Hello,
As for the first question, predict.lm with new data uses the formula
used in the fit so do not change the way you pass on your new data. If
the formula was Y ~ X1 + X2 you can use
newdata = data.frame(New1, New2)
newdata = data.frame(cbind(New1, New2))
but the order must be kept. (And why cbind, by the way?)
2. Use something folloing these lines. (Untested, obviously, without a
data example.)
pred <- predict(...etc...)
no_na <- complete.cases( cbind(matrix1, matrix2) )
matrix1[ no_na, ] <- pred[1, ]
matrix2[ no_na, ] <- pred[2, ]
Hope this helps,
Rui Barradas
Em 21-09-2012 17:09, frauke escreveu:
Thank you for the fast help!
I am not sure though if I understand the predict.lm business. The newdata
that I would make predictions from consists of six matrices, one for each
variable. Do I cbind the matrices like you suggest for the regression and
then convert them to a dataframe? How does R know which column in the matrix
I created with cbind belongs to which variable in the regression?
One last question. If I use predict.lm without newdata it gives me as many
data points as were used for the regression. However, in my matrices I have
rows of NA for days without observations. My problem is now that if
predict.lm only gives me the datapoints used for regression I can not match
them to the actual observations days anymore. Is there anyway of keeping the
original matrix and put the predicted value in it?
Thanks again, Frauke
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