ibr: npregress – R documentation

Pricing

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Get Started for Free

Documentation

ibr

npregress

Local polynomials smoothing

Description

Predicted values from a local polynomials of degree less than 2.
Missing values are not allowed.

Usage

npregress(x, y, criterion="rmse", bandwidth=NULL,kernel="g",
             control.par=list(), cv.options=list())

Arguments

`x`	A numeric vector of explanatory variable of length n.
`y`	A numeric vector of variable to be explained of length n.
`criterion`	Character string. If the bandwidth (`bandwidth`) is missing or `NULL` the number of iterations is chosen using `criterion`. The criterion available is (cross-validated) rmse (`"rmse"`) and mean (relative) absolute error.
`bandwidth`	The kernel bandwidth smoothing parameter (a numeric vector of either length 1).
`kernel`	Character string which allows to choose between gaussian kernel (`"g"`), Epanechnikov (`"e"`), uniform (`"u"`), quartic (`"q"`).
`control.par`	A named list that control optional parameters. The two components are `bandwidth` for compatibility with `ibr` arguments and `degree` which controls the degree of the local polynomial regression. If argument `bandwidth` is not null or missing, its value is used instead `control.par$bandwidth`. `degree` must be smaller than 2. For (gaussian binned) local polynomial see `locpoly`
`cv.options`	A named list which controls the way to do cross validation with component `gridbw`, `ntest`, `ntrain`, `Kfold`, `type`, `seed`, `method` and `npermut`. `gridbw` is numeric vector which contains the search grid for optimal bandwidth (default to `1/n(1+1/n)^(0:kmax)`, with `kmax=floor(log(ndiff(range(x))/3)/log(1+1/n))`). `ntest` is the number of observations in test set and `ntrain` is the number of observations in training set. Actually, only one of these is needed the other can be `NULL` or missing. `Kfold` a boolean or an integer. If `Kfold` is `TRUE` then the number of fold is deduced from `ntest` (or `ntrain`). `type` is a character string in `random`,`timeseries`,`consecutive`, `interleaved` and give the type of segments. `seed` controls the seed of random generator. `npermut` is the number of random draws. If `cv.options` is `list()`, then component `ntest` is set to `1`, `type` is consecutive, `Kfold` is `TRUE`, and the other components are `NULL`, which leads to leave-one-out cross-validation.

Value

Returns an object of class npregress which is a list including:

`bandwidth`	The kernel bandwidth smoothing parameter.
`residuals`	Vector of residuals.
`fitted`	Vector of fitted values.
`df`	The effective degree of freedom of the smoother.
`call`	A list containing four components: `x` contains the initial explanatory variables, `y` contains the initial dependant variables, `criterion` contains the chosen criterion, `kernel` the kernel and `degree` the chosen degree
`criteria`	either a named list containing the bandwidth search grid and all the criteria (`rmse` and `mae`) evaluated on the grid `gridbw`. If the bandwidth `bandwidth` is given by the user `NULL` is returned

Note

See locpoly for fast binned implementation over an equally-spaced grid of local polynomial. See ibr for univariate and multivariate smoothing.

Author(s)

Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.

References

Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.

Examples

f <- function(x){sin(5*pi*x)}
n <- 100
x <- runif(n)
z <- f(x)
sigma2 <- 0.05*var(z)
erreur <- rnorm(n,0,sqrt(sigma2))
y <- z+erreur
res <- npregress(x,y,bandwidth=0.02)
summary(res)
ord <- order(x)
plot(x,y)
lines(x[ord],predict(res)[ord])

ibr

Iterative Bias Reduction

v2.0-3

GPL (>= 2)

Authors

Pierre-Andre Cornillon, Nicolas Hengartner, Eric Matzner-Lober

Initial release

2017-04-28