A partial answer is...
After doing debug(lm.fit) and debug(lm), and waiting to see where the
messages come from, I can tell that when you do na.action=NULL, the R
goes to call the Fortran routine dqrls (through .Fortran) and when R
prepares the data to pass to the dqrls it triggers one of the error
checks. Upshot being that R decides dqrls ought not get NA's as values
to work with...
when you don't do na.action=NULL, the NA's get caught in the error
checking for lm.fit... which seems logical since the complaint describes
what you are doing: "0 (non-NA) cases".
If you're doing regression by adjacent columns, I think calling lm with
a column full of NA's as the independent variable is a mistake since lm
can't really do anything with that--your function should check that
there is a reason to call lm, maybe like so:
##Code start
SourceMat<-matrix(data=rnorm(100), ncol=10, nrow=10)
SourceMat[,3]<-c(NA)
tt<-time(SourceMat)
rownum=2
colnum=10
ResultMat<-matrix(NA, ncol=colnum, nrow=rownum)
#loop through each column in the source matrix:
for (i in 1:10)
{
sel_col<-SourceMat[col(SourceMat)==i] #selecting the correct
column in the matrix in turn
if(!all(is.na(sel_col))) {ResultMat[,i]<-coef(lm(tt~sel_col))}
}
##Code end
rcoder wrote:
Hi Gabor,
Thanks for your reply. I've written something that can be copied and pasted
into your monitor to reproduce the error I am experiencing. Once the loop
experiences a column full of NAs in SourceMat (column 3), it exits with
errors, and ResultMat is only partially complete (up to column 2) with o/p
intercept and slope results.
When I include the 'na.action=NULL' statement, I get the following
statement:
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
NA/NaN/Inf in foreign function call (arg 1)
When I leave this statement out, I get the following:
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
0 (non-NA) cases
In either case, ResultMat is only filled up to column 2:
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 5.3611056 5.4099400 NA NA NA NA NA NA NA NA
[2,] -0.8028985 -0.4078084 NA NA NA NA NA NA NA NA
##Code start
SourceMat<-matrix(data=rnorm(100), ncol=10, nrow=10)
SourceMat[,3]<-c(NA)
tt<-time(SourceMat)
rownum=2
colnum=10
ResultMat<-matrix(NA, ncol=colnum, nrow=rownum)
#loop through each column in the source matrix:
for (i in 1:10)
{
sel_col<-SourceMat[col(SourceMat)==i] #selecting the correct column
in the matrix in turn
ResultMat[,i]<-coef(lm(tt~sel_col, na.action=NULL))
}
##Code end
I would be grateful for any suggestions to avoid this problem.
Thanks,
rcoder
rcoder wrote:
Well, in this case I don't think my original code would have helped
much...
So, I've rewritten as below. I want to perform regression between one
column in a matrix and all other columns in the same matrix. I have a for
loop to achieve this, which succeeds in exporting intercept and slope
coefficients to a results matrix, except when a column that contains only
NAs is reached. Columns partially filled with NAs are handled, but the
code exits with errors when a single column is filled with NAs. I inserted
the 'na.action=NULL' statement within the lm() construct, but to no avail.
I would be very grateful for any advice.
tt<-time(SourceMat)
ResultMat<-matrix(NA, ncol=colnum, nrow=rownum) #creates an o/p
template matrix
#loop through each column in the source matrix:
for (i in 1:5000)
{
sel_col<-[col(SourceMat)==i] #selecting the correct column in the
matrix in turn
SourceMat[,i]<-coef(lm(tt~sel_col), na.action=NULL)
}
Thanks,
rcoder
Gabor Grothendieck wrote:
Read the last line of every message to r-help.
On Tue, Jul 29, 2008 at 6:15 PM, rcoder <[EMAIL PROTECTED]> wrote:
Hi everyone,
I am trying to apply linear regression to adjacent columns in a matrix
(i.e.
col1~col2; col3~col4; etc.). The columns in my matrix come with
identifiers
at the top of each column, but when I try to use these identifiers to
reference the columns in the regression function using rollapply(), the
columns are not recognised and the regression breaks down. Is there a
more
robust way to reference the columns I need, so that I can apply the
regression across the matrix; 'by.column', but every other column?
Thanks,
rcoder
--
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