replacement has *** rows, data has ***

This can happen if there are rows containing missing values (NA's) in the
data used to fit the model.  Use na.action=na.exclude when fitting the
model instead of the default na.action=na.omit to make the prediction
vector line up with the input data instead of lining up with the input data
after the NA-rows have been dropped.

E.g.,

d <- data.frame(y=1:10, x=log2(c(1,2,NA,4:10)))
fitDefault <- lm(y ~ x, data=d)
fitNaExclude <- lm(y ~ x, data=d, na.action=na.exclude)
length(predict(fitDefault))

[1] 9

length(predict(fitNaExclude))

[1] 10

predict(fitNaExclude)

1          2          3          4          5          6
 7          8          9         10
-0.2041631  2.4602537         NA  5.1246704  5.9824210  6.6832543
 7.2758004  7.7890872  8.2418382  8.6468378


Bill Dunlap
TIBCO Software
wdunlap tibco.com

Hi all,

I created a predicted variable based on a model, but somehow not all
observations have a predicted value. When I tired to add the predicated
value to the main data set (data$pr <- pr) , it said:
replacement has 34333 rows, data has 34347.
Do you know how to solve that?

Thanks,
Manqing

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