Worked Examples

Copy-first regression examples and longer scripts across np and related workflows.
Keywords

worked examples, npreg, regression examples, local linear, local constant

This page is meant to give you working code that you can copy directly. The longer scripts are still linked below, but the first goal is to let you run something useful without downloading a bundle of files.

A first regression example

This mirrors the spirit of regression_intro_a.R in a shorter form.

## Load the package data, then fit local-constant and local-linear models
library(np)
data(cps71, package = "np")

fit_lc <- npreg(logwage ~ age, data = cps71)
fit_ll <- npreg(logwage ~ age, data = cps71, regtype = "ll")

## Plot the first fit, then add the second fit and a legend
plot(fit_lc)
lines(cps71$age, fitted(fit_ll), col = 2, lty = 2)

legend("topleft",
  c("Local constant", "Local linear"),
  lty = c(1, 2),
  col = c(1, 2),
  bty = "n")

A small bandwidth-selection example

## Simulate a small mixed-data example and choose the bandwidth explicitly
library(np)

set.seed(42)
n <- 1000
x <- runif(n)
z1 <- rbinom(n, 1, 0.5)
z2 <- rbinom(n, 1, 0.5)
y <- cos(2 * pi * x) + z1 + rnorm(n, sd = 0.25)
z1 <- factor(z1)
z2 <- factor(z2)
mydat <- data.frame(y, x, z1, z2)

## Fit from the saved bandwidth object, then inspect both summaries
bw <- npregbw(y ~ x + z1 + z2, regtype = "ll", bwmethod = "cv.aic", data = mydat)
fit <- npreg(bws = bw, data = mydat)

summary(bw)
summary(fit)

Script library

For a website-first route to the multivariate example, see Multivariate Regression and Prediction.

Notes

  • The script links remain useful because they are more heavily commented than the short code blocks on this page.
  • Some examples call for additional packages. Where that is the case, the script itself says so.
  • The older npglpreg route is deprecated. For a functional replacement, use npreg(..., nomad = TRUE) in np or npRmpi.
  • The wage1 multivariate route now has its own page at Multivariate Regression and Prediction.
  • For plotting objects, gradients, and uncertainty summaries, see Plotting and Intervals.
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