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Getting Started with np

Source: vignettes/np_getting_started.Rmd
np_getting_started.Rmd

This vignette is meant to be the smallest useful package-side introduction to np. The emphasis is on one clean workflow that users can run after installation: choose a bandwidth, fit a model, inspect the result, and plot it.

Broader worked examples, package comparisons, and method-specific articles are better carried by the gallery site:

The basic workflow

In np, the bandwidth object is often the key object in the analysis.

  1. compute or inspect a bandwidth object,
  2. fit the model,
  3. summarize or plot the result.

A simple regression example 

library(np)
#> np 0.70-4
#> Examples and guides at https://jeffreyracine.github.io/gallery/
#> See also vignette("np_getting_started", package = "np")
data(cps71, package = "np")

bw <- npregbw(logwage ~ age, data = cps71)
summary(bw)
#> 
#> Regression Data (205 observations, 1 variable(s)):
#> 
#> Regression Type: Local-Constant
#> Bandwidth Selection Method: Least Squares Cross-Validation
#> Formula: logwage ~ age
#> Bandwidth Type: Fixed
#> Objective Function Value: 0.316055 (achieved on multistart 1)
#> Number of Function Evaluations: 47
#> Powell cache: 17 repeated objective lookups avoided out of 41 (41.5%)
#> 
#> Exp. Var. Name: age Bandwidth: 1.892158  Scale Factor: 0.4487743
#> 
#> Continuous Kernel Type: Second-Order Gaussian
#> No. Continuous Explanatory Vars.: 1
#> Estimation Time: 0.268 seconds

fit <- npreg(bws = bw)
summary(fit)
#> 
#> Regression Data: 205 training points, in 1 variable(s)
#>                    age
#> Bandwidth(s): 1.892158
#> 
#> Kernel Regression Estimator: Local-Constant
#> Bandwidth Type: Fixed
#> Residual standard error: 0.5307943
#> R-squared: 0.3108675
#> 
#> Continuous Kernel Type: Second-Order Gaussian
#> No. Continuous Explanatory Vars.: 1
#> Estimation Time: 0.271 seconds (optim 0.268s, fit 0.003s)

Plotting the fitted relationship 

plot(cps71$age, cps71$logwage, cex = 0.25, col = "grey")
lines(cps71$age, fitted(fit), col = 2, lwd = 2)

Mixed data

One important feature of np is that it handles mixed data directly. Variable class matters: unordered categorical variables should be factors, and ordered categorical variables should be ordered factors when appropriate.

set.seed(42)
mydat <- data.frame(
  y = rnorm(200),
  x_cont = runif(200),
  x_unordered = factor(sample(c("a", "b", "c"), 200, replace = TRUE)),
  x_ordered = ordered(sample(1:4, 200, replace = TRUE))
)

bw_mixed <- npregbw(y ~ x_cont + x_unordered + x_ordered, data = mydat)
fit_mixed <- npreg(bws = bw_mixed)
summary(fit_mixed)
#> 
#> Regression Data: 200 training points, in 3 variable(s)
#> Search Parameter(s):
#>          x_cont  x_unordered  x_ordered
#> Type  Bandwidth       Lambda     Lambda
#> Value   1729347    0.6610069  0.9981613
#> Max          --    0.6666667          1
#> 
#> Kernel Regression Estimator: Local-Constant
#> Bandwidth Type: Fixed
#> Residual standard error: 0.9721457
#> R-squared: 0
#> 
#> Continuous Kernel Type: Second-Order Gaussian
#> No. Continuous Explanatory Vars.: 1
#> 
#> Unordered Categorical Kernel Type: Aitchison and Aitken
#> No. Unordered Categorical Explanatory Vars.: 1
#> 
#> Ordered Categorical Kernel Type: Li and Racine
#> No. Ordered Categorical Explanatory Vars.: 1
#> Estimation Time: 0.454 seconds (optim 0.452s, fit 0.002s)

For local-polynomial-capable methods, np now supports joint selection of polynomial order and bandwidth. The modern route is to use search.engine = "nomad+powell" when you want the search to choose both together.

If you want the recommended route without spelling out all of the LP tuning arguments, use nomad = TRUE. This is a documented convenience preset, not a generic optimizer alias: it fills only missing values among the LP degree-search controls and leaves compatible explicit overrides in place. This route uses the optional NOMAD backend provided by the suggested package crs, so install crs first if you want to use nomad = TRUE or search.engine = "nomad"/"nomad+powell".

if (requireNamespace("crs", quietly = TRUE) &&
    utils::packageVersion("crs") >= package_version("0.15-41")) {
  set.seed(7)
  n <- 120
  x <- runif(n, -1, 1)
  y <- x + 0.4 * x^2 + rnorm(n, sd = 0.18)

  fit_nomad <- npreg(y ~ x, nomad = TRUE, degree.max = 1L, nmulti = 1L)
  fit_nomad$bws$nomad.shortcut

  # Tune one component explicitly while leaving the rest of the preset in place.
  fit_nomad_direct <- npreg(
    y ~ x,
    nomad = TRUE,
    search.engine = "nomad",
    degree.max = 1L,
    nmulti = 1L
  )
}

The same convenience entry point is available for the other LP-capable families: npcdens, npcdist, npplreg, npscoef, and npindex, together with their corresponding *bw constructors.

Keep the first run modest and runnable. Fuller worked examples belong on the gallery rather than in this package vignette.

Data preparation matters

In np, the formula interface tells the function which variables are the response and regressors. It is not imposing an ordinary linear-additive model.

It is also important not to pass blocks of 0/1 dummies as if this were a standard linear-model workflow. If the underlying variable is categorical, it is usually better to keep it as one factor or ordered variable.

Other common starting points

This vignette keeps the package-side introduction intentionally narrow. Other common first routes are:

Those broader branches are better carried by help pages and website articles than by a single shipped vignette.

Where to go next