Page 437 - Applied Statistics with R
P. 437
17.3. WORKING WITH LOGISTIC REGRESSION 437
new_obs = data.frame(
sbp = 148.0,
tobacco = 5,
ldl = 12,
adiposity = 31.23,
famhist = "Present",
typea = 47,
obesity = 28.50,
alcohol = 23.89,
age = 60
)
Fist, we’ll use the predict() function to obtain ̂(x) for this observation.
eta_hat = predict(chd_mod_selected, new_obs, se.fit = TRUE, type = "link")
eta_hat
## $fit
## 1
## 1.579545
##
## $se.fit
## [1] 0.4114796
##
## $residual.scale
## [1] 1
By setting se.fit = TRUE, R also computes SE[ ̂(x)]. Note that we used type
= "link", but this is actually a default value. We added it here to stress that
the output from predict() will be the value of the link function.
z_crit = round(qnorm(0.975), 2)
round(z_crit, 2)
## [1] 1.96
After obtaining the correct critical value, we can easily create a 95% confidence
interval for (x).
eta_hat$fit + c(-1, 1) * z_crit * eta_hat$se.fit
## [1] 0.773045 2.386045
