Package 'mvInterpolation'

Generate R documentation from inline comments.

Description

Roxygen2 allows you to write documentation in comment blocks co-located with code.

Details

The only function Otherwise refer to the vignettes to see how to format the documentation.

Author(s)

Andreas Mändle, [email protected]

References

Shepard, Donald (1968). "A two-dimensional interpolation function for irregularly-spaced data". Proceedings of the 1968 ACM National Conference. pp. 517–524. doi:10.1145/800186.810616.

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Get cosinus of pairwise angles between data points and interpolation point as fixed point

Description

This function is basically a helper function for mvInterpolation::ti_get and not intended for direct use.

Usage

cosangmx(data, xinp, distance = NULL)

Arguments

data	A matrix of data points
xinp	Coordinates of the fixed point of the angles; usually the point where interpolation has to be computed
distance	Distances between xinp and data can be given as an optional argument, if they have been coputed before, such that a new computation is not necessary

Value

The cosini of the pairwise angles between two data points with fixed point xinp

Examples

coords <- matrix(runif(10*4)*10, ncol=4)
cosangmx(coords,rep(4,4))

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Get weights for interpolation by inverse distance weighting

Description

Interpolation weights for inverse distance weighting.

Usage

di_get(data, xinp, p = 2)

Arguments

data	A matrix of the points for which the function is known
xinp	Coordinates, where interpolation has to be computed
p	additional parameter as in the Shepard paper

Value

The weights for the IDW interpolation

Examples

mydata <- matrix(runif(10*4)*10, ncol=4)
mydata <- cbind(mydata,abs(apply(mydata,1,sum)-3),abs(apply(mydata,1,prod)-4))
di_get(mydata, c(4,4,4,4))

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Get initial search-radius

Description

This function determines a search radius such that on average 7 points lie within the ball with the radius.

Usage

getR(data, d = NULL)

Arguments

data	A matrix of the points for which the function is known
d	Dimension of the coordinate space

Value

The radius as a numeric

Examples

coords <- matrix(runif(10*4)*10, ncol=4)
getR(coords)

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Get weights for interpolation by inverse distance weighting (IDW) for nearest neighbors

Description

Interpolation weights for inverse distance weighting of the nearest neighbors (4 to 10 neighbors, depending on the distance)

Usage

si_get(data, xinp, rad = getR)

Arguments

data	A matrix of the points for which the function is known
xinp	Coordinates, where interpolation has to be computed
rad	Function which gives the initial radius for the observations considered in the interpolation

Value

The weights for the IDW interpolation based on the nearest neighbors

Examples

mydata <- matrix(runif(10*4)*10, ncol=4)
mydata <- cbind(mydata,abs(apply(mydata,1,sum)-3),abs(apply(mydata,1,prod)-4))
si_get(mydata, c(4,4,4,4))

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Get drift based on slopes

Description

The drift is needed for interpolation by angular distance weighting extended by slopes. Interpolation weights are based on the nearest neighbors (4 to 10 neighbors, depending on the distance).

Usage

slopezi_get(data, xinp, rad = getR, vparam = 0.1)

Arguments

data	A matrix of the points for which the function is known
xinp	Coordinates, where interpolation has to be computed
rad	Function which gives the initial radius for the observations considered in the interpolation
vparam	Scalar, parameter for the slopes

Value

The weights for the ADW interpolation considering slopes and based on the nearest neighbors

Examples

mydata <- matrix(runif(10*4)*10, ncol=4)
mydata <- cbind(mydata,abs(apply(mydata,1,sum)-3),abs(apply(mydata,1,prod)-4))
slopezi_get(mydata, c(4,4,4,4))

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Get weights for interpolation by angular distance weighting (ADW)

Description

Interpolation weights for angular distance weighting of the nearest neighbors (4 to 10 neighbors, depending on the distance)

Usage

ti_get(data, xinp, rad = getR)

Arguments

data	A matrix of the points for which the function is known
xinp	Coordinates, where interpolation has to be computed
rad	Function which gives the initial radius for the observations considered in the interpolation

Value

The weights for the ADW interpolation based on the nearest neighbors

Examples

mydata <- matrix(runif(10*4)*10, ncol=4)
mydata <- cbind(mydata,abs(apply(mydata,1,sum)-3),abs(apply(mydata,1,prod)-4))
ti_get(mydata, c(4,4,4,4))

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Do the multivaiate interpolation based on weights and drift functions

Description

The drift is needed for interpolation by angular distance weighting extended by slopes. Interpolation weights include either all observations or are based on the nearest neighbors (4 to 10 neighbors, depending on the distance).

Usage

wintpl(data, xinp, weights, drift = 0, p = NULL, vparam = NULL)

Arguments

data	A matrix of the points for which the function is known
xinp	Coordinates, where interpolation has to be computed
weights	Vector of the weights for the interpolation
drift	Matrix of the drift for the interpolation
p	Scalar, parameter for the weights, currently not used
vparam	Scalar, parameter for the slopes, currently not used

Details

The interpoliation works for a multidimensional input space and also for multi-responses.

Value

The interpolated value(s) at xinp

Examples

mydata <- matrix(runif(10*4)*10, ncol=4)
mydata <- cbind(mydata,abs(apply(mydata,1,sum)-3),abs(apply(mydata,1,prod)-4))
inp <- rep(4,4)
#Inverse distance weighting (based on all observations)
wintpl(data=mydata, xinp=inp, weights=di_get(data=mydata, xinp=inp,2))
#Inverse distance weighting (based on nearest neighbors)
wintpl(data=mydata, xinp=inp, weights=si_get(data=mydata, xinp=inp)^2)
#Angular distance weighting
wintpl(data=mydata, xinp=inp, weights=ti_get(data=mydata, xinp=inp))
#Angular distance weighting under consideration of slopes
wintpl(data=mydata, xinp=inp, weights=ti_get(data=mydata, xinp=inp), drift=slopezi_get(mydata,inp))

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