Andreas Wittmann wrote:
Sorry for my bad description, i don't want get a constructed algorithm without
own work. i only hoped to get some advice how to do this. i don't want to
predict any sort of data, i reference only to newdata which variables are the
same as in the model data. But if factors in the data than i can by possibly
that the newdata has a level which doesn't exist in the original data.
So i have to compare each factor in the data and in the newdata and if the newdata has a levels which is not in the original data and drop this variable and do compute the model and prediction again.
I thought this problem is quite common and i can use an algorithm somebody has already implemented.
best regards
Andreas
If I understand correctly, you want to build a model that
includes at least one factor predictor (say xf with k levels).
Then you want to use this model to predict a response value
when xf takes a _new_ level about which the model knows
nothing. That doesn't make sense to me, so I doubt that
it's a common problem. Introducing a new level for a factor
variable is just like introducing a new variable.
-Peter Ehlers
-------- Original-Nachricht --------
Datum: Wed, 25 Nov 2009 00:48:59 -0500
Von: David Winsemius <dwinsem...@comcast.net>
An: Andreas Wittmann <andreas_wittm...@gmx.de>
CC: r-help@r-project.org
Betreff: Re: [R] predict: remove columns with new levels automatically
On Nov 24, 2009, at 2:24 PM, Andreas Wittmann wrote:
Dear R-users,
in the follwing thread
http://tolstoy.newcastle.edu.au/R/help/03b/3322.html
the problem how to remove rows for predict that contain levels which
are not in the model.
now i try to do this the other way round and want to remove columns
(variables) in the model which will be later problematic with new
levels for prediction.
## example:
set.seed(0)
x <- rnorm(9)
y <- x + rnorm(9)
training <- data.frame(x=x, y=y, z=c(rep("A", 3), rep("B", 3),
rep("C", 3)))
test <- data.frame(x=t<-rnorm(1), y=t+rnorm(1), z="D")
lm1 <- lm(x ~ ., data=training)
## prediction does not work because the variable z has the new level
"D"
predict(lm1, test)
## solution: the variable z is removed from the model
## the prediction happens without using the information of variable z
lm2 <- lm(x ~ y, data=training)
predict(lm2, test)
How can i autmatically recognice this and calculate according to this?
Let me get this straight. You want us to predict in advance (or more
accurately design an algorithm that can see into the future and work
around) any sort of newdata you might later construct????
--
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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