[R] Multiple regression in R

[Rui Barradas]

Hello,

lm() is designed to work with data.frames, not with matrices. You can 
change your code to something like

dat <- data.frame(price, pred1 = c(5,6,3,4,5), pred2 = c(2,1,8,5,6))
fit <- lm(price ~ pred1 + pred2, data = dat)

and then use the fitted model to do predictions. You don't have to give 
the new values in a matrix, you can give them as vectors of a data.frame.

predict(fit, data.frame(pred1 = 1:3, pred2 = 3:5))


Hope this helps,

Rui Barradas

Em 29-05-2014 21:38, Safiye Celik escreveu:
> 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!
>