kriging: Ordinary Kriging

In kriging: Ordinary Kriging

Ordinary Kriging

Description

Simple and highly optimized ordinary kriging algorithm to plot geographical data

Usage

kriging(x, y, response, model = "spherical", lags = 10, pixels = 100, polygons = NULL)

Arguments

x	vector of x-axis spatial points.
y	vector of y-axis spatial points.
response	vector of observed values.
model	specification of the variogram model. Choices are "spherical", "exponential" or "gaussian". Defaults to "spherical".
lags	number of lags. Defaults to 10.
pixels	maximum number of points along either axis. Defaults to 100.
polygons	list of polygons used to grid predicted values on to. The default value of NULL automatically generates an evenly spaced out rectangular grid of points spanning the range of the data.

Details

The kriging algorithm assumes a minimum number of observations in order to fit the variogram model.

Value

An object of class kriging that inherits from list and is composed of:

model	character; variogram model.
nugget	numeric; value of nugget parameter.
range	numeric; value of range parameter.
sill	numeric; value of sill parameter.
map	data.frame; contains the predicted values along with the coordinate covariates.
semivariogram	data.frame; contains the distance and semivariance values.

Author(s)

Omar E. Olmedo

See Also

image.kriging, plot.kriging.

Examples

# Krige random data for a specified area using a list of polygons
library(maps)
usa <- map("usa", "main", plot = FALSE)
p <- list(data.frame(usa$x, usa$y))

# Create some random data
x <- runif(50, min(p[[1]][,1]), max(p[[1]][,1]))
y <- runif(50, min(p[[1]][,2]), max(p[[1]][,2]))
z <- rnorm(50)

# Krige and create the map
kriged <- kriging(x, y, z, polygons=p, pixels=300)
image(kriged, xlim = extendrange(x), ylim = extendrange(y))

kriging documentation built on June 25, 2022, 1:06 a.m.