R - How to predict values from an artificial data

Question

I'm getting a bit confused on how can I predict values from artificial data, so here's my problem.

I'm trying to do simple linear regression (predict) with the following data:

set.seed(1)
x.train<-runif(1000,0,2)
eps.train<-rnorm(1000,sd=0.1)
y.train<-sin(x.train)+eps.train
model<-lm(y.train~x.train)
confint(modelo,level=0.95)

So now, I think I must do something like:

set.seed(16)
x.test<-data.frame(runif(100,0,2))
eps.test<-rnorm(100,sd=0.1)
y.test<-sin(x.test)+eps.test
linear_prediction<-predict(model, x.test, interval="prediction")

For clarify things, I want to predict with test data of size 100 from the "original" data of size 1000.

I know I'm doing something wrong in the second part of my code, but I can't solve it myself. I'll appreciate all the help. Thanks in advance.

Answer 1

The variable in your linear regression model is called x.train . For example, printing your model gives,

model

Call:
lm(formula = y.train ~ x.train)

Coefficients:
(Intercept)      x.train  
     0.2246       0.4809  

But, while passing the testdata , the variable name is runif.100..0..2. . To avoid the warning message just change the variable name in your test data and rerun the predictions.

colnames(x.test) = c("x.train") 
linear_prediction<-predict(model, x.test, interval="prediction")