[R] Multiple regression in R

[Sarah Goslee]

Hi,

I'd do it like this, making use of data frames and the data argument to lm.

traindata <- data.frame(price=price, predictor1=predictor1,
predictor2=predictor2)
testdata <- data.frame(predictor1=3, predictor2=5)
predict(lm(price ~ ., data=traindata), testdata)

Note that you don't have to specify all the predictors individually:
the . in the formula takes care of that.
Note also that ?predict.lm states that newdata should be a data frame;
I suspect trying to use a matrix is the cause of your problem.

Sarah

On Thu, May 29, 2014 at 4:38 PM, Safiye Celik <safisce at gmail.com> wrote:
> I want to perform a multiple regression in R and make predictions based on
> the trained model. Below is an example code I am using:
>
> price = c(10,18,18,11,17)
> predictors = cbind(c(5,6,3,4,5),c(2,1,8,5,6))
> predict(lm(price ~ predictors), data.frame(predictors=matrix(c(3,5),nrow=1)))
>
>  So, based on the 2-variate regression model trained by 5 samples, I want
> to make a prediction for the test data point where the first variate is 3
> and second variate is 5. But I get a warning from above code saying
> that 'newdata'
> had 1 rows but variable(s) found have 5 rows. How can I correct above code?
> Below code works fine where I give the variables separately to the model
> formula. But since I will have hundreds of variates, I have to give them in
> a matrix since it would be unfeasible to append hundreds of columns using +
>  sign.
>
> price = c(10,18,18,11,17)
> predictor1 = c(5,6,3,4,5)
> predictor2 = c(2,1,8,5,6)
> predict(lm(price ~ predictor1 + predictor2),
> data.frame(predictor1=3,predictor2=5))
>
>  Thanks in advance!
>
> --
> -safiye
>
>         [[alternative HTML version deleted]]
>
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-- 
Sarah Goslee
http://www.functionaldiversity.org