Using predict to find values of non-linear model

Question

I'm trying the next code to try to see if predict can help me to find the values of the dependent variable for a polynomial of order 2, in this case it is obvious y=x^2:

x <- c(1, 2, 3, 4, 5 , 6)
y <- c(1, 4, 9, 16, 25, 36)
mypol <- lm(y ~ poly(x, 2, raw=TRUE))

> mypol

Call:
lm(formula = y ~ poly(x, 2, raw = TRUE))

Coefficients:
            (Intercept)  poly(x, 2, raw = TRUE)1  poly(x, 2, raw = TRUE)2  
                      0                        0                        1  

If I try to find the value of x=7, I get this:

> predict(mypol, 7)
Error in eval(predvars, data, env) : not that many frames on the stack

What am I doing wrong?

Answer 1

If you read the help for predict.lm, you will see that it takes a number of arguments including newdata

newdata -- An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.

predict(mypol, newdata = data.frame(x=7))