NOIS_fit: Fitting NOIS to data

In hoangtt1989/NOIS: Non-parametric Outlier Identification and Smoothing

Description

Fit NOIS to data and return a NOIS_fit object

Usage

NOIS_fit(data, x = "x", y = "y", CV_method = "LOOCV", first_h = NULL,
  pool_h = NULL, local_q = 0.1, pool_q = 0.1, tol = 1e-07,
  maxit = 200, ...)

Arguments

data	A data.frame.
x	The name of the column in data containing the 'x' values.
y	The name of the column in data containing the 'y' values.
CV_method	The type of cross-validation to use. Possible types are c('MCV', 'LOOCV', 'PCV', 'none').
first_h	Bandwidth for first model fit. Only used when CV_method = 'none'. Default value is the theoretically optimal bandwidth of n^{-1/5}.
pool_h	Bandwidth for pooled model fit. Only used when CV_method = 'none'. Default value is the theoretically optimal bandwidth of n^{-1/5}.
local_q	Fraction of points detected as outliers for each unpooled fit.
pool_q	Pooled outlier detection. For numerics \in (0, 1), this is the fraction of points detected as outliers. For integers ≥ 1 this is the number of points detected as outliers.
tol	Tolerance for each unpooled fit.
maxit	Maximum number of iterations for each individual kernel smoothing fit.
...	Additional arguments passed to the CV_method.

Value

An object of class 'NOIS_fit' that is a list with the following components.

fit_df	A data_frame with columns specifying the original 'x' and 'y' values, the pooled adjusted 'y' values, a logical indicating whether the observation is an outlier, the pooled non-bias corrected and bias corrected fits, and the non-robust non-bias corrected and bias corrected fits.
local_fit	Unpooled NOIS fits.
local_gamma	The unpooled γ estimates.
pool_gamma	The pooled γ estimate.
local_q	Fraction of points detected as outliers for each unpooled fit.
pool_q	Pooled outlier detection. For numerics \in (0, 1), this is the fraction of points detected as outliers. For integers ≥ 1 this is the number of points detected as outliers.
pool_nonout	The positions of the clean observations.
codeCV	A list with cross-validation information.
codeconv	A list with convergence information.

See Also

Other NOIS CV functions: LOOCV_grid, MCV_grid, PCV_grid

Examples

###generate some random data and introduce outliers
set.seed(123)
npts <- 100
nout <- floor(.1*npts)
xt <- seq(from=0, to=2*pi, length.out=npts)
gaussnoise <- rnorm(npts)
outliers <- sample(floor(npts/2):npts, size=nout)
randpts <- runif(nout, min=5, max=7)
yt <- sin(xt) + gaussnoise
yt[outliers] <- yt[outliers] + randpts
sine_data <- data.frame(x = xt, y = yt)
###fit NOIS to this data
sine_fit <- NOIS_fit(sine_data, 'x', 'y', pool_q = .1, CV_method = 'LOOCV')

hoangtt1989/NOIS documentation built on May 20, 2019, 2:08 p.m.