My answers are going to be very similar but with minor cosmetic changes
that hopefully will make it bit more clearer.
1) How do you read in the data ? If you are using read.table (or
read.csv, read.delim, etc) you can set na.strings="-999" to take
advantage of the R's missing value features.
2) First count how many missing values. Then subset to the rows with at
least 6 numerical values:
number.present <- rowSums( myMatrix != -999 )
good.rows <- which( number.present >= 6 )
myMatrix.sub <- myMatrix[ good.rows, ]
Note : change the first line to rowSums( !is.na( myMatrix ) ) if you
have coded missing values properly as in comment 1).
Regards, Adai
On Thu, 2006-03-16 at 21:45 +0100, [EMAIL PROTECTED] wrote:
> Quoting mark salsburg <[EMAIL PROTECTED]>:
>
> > I am trying to find out if R can recognize specific criteria for removing
> > rows (i.e. a prexisting function)
> >
> > I have a matrix myMatrix that is 12000 by 20
> >
> > I would like to remove rows from myMatrix that have:
> >
> > -999 across all columns
> > -999 across all columns but one
> > -999 across all columns but two
> > -999 across all columns but three
> > -999 across all columns but four
> > -999 across all columns but five
> >
> > (-999 here is my missing value)
> >
> > Does R have a function for this, I've explored subset() so far
> >
>
> You can create a vector that records the number of missing values
> in each row
>
> n.notmissing <- apply(myMatrix != -999, 1, sum)
>
> then use row subsetting to remove the ones you don't want
>
> myMatrix[n.notmissing == n, ]
>
> for n = 0, 1, ... 5, etc.
>
> (As an aside, R functions will work better with your data if you use NA
> instead of a numeric code to represent missing data.)
>
> Martyn
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