| Type: | Package |
| Title: | Spatial Multivariate Data Modeling |
| Version: | 1.0.0 |
| Copyright: | Vilnius University Institute of Data Science and Digital Technologies |
| Author: | Neringa Urbonaite [aut, cre], Leonidas Sakalauskas [aut] |
| Maintainer: | Neringa Urbonaite <neringa.urbonaite@mif.vu.lt> |
| Description: | Aim is to provide fractional Brownian vector field generation algorithm, Hurst parameter estimation method and fractional kriging model for multivariate data modeling. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| URL: | https://github.com/NidaGreen/FracKriging |
| Imports: | psych, clusterGeneration, graphics, stats |
| Suggests: | knitr, gstat, sp, rmarkdown, raster |
| RoxygenNote: | 7.1.2 |
| NeedsCompilation: | no |
| Packaged: | 2021-11-05 13:42:32 UTC; nerin |
| Repository: | CRAN |
| Date/Publication: | 2021-11-08 08:40:08 UTC |
FracField
Description
Generates fractional Brownian vector field data
Usage
FracField(K, m, H, X)
Arguments
K |
number of observations |
m |
number of criteria |
H |
Hurst parameter (a real in interval [0,1)) |
X |
Coordinates |
Value
Returns a fractional Brownian vector field matrix.
Examples
# Load FracKrigingR library
library(FracKrigingR)
# generate Coordinates
p=2; K=10;
X<-matrix(0,ncol=p, nrow=K)
for(j in 1:p){
for(i in 1:K){
X[i,j] = rnorm(1, 0, 1)
}
}
# generate fractional Brownian vector field
H = 0.5; m = 3
FracField(K,m,H,X)
FracKrig
Description
Performs extrapolation for spatial multivariate data
Usage
FracKrig(X, Z, Xnew, H)
Arguments
X |
Coordinates |
Z |
observations |
Xnew |
Coordinates of points where the prognosis should be made |
H |
Hurst parameter (a real in interval [0,1)) |
Value
Returns a matrix of fractional kriging prognosis.
Examples
library(sp)
library(gstat)
data(meuse)
xy<-cbind(meuse$x,meuse$y)
X<-xy[1:50,]
min_max_norm <- function(x) {
(x - min(x)) / (max(x) - min(x))
}
normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x)))
}
dat<-cbind(meuse[3],meuse[4],meuse[5])
data<-dat[51:100,]
zz1 <- as.data.frame(lapply(dat, normalize))
data1=as.data.frame(lapply(as.data.frame(data), normalize))
Z<-as.matrix(zz1[1:50,])
library(FracKrigingR)
K<-50
#Hurst parameter estimation
H<-0.2
Xnew<-xy[51:100,]
results<- FracKrig(X,Z,Xnew,H)
denormalize <- function(x, bottom, top){
(top - bottom) * x + bottom
}
z1 = denormalize(
results[,1], top = max(data[,1]), bottom = min(data[,1])
)
z2 = denormalize(
results[,2], top = max(data[,2]), bottom = min(data[,2])
)
z3 = denormalize(
results[,3], top = max(data[,3]), bottom = min(data[,3])
)
RMSE<-function(z,prognosis){
rmse<-sqrt(((1/(length(z))))*sum((z-prognosis)^2))
rmse
}
Cd<-RMSE(data[,1],z1)
Cu<-RMSE(data[,2],z2)
Pb<-RMSE(data[,3],z3)
Cd
Cu
Pb
FracMatrix
Description
Fractional distance matrix
Usage
FracMatrix(H, K, X)
Arguments
H |
Hurst parameter (a real in interval [0,1)) |
K |
number of observations |
X |
Coordinates |
Value
Returns a fractional distance matrix, which depends on the Hurst parameter.
Examples
# Load FracKrigingR library
library(FracKrigingR)
#Fractional Brownian vector field
K = 10; H = 0.5; p = 2
#Generate coordinates
X<-matrix(0,ncol=p, nrow=K)
for(j in 1:p){
for(i in 1:K){
X[i,j] = rnorm(1, 0, 1)
}
}
FracMatrix(H, K, X)
MaxLikelihood
Description
Maximum likelihood method for Hurst parameter estimation of multivariate data
Usage
MaxLikelihood(X, Z)
Arguments
X |
Coordinates |
Z |
Observations |
Value
Returns the estimate of the Hurst parameter (a real in [0,1)) and a graph indicating the minimized maximum likelihood function with the Hurst parameter.
Examples
# Load FracKrigingR library
library(FracKrigingR)
# generate Coordinates
p<-2; K<-20;
X<-matrix(0,ncol=p, nrow=K)
for(j in 1:p){
for(i in 1:K){
X[i,j] = rnorm(1, 0, 1)
}
}
# generate fractional Brownian vector field
H <- 0.8; m <- 3
Z<-FracField(K,m,H,X)
# Hurst parameter estimation
MaxLikelihood(X,Z)