>
    Rajarshi> GPG Fingerprint: 0CCA 8EE2 2EEB 25E2 AB04 06F7 1BB9 E634 9B87
56EE
    Rajarshi>
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