Description Usage Arguments Value Author(s) References Examples
View source: R/stepArchetypesRawData_funct_multiv_robust.R
This is a slight modification of stepArchetypesRawData
to use the functional archetype algorithm with the multivariate Frobenius norm.
1 2 | stepArchetypesRawData_funct_multiv_robust(data, numArch, numRep = 3,
verbose = TRUE, saveHistory = FALSE, PM, prob, nbasis, nvars)
|
data |
Data to obtain archetypes. |
numArch |
Number of archetypes to compute, from 1 to |
numRep |
For each |
verbose |
If TRUE, the progress during execution is shown. |
saveHistory |
Save execution steps. |
PM |
Penalty matrix obtained with |
prob |
Probability with values in [0,1]. |
nbasis |
Number of basis. |
nvars |
Number of variables. |
A list with the archetypes.
Irene Epifanio
Cutler, A. and Breiman, L., Archetypal Analysis. Technometrics, 1994, 36(4), 338-347, https://doi.org/10.2307/1269949
Epifanio, I., Functional archetype and archetypoid analysis, 2016. Computational Statistics and Data Analysis 104, 24-34, https://doi.org/10.1016/j.csda.2016.06.007
Eugster, M.J.A. and Leisch, F., From Spider-Man to Hero - Archetypal Analysis in R, 2009. Journal of Statistical Software 30(8), 1-23, https://doi.org/10.18637/jss.v030.i08
Moliner, J. and Epifanio, I., Robust multivariate and functional archetypal analysis with application to financial time series analysis, 2019. Physica A: Statistical Mechanics and its Applications 519, 195-208. https://doi.org/10.1016/j.physa.2018.12.036
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | ## 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])
lass <- stepArchetypesRawData_funct_multiv_robust(data = Xs, numArch = 3,
numRep = 5, verbose = FALSE,
saveHistory = FALSE, PM, prob = 0.8,
nbasis, nvars)
str(lass)
length(lass[[1]])
class(lass[[1]])
class(lass[[1]][[5]])
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.