Package 'FracKrigingR'

Title: Spatial Multivariate Data Modeling
Description: Aim is to provide fractional Brownian vector field generation algorithm, Hurst parameter estimation method and fractional kriging model for multivariate data modeling.
Authors: Neringa Urbonaite [aut, cre], Leonidas Sakalauskas [aut]
Maintainer: Neringa Urbonaite <[email protected]>
License: GPL-2
Version: 1.0.0
Built: 2024-11-09 03:06:56 UTC
Source: https://github.com/nidagreen/frackriging

Help Index

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

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))

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

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

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)