The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
library("fdaPOIFD")
# auxiliary functions to generate Gaussian processes
Cov_exponential <- function(X1, X2, alpha = NULL, beta = NULL){
x.aux <- expand.grid(i = X1, j = X2)
cov <- alpha*exp(-beta*abs(x.aux$i - x.aux$j))
Sigma <- matrix(cov, nrow = length(X1))
return(Sigma)
}
Cov_Periodic <- function(X1, X2, sigma = NULL, p = NULL, l = NULL) {
#p = period, l = wiggles, sigma = noise
Sigma <- matrix(rep(0, length(X1)*length(X2)), nrow=length(X1))
for (i in 1:nrow(Sigma)) {
for (j in 1:ncol(Sigma)) {
Sigma[i,j] <- sigma*exp(-(2*(sin(pi*abs(X1[i]-X2[j])/(p)))^2)/(l^2))
}
}
return(Sigma)
}n <- 100
p <- 200
#parameters
time_grid <- seq(0, 1, length.out = p)
sigmaPeriodic <- Cov_Periodic(time_grid, time_grid, sigma = 3, p = 1 , l = 0.5)
sigmaExpo <- Cov_exponential(time_grid, time_grid, alpha = 0.5, beta = 5)
# Generate the random mean
centerline <- MASS::mvrnorm(1, rep(0, p), sigmaPeriodic)
# Generate the random sample
dataY <- FastGP::rcpp_rmvnorm(n, sigmaExpo, centerline)
data <- t(dataY)
colnames(data) <- as.character(c(1:n))
rownames(data) <- round(time_grid, digits=5)
dataPOFD <- intervalPOFD(data, observability = 0.5, ninterval = 4, pIncomplete = 0.75)Notice that the function POIFD on fully observed data coincides with the unweighted IFD.
Install and load the Rpackage refund.
library("refund")
Fit.IV <- ccb.fpc(t(dataPOFD$pofd))
goldsmith_reconstruction <- t(Fit.IV$Yhat)
depth_goldsmith <- POIFD(goldsmith_reconstruction, type = "MBD")Notice that the function POIFD on reconstructed data coincides with the unweighted IFD.
Kraus (2015) was implemented using the function pred.missfd obtained from the code at https://is.muni.cz/www/david.kraus/web_files/papers/partial_fda_code.zip.
Liebl (2020) was implemented using the function reconstructKneipLiebl obtained from the code at https://github.com/lidom/ReconstPoFD
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.