interp  R Documentation 
These functions implement bivariate interpolation onto a grid for irregularly spaced input data. Bilinear or bicubic spline interpolation is applied using different versions of algorithms from Akima.
interp(x, y=NULL, z, xo=seq(min(x), max(x), length = nx), yo=seq(min(y), max(y), length = ny), linear = TRUE, extrap=FALSE, duplicate = "error", dupfun = NULL, nx = 40, ny = 40, jitter = 10^12, jitter.iter = 6, jitter.random = FALSE, remove = !linear)
x 
vector of xcoordinates of data points or a

y 
vector of ycoordinates of data points. Missing values are not accepted. If left as NULL indicates that 
z 
vector of zcoordinates of data points or a character variable
naming the variable of interest in the
Missing values are not accepted.

xo 
vector of xcoordinates of output grid. The default is 40 points
evenly spaced over the range of 
yo 
vector of ycoordinates of output grid; analogous to

linear 
logical – indicating wether linear or spline interpolation should be used. 
extrap 
logical flag: should extrapolation be used outside of the convex hull determined by the data points? 
duplicate 
character string indicating how to handle duplicate data points. Possible values are

dupfun 
a function, applied to duplicate points if

nx 
dimension of output grid in x direction 
ny 
dimension of output grid in y direction 
jitter 
Jitter of amount of Note that the jitter is not generated randomly unless

