Predict function in R not giving an interval

January 17, 2023

I’m trying to use predict() in R to compute a prediction interval for a linear model. When I tried this on a simpler model with only one covariate, it gave the expected output of a point estimate with a confidence interval. When I added a categorical predictor to the model, the predict() output gives what seems like a single-point estimate with no interval. I’ve Googled to no avail. Can anyone tell me what I’ve done wrong here?

medcost <- data.frame(
  ID = c(1:100),
  charges = sample(0:100000, 100, replace = T),
  bmi = sample(18:40, 100, replace = T),
  smoker = factor(sample(c("smoker", "nonsmoker"), 100, replace = TRUE))
)

mod2 <- glm(charges ~ bmi + smoker, data = medcost)

predict(mod2, interval="predict",
        newdata = data.frame(bmi=c(29, 31.5), smoker=c("smoker", "smoker")))

>Solution :

If you want to have the standard error, you could use se.fit = TRUE like this:

mod2 <- glm(charges ~ bmi + smoker, data = medcost)
predict(mod2, interval="predict",
        newdata = data.frame(bmi=c(29, 31.5), smoker=c("smoker", "smoker")),
        se.fit = TRUE)
#> $fit
#>        1        2 
#> 47638.66 47106.14 
#> 
#> $se.fit
#>        1        2 
#> 4304.220 4475.473 
#> 
#> $residual.scale
#> [1] 28850.85

^{Created on 2023-01-17 with reprex v2.0.2}

I would recommend you having a look at this post: R: glm(…,family=poisson) plot confidence and prediction intervals