Search code examples
rsasstatalinear-regressionlsmeans

Estimate predicted value from linear model in R

Given a regression model:

y = b0 + b1(x)

where both x and y are continuous.

After fitting the model, I'd like to estimate the predicted mean and 95%CI of y when x is at a certain value, say, 100.

In Stata, it can be achieved with margins:

reg y x
margins, at (x = 100)

In SAS, it can be done with estimate:

proc glm;
model y = x / clparm solution;
estimate "Test x = 100" intercept 1 x 100;
run;

My question is: how to achieve the same action in R? I tried the lsmeans package but it seems it will not work if I don't have any categorical variables in my model.

Solution 
  • predict(fit,newdata=data.frame(x=100),interval="confidence")

    (I don't agree with @thelatemail's advice that prediction intervals are preferred; prediction intervals are specified if you want to allow for the residual error ...)