jitter.iter 
number of iterations to retry with jitter, amount
will be multiplied in each iteration by 
jitter.random 
logical, see 
remove 
logical, indicates whether Akimas removal of thin triangles along
the border of the convex hull should be performed, experimental setting!
defaults to 
If linear
is TRUE
(default), linear
interpolation is used in the triangles bounded by data points.
Cubic interpolation is done if linear
is set to FALSE
.
If extrap
is FALSE
, zvalues for points outside the
convex hull are returned as NA
.
No extrapolation can be performed for the linear case.
The interp
function handles duplicate (x,y)
points
in different ways. As default it will stop with an error message. But
it can give duplicate points an unique z
value according to the
parameter duplicate
(mean
,median
or any other
user defined function).
The triangulation scheme used by interp
works well if x
and y
have similar scales but will appear stretched if they have
very different scales. The spreads of x
and y
must be
within four orders of magnitude of each other for interp
to work.
list with 3 components:
x,y 
vectors of x and y coordinates of output grid, the same as the input
argument 
z 
matrix of fitted zvalues. The value 
If input is a SpatialPointsDataFrame
a
SpatialPixelssDataFrame
is returned.
interp
uses Akimas new Fortran code (ACM 761) from 1996 in the revised
version by Renka from 1998 for spline interpolation, the triangulation
(based on Renkas tripack) is reused for linear interpolation. In this
newer version Akima switched from his own triangulation to Renkas
tripack (ACM 751).
Note that earlier versions (up to version 0.512) as well as SPlus used the old Fortran code from Akima 1978 (ACM 526).
The resulting structure is suitable for input to the
functions contour
and image
. Check
the requirements of these functions when choosing values for
xo
and yo
.
Akima, H. (1978). A Method of Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points. ACM Transactions on Mathematical Software 4, 148164.
Akima, H. (1996). Algorithm 761: scattereddata surface fitting that has the accuracy of a cubic polynomial. ACM Transactions on Mathematical Software 22, 362–371.
R. J. Renka (1996). Algorithm 751: TRIPACK: a constrained twodimensional Delaunay triangulation package. ACM Transactions on Mathematical Software. 22, 18.
R. J. Renka and Ron Brown (1998). Remark on algorithm 761. ACM Transactions on Mathematical Software. 24, 383385.
contour
, image
,
approx
, spline
,
aspline
,
outer
, expand.grid
,
link{franke.data}
.
data(akima) plot(y ~ x, data = akima, main = "akima example data") with(akima, text(x, y, formatC(z,dig=2), adj = 0.1)) ## linear interpolation akima.li < interp(akima$x, akima$y, akima$z) li.zmin < min(akima.li$z,na.rm=TRUE) li.zmax < max(akima.li$z,na.rm=TRUE) breaks < pretty(c(li.zmin,li.zmax),10) colors < heat.colors(length(breaks)1) with(akima.li, image (x,y,z, breaks=breaks, col=colors)) with(akima.li,contour(x,y,z, levels=breaks, add=TRUE)) points (akima, pch = 3) ## increase smoothness (using finer grid): akima.smooth < with(akima, interp(x, y, z, nx=100, ny=100)) si.zmin < min(akima.smooth$z,na.rm=TRUE) si.zmax < max(akima.smooth$z,na.rm=TRUE) breaks < pretty(c(si.zmin,si.zmax),10) colors < heat.colors(length(breaks)1) image (akima.smooth, main = "interp(<akima data>, *) on finer grid", breaks=breaks, col=colors) contour(akima.smooth, add = TRUE, levels=breaks, col = "thistle") points(akima, pch = 3, cex = 2, col = "blue") ## use triangulation package to show underlying triangulation: ## Not run: if(library(tripack, logical.return=TRUE)) plot(tri.mesh(akima), add=TRUE, lty="dashed") ## End(Not run) ## use only 15 points (interpolation only within convex hull!) akima.part < with(akima, interp(x[1:15], y[1:15], z[1:15])) p.zmin < min(akima.part$z,na.rm=TRUE) p.zmax < max(akima.part$z,na.rm=TRUE) breaks < pretty(c(p.zmin,p.zmax),10) colors < heat.colors(length(breaks)1) image(akima.part, breaks=breaks, col=colors) title("interp() on subset of only 15 points") contour(akima.part, levels=breaks, add=TRUE) points(akima$x[1:15],akima$y[1:15], col = "blue") ## spline interpolation akima.spl < with(akima, interp(x, y, z, nx=100, ny=100, linear=FALSE)) contour(akima.spl, main = "smooth interp(*, linear = FALSE)") points(akima) full.pal < function(n) hcl(h = seq(340, 20, length = n)) cool.pal < function(n) hcl(h = seq(120, 0, length = n) + 150) warm.pal < function(n) hcl(h = seq(120, 0, length = n)  30) filled.contour(akima.spl, color.palette = full.pal, plot.axes = { axis(1); axis(2); title("smooth interp(*, linear = FALSE)"); points(akima, pch = 3, col= hcl(c=100, l = 20))}) ## no extrapolation! ## Not run: ## interp can handle spatial point dataframes created by the sp package: library(sp) data(meuse) coordinates(meuse) < ~x+y ## argument z has to be named, y has to be omitted! z < interp(meuse,z="zinc",nx=100,ny=150) spplot(z,"zinc") z < interp(meuse,z="zinc",nx=100,ny=150,linear=FALSE) spplot(z,"zinc") ## End(Not run) ## Not run: ### An example demonstrating the problems that occur for rectangular ### gridded data. ### require(tripack) ### Create irregularly spaced sample data on even values of x and y ### (the "14" makes it irregular spacing). x < c(seq(2,10,2),14) nx < length(x) y < c(seq(2,10,2),14) ny < length(y) nxy < nx*ny xy < expand.grid(x,y) colnames(xy) < c("x","y") ### prepare a dataframe for interp df < cbind(xy,z=rnorm(nxy)) ### and a matrix for bicubic and bilinear z < matrix(df$z,nx,ny) old.par < par(mfrow=c(2,2)) ### First: bicubic spline interpolation: ### This is Akimas bicubic spline implementation for regular gridded ### data: iRbic < bicubic.grid(x,y,z,nx=250,ny=250) ### Note that this interpolation tends to extreme values in large cells. ### Therefore zmin and zmax are taken from here to generate the same ### color scheme for the next plots. zmin < min(iRbic$z, na.rm=TRUE) zmax < max(iRbic$z, na.rm=TRUE) breaks < pretty(c(zmin,zmax),10) colors < heat.colors(length(breaks)1) image(iRbic,breaks=breaks,col = colors) contour(iRbic,col="black",levels=breaks,add=TRUE) points(xy$x,xy$y) title(main="bicubic interpolation", xlab="bcubic.grid(...)", sub="Akimas regular grid version, ACM 760") ### Now Akima splines with accurracy of bicubic polynomial ### for irregular gridded data: iRspl < with(df,interp(x,y,z,linear=FALSE,nx=250,ny=250)) ### Note that the triangulation is created by adding small amounts ### of jitter to the coordinates, resulting in an unique triangulation. ### This jitter is not randomly choosen to get reproducable results. ### tri.mesh() from package tripack uses the same code and so produces the ### same triangulation. image(iRspl,breaks=breaks,col = colors) contour(iRspl,col="black",levels=breaks,add=TRUE) plot(tri.mesh(xy$x,xy$y),col="white",add=TRUE) title(main="bicubic* interpolation", xlab="interp(...,linear=FALSE)", ylab="*: accuracy of bicubic polynomial" sub="Akimas irregular grid version, ACM 761") ### Just for comparison an implementation of bilinear interpolation, ### only applicable to regular gridded data: iRbil < bilinear.grid(x,y,z,nx=250,ny=250) ### Note the lack of differentiability at grid cell borders. image(iRbil,breaks=breaks,col = colors) contour(iRbil,col="black",levels=breaks,add=TRUE) points(xy$x,xy$y) title(main="bilinear interpolation", xlab="bilinear.grid(...)", sub="only works for regular grid") ### Linear interpolation using the same triangulation as ### Akima bicubic splines for irregular gridded data. iRlin < with(df,interp(x,y,z,linear=TRUE,nx=250,ny=250)) ### Note how the triangulation influences the interpolation. ### For this rectangular gridded dataset the triangulation ### in each rectangle is arbitraryly choosen from two possible ### solutions, hence the interpolation would change drastically ### when the triangulation changes. For this reason interp() ### is not meant for regular (rectangular) gridded data! image(iRlin,breaks=breaks,col = colors) contour(iRlin,col="black",levels=breaks,add=TRUE) plot(tri.mesh(xy$x,xy$y),col="white",add=TRUE) title(main="linear interpolation", xlab="interp(...,linear=TRUE)", sub="same triangulation as Akima irregular grid") ### And now four times Akima 761 with random jitter for ### triangulation correction, note that now interp() and tri.mesh() ### need the same random seed to produce identical triangulations! for(i in 1:4){ set.seed(42+i) iRspl < with(df,interp(x,y,z,linear=FALSE,nx=250,ny=250,jitter.random=TRUE)) image(iRspl,breaks=breaks,col = colors) contour(iRspl,col="black",levels=breaks,add=TRUE) set.seed(42+i) plot(tri.mesh(xy$x,xy$y,jitter.random=TRUE),col="white",add=TRUE) title(main="bicubic* interpolation", xlab="interp(...,linear=FALSE)", ylab="random jitter added", sub="Akimas irregular grid version, ACM 761") } par(old.par) ## End(Not run) ### Use all datasets from Franke, 1979: data(franke) for(i in 1:5) for(j in 1:3){ FR < franke.data(i,j,franke) IL < with(FR, interp(x,y,z,linear=FALSE)) image(IL) contour(IL,add=TRUE) with(FR,points(x,y)) }
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