R: Ordinary Kriging

kriging {kriging} | R Documentation

## 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))

[Package kriging version 1.1 Index]