This function returns experimental and fitted variogram information
for multiple variables. For each variable autofitVariogram is used.
The function supports spatial data in simple features (sf), SpatialPointsDataFrame(sp)
or data.frame. If the input data is in data.frame, the X, and Y should be
present in the data as variables.
Usage
fit_multiple_variogram(data, coords = c("X", "Y"), length = 99)Arguments
- data
input data set one of sp, sf or data.frame with X and Y as variables
- coords
(vector) pair of coordinates if data type is aspatial
- length
(integer) the length of sequence used to produced fitted data.
Value
A data frame with combined information from all variables as response is returned.
The data.frame contains the experimental variogram information, the fitted
variogram model information.
The sums of squares (sserr) between the sample variogram dropped,
For further information please refer to autofitVariogram
References
automatp: A index page for automap package on CRAN
Hiemstra, P.H., Pebesma, E.J., Twenhofel, C.J.W. and G.B.M. Heuvelink, 2008. Real-time automatic interpolation of ambient gamma dose rates from the Dutch Radioactivity Monitoring Network. Computers & Geosciences.DOI:
Examples
## Read data
data(landcover)
# subset data
lcdat <- landcover[, c("MBLU", "MNDVI", "MNIR", "MPC1", "MPC2", "MPC23", "MRED", "MSAVI")]
# fit variograms
fmv <- fit_multiple_variogram(data = lcdat,coords = NULL,length = 99 )
#> [1] "running semivariance analysis for variable : MBLU"
#> [1] "running semivariance analysis for variable : MNDVI"
#> [1] "running semivariance analysis for variable : MNIR"
#> [1] "running semivariance analysis for variable : MPC1"
#> [1] "running semivariance analysis for variable : MPC2"
#> [1] "running semivariance analysis for variable : MPC23"
#> [1] "running semivariance analysis for variable : MRED"
#> [1] "running semivariance analysis for variable : MSAVI"
if (FALSE) {
data("landcover")
fit_multiple_variogram(data = landcover,coords = NULL, length = 99)
}