Hi.

I need to apply run a regression analysis for groups of data of fixed length:100 As, 100 Bs, 100 Cs etc.

eg

x
Key     Value
A       1
A       21.2
A       4
A       6.5
...repeat 96 times with differing values of A
B       1
B       2.3
B       NA
B       6.5
...repeat 96 times with differing values of B
etc

I run these against a linear model using tapply(data$Value, data$Key,FUN=regr,100) where
regr<-function(x,w)
{
        #run the model against the last w values of x
        lm((x[length(x)-w):length(x)]~myModel(w))
}
 In the results, I want to return NA for any Key group where one or more of the values is NA.  If I run the above I get a regression structure ignoring the missing values and returning values for data that contains NA.  Using na.action=na.fail or na.action=NULL causes the whole tapply function to fail and I get nothing.  Is there a way I can get lm to return NA if any of the values in the data are NA but valid numbers for complete data?

I realise that I could remove the groups with NAs but I'm running the regressions over multiple time periods and most of the data groups will have a full complement of data for at least some of these periods. It becomes a pain to manage NAs if I do that.

Sorry if the above is a little unclear.

Thanks

Neil


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