[R] SoS! How to predict new values using linear regression models?

[Gabor Grothendieck]

Leaving aside the issue of whether linear regression is appropriate here,
do it like this where I have used the builtin iris data frame since I don't have
access to your ss:

iris.lm <- lm(as.numeric(Species) ~ Sepal.Length + Sepal.Width, iris)
predict(iris.lm, data.frame(Sepal.Length = 3, Sepal.Width = 2))

On 1/29/06, Michael <comtech.usa at gmail.com> wrote:
> Hi all,
>
> After trial and error by myself for a few hours, I decide to ask for your
> help.
>
> I have a training set which is a matrix of size 200 x 2, where the two
> columns denote each independent variable. I have 200 observations.
>
> -----------------
> ss=data.frame(trainingSet);
> result=lm(trainingClass~ss$X1+ss$X2);
> -----------------
>
> where trainingClass denotes the true classes of the training data.
>
> Now I want to apply the model to predict new data:
>
> -----------------
> > gg=predict(result, data.frame(X1=1, X2=2))
> Warning message:
> 'newdata' had 1 rows but variable(s) found have 200 rows
> -----------------
>
> That's to say, I provide a new data which is one observation of 2
> independent variables(1 row, two columns). I converted it into data frame.
>
> However, the R never gives me new predication value for this NEW ONE
> observation. Instead, it keeps giving me the above warning and keeps
> printing the fitted value for the 200 training samples...
>
> That's very bad.
>
> Please help me!
>
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>
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