adamethods (version 1.2.1)

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.

See Also

boxplot

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
# }
# NOT RUN {
                  
# }