adamethods: do_clean_multiv – R documentation

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

do_clean_multiv

Cleaning multivariate functional outliers

Description

Cleaning of the most remarkable multivariate functional outliers. This improves the performance of the archetypoid algorithm since it is not affected by spurious points.

Usage

do_clean_multiv(data, num_pts, range = 1.5, out_perc = 80, nbasis, nvars)

Arguments

`data`	Data frame with (temporal) points in the rows and observations in the columns.
`num_pts`	Number of temporal points.
`range`	Same parameter as in function `boxplot`. A value of 1.5 is enough to detect amplitude and shift outliers, while a value of 3 is needed to detect isolated outliers.
`out_perc`	Minimum number of temporal points (in percentage) to consider the observation as an outlier. Needed when `range=1.5`.
`nbasis`	Number of basis.
`nvars`	Number of variables.

Value

List with the outliers for each variable.

Author(s)

Irene Epifanio

Examples

## Not run: 
library(fda)
?growth
str(growth)
hgtm <- growth$hgtm
hgtf <- growth$hgtf[,1:39]

# Create array:
nvars <- 2
data.array <- array(0, dim = c(dim(hgtm), nvars))
data.array[,,1] <- as.matrix(hgtm)
data.array[,,2] <- as.matrix(hgtf)
rownames(data.array) <- 1:nrow(hgtm)
colnames(data.array) <- colnames(hgtm)
str(data.array)

# Create basis:
nbasis <- 10
basis_fd <- create.bspline.basis(c(1,nrow(hgtm)), nbasis)
PM <- eval.penalty(basis_fd)
# Make fd object:
temp_points <- 1:nrow(hgtm)
temp_fd <- Data2fd(argvals = temp_points, y = data.array, basisobj = basis_fd)

X <- array(0, dim = c(dim(t(temp_fd$coefs[,,1])), nvars))
X[,,1] <- t(temp_fd$coef[,,1]) 
X[,,2] <- t(temp_fd$coef[,,2])

# Standardize the variables:
Xs <- X
Xs[,,1] <- scale(X[,,1])
Xs[,,2] <- scale(X[,,2])

x1 <- t(Xs[,,1]) 
for (i in 2:nvars) { 
 x12 <- t(Xs[,,i]) 
 x1 <- rbind(x1, x12) 
}
data_all <- t(x1) 

num_pts <- ncol(data_all) / nvars
range <- 3 
outl <- do_clean_multiv(t(data_all), num_pts, range, out_perc, nbasis, nvars)
outl

## End(Not run)

adamethods

Archetypoid Algorithms and Anomaly Detection

v1.2.1

GPL (>= 2)

Authors

Guillermo Vinue, Irene Epifanio

Initial release

2020-08-04