interp: Interpolation function

Description

This function implements bivariate interpolation for irregularly spaced input data. Piecewise linear (=barycentric interpolation), bilinear or bicubic spline interpolation according to Akimas method is applied.

Usage

interp(x, y = NULL, z, xo = seq(min(x), max(x), length = nx),
       yo = seq(min(y), max(y), length = ny),
       linear = (method == "linear"), extrap = FALSE,
       duplicate = "error", dupfun = NULL,
       nx = 40, ny = 40, input="points", output = "grid",
       method = "linear", deltri = "shull", h=0,
       kernel="gaussian", solver="QR", degree=3,
       baryweight=TRUE, autodegree=FALSE, adtol=0.1,
       smoothpde=FALSE, akimaweight=TRUE, nweight=25,
       na.rm=FALSE)

Value

a list with 3 components:

x,y

If output="grid": vectors of $x$ - and $y$ -coordinates of output grid, the same as the input argument xo, or yo, if present. Otherwise, their default, a vector 40 points evenly spaced over the range of the input x and y.

If output="points": vectors of $x$ - and $y$ -coordinates of output points as given by xo and yo.

z

If output="grid": matrix of fitted $z$ -values. The value z[i,j] is computed at the point $(x o [i], y o [j])$ . z has dimensions length(xo) times length(yo).

If output="points": a vector with the calculated z values for the output points as given by xo and yo.

If the input was a SpatialPointsDataFrame a SpatialPixelsDataFrame is returned for output="grid" and a SpatialPointsDataFrame for output="points".

Arguments

x

vector of $x$ -coordinates of data points or a SpatialPointsDataFrame object (a regular gridded SpatialPixelsDataFrame is also allowed). In this case also an sp data object will be returned. Missing values are not accepted.

y

vector of $y$ -coordinates of data points. Missing values are not accepted.

If left as NULL indicates that x should be a SpatialPointsDataFrame and z names the variable of interest in this dataframe.

z

vector of $z$ -values at data points or a character variable naming the variable of interest in the SpatialPointsDataFrame x.

Missing values are not accepted by default, see parameter na.rm.

x, y, and z must be the same length (execpt if x is a SpatialPointsDataFrame) and may contain no fewer than four points. The points of x and y should not be collinear if input="grid", as the underlying triangulation in these cases sometimes fails.

interp is meant for cases in which you have $x$ , $y$ values scattered over a plane and a $z$ value for each. If, instead, you are trying to evaluate a mathematical function, or get a graphical interpretation of relationships that can be described by a polynomial, try outer.

xo

If output="grid" (which is the default): sequence of $x$ locations for rectangular output grid, defaults to nx points between min(x) and max(x).

If output="points": vector of $x$ locations for output points.

yo

If output="grid" (default): sequence of $y$ locations for rectangular output grid, defaults to ny points between min(y) and max(y).

If output="points": vector of $y$ locations for output points. In this case it has to be same length as xo.

input

text, possible values are "grid" (not yet implemented) and "points" (default).

This is used to distinguish between regular and irregular gridded input data.

output

text, possible values are "grid" (=default) and "points".

If "grid" is choosen then xo and yo are interpreted as vectors spanning a rectangular grid of points $(x o [i], y o [j])$ , $i = 1, . . ., n x$ , $j = 1, . . ., n y$ . This default behaviour matches how akima::interp works.

In the case of "points" xo and yo have to be of same length and are taken as possibly irregular spaced output points $(x o [i], y o [i])$ , $i = 1, . . ., n o$ with no=length(xo). nx and ny are ignored in this case. This case is meant as replacement for the pointwise interpolation done by akima::interpp. If the input x is a SpatialPointsDataFrame and output="points" then xo has to be a SpatialPointsDataFrame, yo will be ignored.

linear

logical, only for backward compatibility with akima::interp, indicates if piecewise linear interpolation or Akima splines should be used.

Please use the new method argument instead!

method

text, possible methods are "linear" (piecewise linear interpolation within the triangles of the Delaunay triangulation, also referred to as barycentric interpolation based on barycentric coordinates) and "akima" (a reimplementation for Akimas spline algorithms for irregular gridded data with the accuracy of a bicubic polynomial).

method="bilinear" is only applicable to regular grids (input="grid") and in turn calls bilinear, see there for more details.

method="linear" replaces the old linear argument of akima::interp.

extrap

logical, indicates if extrapolation outside the convex hull is intended, this will not work for piecewise linear interpolation!

duplicate

character string indicating how to handle duplicate data points. Possible values are

"error": produces an error message,

"strip"

remove duplicate z values,

"mean","median","user"

calculate mean , median or user defined function (dupfun) of duplicate $z$ values.

dupfun

a function, applied to duplicate points if duplicate= "user".

dimension of output grid in x direction

dimension of output grid in y direction

deltri

triangulation method used, this argument may later be moved into a control set together with others related to the spline interpolation! Possible values are "shull" (default, sweep hull algorithm) and "deldir" (uses packagedeldir).

bandwidth for partial derivatives estimation, compare locpoly for details

kernel

kernel for partial derivatives estimation, compare locpoly for details

solver

solver used in partial derivatives estimation, compare locpoly for details

degree

degree of local polynomial used for partial derivatives estimation, compare locpoly for details

baryweight

calculate three partial derivatives estimators and return a barycentric weighted average.

This increases the accuracy of Akima splines but the runtime is multplied by 3!

autodegree

try to reduce degree automatically

adtol

tolerance used for autodegree

smoothpde

Use an averaged version of partial derivatives estimates, by default simple average of nweight estimates.

