This function implements bivariate interpolation onto a set of points for irregularly spaced input data.
This function is meant for backward compatibility to package
akima
, please use interp
with its output
argument set to "points"
now.
interpp(x, y = NULL, z, xo, yo = NULL, linear = TRUE,
extrap = FALSE, duplicate = "error", dupfun = NULL,
deltri = "shull")
vector of x-coordinates of data points or a
SpatialPointsDataFrame
object.
Missing values are not accepted.
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.
vector of z-coordinates of data points or a character variable
naming the variable of interest in the
SpatialPointsDataFrame
x
.
Missing values are not accepted.
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
cannot be collinear, i.e, they cannot fall on the same line (two vectors
x
and y
such that y = ax + b
for some a
,
b
will not be accepted).
vector of x-coordinates of points at which to evaluate the interpolating
function. If x
is a SpatialPointsDataFrame
this has
also to be a SpatialPointsDataFrame
.
vector of y-coordinates of points at which to evaluate the interpolating function.
If operating on SpatialPointsDataFrame
s this is left as NULL
logical -- indicating wether linear or spline interpolation should be used.
logical flag: should extrapolation be used outside of the convex hull determined by the data points? Not possible for linear interpolation.
indicates 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 of duplicate z
values.
this function is applied to duplicate points if duplicate="user"
triangulation method used, this argument will later be moved into a control set together with others related to the spline interpolation!
a list with 3 components:
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
.
If output="grid"
:
matrix of fitted \(z\)-values. The value z[i,j]
is computed
at the point \((xo[i], yo[j])\). 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
SpatialPixelssDataFrame
is returned for output="grid"
and a SpatialPointsDataFrame
for output="points"
.
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.
# NOT RUN {
### Use all datasets from Franke, 1979:
### calculate z at shifted original locations.
data(franke)
for(i in 1:5)
for(j in 1:3){
FR <- franke.data(i,j,franke)
IL <- with(FR, interpp(x,y,z,x+0.1,y+0.1,linear=TRUE))
str(IL)
}
# }
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