Currently disabled by default (FALSE), underlying code still a bit experimental.

akimaweight

apply Akima weighting scheme on partial derivatives estimations instead of simply averaging

nweight

size of search neighbourhood for weighting scheme, default: 25

na.rm

remove points where z=NA, defaults to FALSE

Author

Albrecht Gebhardt <albrecht.gebhardt@aau.at>, Roger Bivand <roger.bivand@nhh.no>

References

Moebius, A. F. (1827) Der barymetrische Calcul. Verlag v. Johann Ambrosius Barth, Leipzig, https://books.google.at/books?id=eFPluv_UqFEC&hl=de&pg=PR1#v=onepage&q&f=false

Franke, R., (1979). A critical comparison of some methods for interpolation of scattered data. Tech. Rep. NPS-53-79-003, Dept. of Mathematics, Naval Postgraduate School, Monterey, Calif.

Akima, H. (1978). A Method of Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points. ACM Transactions on Mathematical Software 4, 148-164.

Akima, H. (1996). Algorithm 761: scattered-data surface fitting that has the accuracy of a cubic polynomial. ACM Transactions on Mathematical Software 22, 362--371.

Examples

Run this code

### Use all datasets from Franke, 1979:
data(franke)
## x-y irregular grid points:
oldseed <- set.seed(42)
ni <- 64
xi <- runif(ni,0,1)
yi <- runif(ni,0,1)
xyi <- cbind(xi,yi)
## linear interpolation
fi <- franke.fn(xi,yi,1)
IL <- interp(xi,yi,fi,nx=80,ny=80,method="linear")
## prepare breaks and colors that match for image and contour:
breaks <- pretty(seq(min(IL$z,na.rm=TRUE),max(IL$z,na.rm=TRUE),length=11))
db <- breaks[2]-breaks[1]
nb <- length(breaks)
breaks <- c(breaks[1]-db,breaks,breaks[nb]+db)
colors <- terrain.colors(length(breaks)-1)
image(IL,breaks=breaks,col=colors,main="Franke function 1",
      sub=paste("linear interpolation, ", ni,"points"))
contour(IL,add=TRUE,levels=breaks)
points(xi,yi)
## spline interpolation
fi <- franke.fn(xi,yi,1)
IS <- interp(xi,yi,fi,method="akima",
             kernel="gaussian",solver="QR")
## prepare breaks and colors that match for image and contour:
breaks <- pretty(seq(min(IS$z,na.rm=TRUE),max(IS$z,na.rm=TRUE),length=11))
db <- breaks[2]-breaks[1]
nb <- length(breaks)
breaks <- c(breaks[1]-db,breaks,breaks[nb]+db)
colors <- terrain.colors(length(breaks)-1)
image(IS,breaks=breaks,col=colors,main="Franke function 1",
      sub=paste("spline interpolation, ", ni,"points"))
contour(IS,add=TRUE,levels=breaks)
        points(xi,yi)
## regular grid:
nx <- 8; ny <- 8
xg<-seq(0,1,length=nx)
yg<-seq(0,1,length=ny)
xx <- t(matrix(rep(xg,ny),nx,ny))
yy <- matrix(rep(yg,nx),ny,nx)
xyg<-expand.grid(xg,yg)
## linear interpolation
fg <- outer(xg,yg,function(x,y)franke.fn(x,y,1))
IL <- interp(xg,yg,fg,input="grid",method="linear")
## prepare breaks and colors that match for image and contour:
breaks <- pretty(seq(min(IL$z,na.rm=TRUE),max(IL$z,na.rm=TRUE),length=11))
db <- breaks[2]-breaks[1]
nb <- length(breaks)
breaks <- c(breaks[1]-db,breaks,breaks[nb]+db)
colors <- terrain.colors(length(breaks)-1)
image(IL,breaks=breaks,col=colors,main="Franke function 1",
      sub=paste("linear interpolation, ", nx,"x",ny,"points"))
contour(IL,add=TRUE,levels=breaks)
points(xx,yy)
## spline interpolation
fg <- outer(xg,yg,function(x,y)franke.fn(x,y,1))
IS <- interp(xg,yg,fg,input="grid",method="akima",
             kernel="gaussian",solver="QR")
## prepare breaks and colors that match for image and contour:
breaks <- pretty(seq(min(IS$z,na.rm=TRUE),max(IS$z,na.rm=TRUE),length=11))
db <- breaks[2]-breaks[1]
nb <- length(breaks)
breaks <- c(breaks[1]-db,breaks,breaks[nb]+db)
colors <- terrain.colors(length(breaks)-1)
image(IS,breaks=breaks,col=colors,main="Franke function 1",
      sub=paste("spline interpolation, ", nx,"x",ny,"points"))
contour(IS,add=TRUE,levels=breaks)
points(xx,yy)

## apply interp to sp data:
require(sp)
## convert Akima data set to a sp object 
data(akima)
asp <- SpatialPointsDataFrame(list(x=akima$x,y=akima$y),
                              data = data.frame(z=akima$z))
spplot(asp,"z")
## linear interpolation
spli <- interp(asp, z="z", method="linear")
## the result is again a SpatialPointsDataFrame: 
spplot(spli,"z")
## now with spline interpolation, slightly higher resolution
spsi <- interp(asp, z="z", method="akima", nx=120, ny=120)
spplot(spsi,"z")

## now sp grids: reuse stuff from above
spgr <- SpatialPixelsDataFrame(list(x=c(xx),y=c(yy)),
                               data=data.frame(z=c(fg)))
spplot(spgr)
## linear interpolation
spli <- interp(spgr, z="z", method="linear", input="grid")
## the result is again a SpatialPointsDataFrame: 
spplot(spli,"z")
## now with spline interpolation, slightly higher resolution
spsi <- interp(spgr, z="z", method="akima", nx=240, ny=240)
spplot(spsi,"z")

set.seed(oldseed)

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning