| Title: | Automatic Interpolation Package |
|---|---|
| Description: | An automatic interpolation is done by automatically estimating the variogram and then calling gstat. An overview is given by Hiemstra et al (2008) <doi:10.1016/j.cageo.2008.10.011>. |
| Authors: | Paul Hiemstra [aut], Jon Olav Skoien [aut, cre] |
| Maintainer: | Jon Olav Skoien <[email protected]> |
| License: | GPL |
| Version: | 1.1-12 |
| Built: | 2024-11-03 03:04:41 UTC |
| Source: | https://github.com/cran/automap |
Automatically fitting a variogram to the data on which it is applied. The automatic fitting
is done through fit.variogram. In fit.variogram the user had to supply an initial estimate for the sill,
range etc. autofitVariogram provides this estimate based on the data and then calls fit.variogram.
autofitVariogram(formula, input_data, model = c("Sph", "Exp", "Gau", "Ste"), kappa = c(0.05, seq(0.2, 2, 0.1), 5, 10), fix.values = c(NA,NA,NA), verbose = FALSE, GLS.model = NA, start_vals = c(NA,NA,NA), miscFitOptions = list(), ...)autofitVariogram(formula, input_data, model = c("Sph", "Exp", "Gau", "Ste"), kappa = c(0.05, seq(0.2, 2, 0.1), 5, 10), fix.values = c(NA,NA,NA), verbose = FALSE, GLS.model = NA, start_vals = c(NA,NA,NA), miscFitOptions = list(), ...)
formula |
formula that defines the dependent variable as a linear model of independent variables; suppose the dependent variable has name 'z', for ordinary and simple kriging use the formula 'z~1'; for simple kriging also define 'beta' (see below); for universal kriging, suppose 'z' is linearly dependent on 'x' and 'y', use the formula 'z~x+y'. |
input_data |
An object of SpatialPointsDataFrame-class or sf . |
model |
The list of variogrammodels that will be tested. |
kappa |
Smoothing parameter of the Matern model. Provide a list if you want to check more than one value. |
fix.values |
Can be used to fix a variogram parameter to a certain value. It consists of a list with a length of three. The items describe the fixed value for the nugget, range and sill respectively. They need to be given in that order. Setting the value to NA means that the value is not fixed. |
verbose |
logical, if TRUE the function will give extra feedback on the fitting process |
GLS.model |
If a variogram model is passed on through this parameter a Generalized Least Squares sample variogram is calculated. |
start_vals |
Can be used to give the starting values for the variogram fitting. The items describe the fixed value for the nugget, range and sill respectively. They need to be given in that order. Setting the value to NA means that the value will be automatically chosen. |
miscFitOptions |
A list with named arguments that provide additional control over the fitting process.
For example:
|
... |
parameters that are passed on to variogram when calculating the sample variogram. |
Geostatistical routines are used from package gstat.
A few simple choices are made when estimating the inital guess for fit.variogram.
The initial sill is estimated as the mean of the max and the median
of the semi-variance. The inital range is defined as 0.10 times the diagonal of the bounding
box of the data. The initial nugget is defined as the min of the the semi-variance.
There are five different types of models that are often used:
A shperical model.
An exponential model.
A gaussian model.
A model of the Matern familiy
Matern, M. Stein's parameterization
A list of all permitted variogram models is available by typing vgm() into the R console.
autofitVariogram iterates over the variogram models listed in model and picks the model
that has the smallest residual sum of squares with the sample variogram. For the Matern model, all the
kappa values in kappa are tested.
Note that when using the power model, and not specifying starting values yourself, the sill is set to 1, the range to 1 and the nugget to 0. This is because the normal initial values for those paramters don't work well with the power model. I consider this a temporary solution, any suggestions are appreciated.
It is possible to pass anisotropy parameters to autofitVariogram. However, autofitVariogram does not fit anisotropic variogram models. The function sees the anisotropic sample variogram as one big sample variogram. So it fits an average isotropic variogram model from the anisotropic sample variogram. A warning is issued when a users passes alpha to autofitVariogram.
An object of type autofitVariogram is returned. This object contains the experimental variogram,
the fitted variogram model and the sums of squares (sserr) between the sample variogram and the
fitted variogram model.
autofitVariogram is mostly used indirectly through the function autoKrige
Paul Hiemstra, [email protected]
fit.variogram, autoKrige, posPredictionInterval
library(sp) data(meuse) coordinates(meuse) =~ x+y variogram = autofitVariogram(zinc~1,meuse) plot(variogram) # Residual variogram data(meuse) coordinates(meuse) =~ x+y variogram = autofitVariogram(zinc ~ soil + ffreq + dist, meuse) plot(variogram) # Settings additional fitting options variogram = autofitVariogram(zinc ~ soil + ffreq + dist, meuse, miscFitOptions = list(merge.small.bins = FALSE)) plot(variogram) # Settings the minimum number of pairs per bin quite high # to see the effect of merging bins variogram = autofitVariogram(zinc ~ soil + ffreq + dist, meuse, miscFitOptions = list(min.np.bin = 500)) plot(variogram) # ...and disable the merging, note the difference between the two plots variogram = autofitVariogram(zinc ~ soil + ffreq + dist, meuse, miscFitOptions = list(min.np.bin = 500, merge.small.bins = FALSE)) plot(variogram) # An example of autofitVariogram with anisotropic sample variogram. # This is not supported, see details section. vm.isotropic = autofitVariogram(log(zinc) ~ dist, meuse) # The following line might not work, depending on version of R and gstat # vm.anisotropic = autofitVariogram(log(zinc) ~ dist, meuse, alpha = c(0,45,90,135))library(sp) data(meuse) coordinates(meuse) =~ x+y variogram = autofitVariogram(zinc~1,meuse) plot(variogram) # Residual variogram data(meuse) coordinates(meuse) =~ x+y variogram = autofitVariogram(zinc ~ soil + ffreq + dist, meuse) plot(variogram) # Settings additional fitting options variogram = autofitVariogram(zinc ~ soil + ffreq + dist, meuse, miscFitOptions = list(merge.small.bins = FALSE)) plot(variogram) # Settings the minimum number of pairs per bin quite high # to see the effect of merging bins variogram = autofitVariogram(zinc ~ soil + ffreq + dist, meuse, miscFitOptions = list(min.np.bin = 500)) plot(variogram) # ...and disable the merging, note the difference between the two plots variogram = autofitVariogram(zinc ~ soil + ffreq + dist, meuse, miscFitOptions = list(min.np.bin = 500, merge.small.bins = FALSE)) plot(variogram) # An example of autofitVariogram with anisotropic sample variogram. # This is not supported, see details section. vm.isotropic = autofitVariogram(log(zinc) ~ dist, meuse) # The following line might not work, depending on version of R and gstat # vm.anisotropic = autofitVariogram(log(zinc) ~ dist, meuse, alpha = c(0,45,90,135))
This function performs automatic kriging on the given dataset. The variogram is generated automatically using autofitVariogram.
autoKrige(formula, input_data, new_data, data_variogram = input_data, block = 0, model = c("Sph", "Exp", "Gau", "Ste"), kappa = c(0.05, seq(0.2, 2, 0.1), 5, 10), fix.values = c(NA,NA,NA), remove_duplicates = TRUE, verbose = FALSE, GLS.model = NA, start_vals = c(NA,NA,NA), miscFitOptions = list(), ...)autoKrige(formula, input_data, new_data, data_variogram = input_data, block = 0, model = c("Sph", "Exp", "Gau", "Ste"), kappa = c(0.05, seq(0.2, 2, 0.1), 5, 10), fix.values = c(NA,NA,NA), remove_duplicates = TRUE, verbose = FALSE, GLS.model = NA, start_vals = c(NA,NA,NA), miscFitOptions = list(), ...)
formula |
formula that defines the dependent variable as a linear model of independent variables; suppose the dependent variable has name 'z', for ordinary and simple kriging use the formula 'z~1'; for simple kriging also define 'beta' (see below); for universal kriging, suppose 'z' is linearly dependent on 'x' and 'y', use the formula 'z~x+y'. |
input_data |
An object of the SpatialPointsDataFrame-class or sf containing the data to be interpolated. |
new_data |
A |
data_variogram |
An optional way to provide a different dataset for the building of the variogram then for the spatial interpolation. |
block |
Use this parameter to pass on a specification for the block size. e.g. c(1000,1000) |
model |
List of models that will be tested during automatic variogram fitting. |
kappa |
List of values for the smoothing parameter of the Matern model that will be tested during automatic variogram fitting. |
fix.values |
Can be used to fix a variogram parameter to a certain value. It consists of a list with a length of three. The items describe the fixed value for the nugget, range and sill respectively. Setting the value to NA means that the value is not fixed. Is passed on to autofitVariogram. |
remove_duplicates |
logical, remove duplicate points from the |
verbose |
logical, if TRUE autoKrige will give extra information on the fitting process |
GLS.model |
If a variogram model is passed on through this parameter a Generalized Least Squares sample variogram is calculated. |
start_vals |
Can be used to give the starting values for the variogram fitting. The items describe the fixed value for the nugget, range and sill respectively. They need to be given in that order. Setting the value to NA means that the value will be automatically chosen. |
miscFitOptions |
Additional options to set the behavior of autofitVariogram. For details see the documentation of autofitVariogram. |
... |
arguments that are passed on to the gstat function |
autoKrige calls the function autofitVariogram that fits a variogram model to the
given dataset. This variogram model and the data are used to make predictions on the locations
in new_data. The only compulsory argument is input_data. So the most
simple call would of the form:
autoKrige(meuse)
autoKrige now assumes that you want to perform ordinary kriging on the first column of
input_data.
autoKrige performs some checks on the coordinate systems of input_data and new_data.
If one of both is NA, it is assigned the projection of the other. If they have different projections,
an error is raised. If one of both has a non-projected system (i.e. latitude-longitude), an error is raised.
This error is raised because 'gstat does use spherical distances when data are in geographical
coordinates, however the usual variogram models are typically not
non-negative definite on the sphere, and no appropriate models are
available' (Edzer Pebesma on r-sig-geo).
When the user specifies the power model (Pow) as the model, the initial range is set to one. Note that
when using the power model, the initial range is the initial power.
This function returns an autoKrige object containing the results of the interpolation
(prediction, variance and standard deviation), the sample variogram, the variogram model that
was fitted by autofitVariogram and the sums of squares between the sample variogram and the
fitted variogram model. The attribute names are krige_output, exp_var, var_model
and sserr respectively.
Paul Hiemstra, [email protected]
# Data preparation library(sp) library(sf) library(stars) data(meuse) coordinates(meuse) =~ x+y data(meuse.grid) gridded(meuse.grid) =~ x+y # Ordinary kriging, no new_data object kriging_result = autoKrige(zinc~1, meuse) plot(kriging_result) # Ordinary kriging kriging_result = autoKrige(zinc~1, meuse, meuse.grid) plot(kriging_result) # Fixing the nugget to 0.2 kriging_result = autoKrige(zinc~1, meuse, meuse.grid, fix.values = c(0.2,NA,NA)) plot(kriging_result) # Universal kriging kriging_result = autoKrige(zinc~soil+ffreq+dist, meuse, meuse.grid) plot(kriging_result) # Block kriging kriging_result_block = autoKrige(zinc~soil+ffreq+dist, meuse, meuse.grid, block = c(400,400)) plot(kriging_result_block) # Dealing with duplicate observations data(meuse) meuse.dup = rbind(meuse, meuse[1,]) # Create duplicate coordinates(meuse.dup) = ~x+y kr = autoKrige(zinc~dist, meuse.dup, meuse.grid) # Extracting parts from the autoKrige object prediction_spdf = kr$krige_output sample_variogram = kr$exp_var variogram_model = kr$var_model coordinates(meuse) = ~x + y meuse = st_as_sf(meuse) meuse.grid = st_as_stars(meuse.grid) kriging_result = autoKrige(zinc~1, meuse, meuse.grid, fix.values = c(0.2,NA,NA))# Data preparation library(sp) library(sf) library(stars) data(meuse) coordinates(meuse) =~ x+y data(meuse.grid) gridded(meuse.grid) =~ x+y # Ordinary kriging, no new_data object kriging_result = autoKrige(zinc~1, meuse) plot(kriging_result) # Ordinary kriging kriging_result = autoKrige(zinc~1, meuse, meuse.grid) plot(kriging_result) # Fixing the nugget to 0.2 kriging_result = autoKrige(zinc~1, meuse, meuse.grid, fix.values = c(0.2,NA,NA)) plot(kriging_result) # Universal kriging kriging_result = autoKrige(zinc~soil+ffreq+dist, meuse, meuse.grid) plot(kriging_result) # Block kriging kriging_result_block = autoKrige(zinc~soil+ffreq+dist, meuse, meuse.grid, block = c(400,400)) plot(kriging_result_block) # Dealing with duplicate observations data(meuse) meuse.dup = rbind(meuse, meuse[1,]) # Create duplicate coordinates(meuse.dup) = ~x+y kr = autoKrige(zinc~dist, meuse.dup, meuse.grid) # Extracting parts from the autoKrige object prediction_spdf = kr$krige_output sample_variogram = kr$exp_var variogram_model = kr$var_model coordinates(meuse) = ~x + y meuse = st_as_sf(meuse) meuse.grid = st_as_stars(meuse.grid) kriging_result = autoKrige(zinc~1, meuse, meuse.grid, fix.values = c(0.2,NA,NA))
Uses autofitVariogram to fit a variogram model to the data and then calls
krige.cv to perform cross-validation.
autoKrige.cv(formula, input_data, data_variogram = input_data, model = c("Sph", "Exp", "Gau", "Ste"), kappa = c(0.05, seq(0.2, 2, 0.1), 5, 10), fix.values = c(NA,NA,NA), verbose = c(FALSE, interactive()), GLS.model = NA, start_vals = c(NA,NA,NA), miscFitOptions = list(), ...)autoKrige.cv(formula, input_data, data_variogram = input_data, model = c("Sph", "Exp", "Gau", "Ste"), kappa = c(0.05, seq(0.2, 2, 0.1), 5, 10), fix.values = c(NA,NA,NA), verbose = c(FALSE, interactive()), GLS.model = NA, start_vals = c(NA,NA,NA), miscFitOptions = list(), ...)
formula |
formula that defines the dependent variable as a linear model of independent variables; suppose the dependent variable has name 'z', for ordinary and simple kriging use the formula 'z~1'; for simple kriging also define 'beta' (see below); for universal kriging, suppose 'z' is linearly dependent on 'x' and 'y', use the formula 'z~x+y'. |
input_data |
An object of the SpatialPointsDataFrame-class containing the data to be interpolated. |
data_variogram |
An optional way to provide a different dataset for the building of the variogram. |
model |
List of models that will be tested during automatic variogram fitting. |
kappa |
List of values for the smoothing parameter of the Matern model that will be tested during automatic variogram fitting. |
fix.values |
Can be used to fix a variogram parameter to a certain value. It consists of a list with a length of three. The items describe the fixed value for the nugget, range and sill respectively. Setting the value to NA means that the value is not fixed. Is passed on to autofitVariogram. |
verbose |
vector of 2 logicals. The first element sets the verbosity of autofitVariogram, see its documentation for more information. The second element sets the verbosity level of krige.cv, see its documentation for more information. |
GLS.model |
If a variogram model is passed on through this parameter a Generalized Least Squares sample variogram is calculated. |
start_vals |
Can be used to give the starting values for the variogram fitting. The items describe the fixed value for the nugget, range and sill respectively. They need to be given in that order. Setting the value to NA means that the value will be automatically chosen. |
miscFitOptions |
Additional options to set the behavior of autofitVariogram. For details see the documentation of autofitVariogram. |
... |
arguments passed to |
autoKrige.cv returns an object of class autoKrige.cv. This is a list
containing one object of class SpatialPointsDataFrame with the results of
the cross-validation, see krige.cv for more details. The
attribute name is krige.cv_output.
Paul Hiemstra, [email protected]
krige.cv, autofitVariogram, compare.cv
library(sp) data(meuse) coordinates(meuse) = ~x+y data(meuse.grid) gridded(meuse.grid) = ~x+y kr.cv = autoKrige.cv(log(zinc)~1, meuse, model = c("Exp"), nfold = 10) kr_dist.cv = autoKrige.cv(log(zinc)~sqrt(dist), meuse, model = c("Exp"), nfold = 10) kr_dist_ffreq.cv = autoKrige.cv(log(zinc)~sqrt(dist)+ffreq, meuse, model = c("Exp"), nfold = 10)library(sp) data(meuse) coordinates(meuse) = ~x+y data(meuse.grid) gridded(meuse.grid) = ~x+y kr.cv = autoKrige.cv(log(zinc)~1, meuse, model = c("Exp"), nfold = 10) kr_dist.cv = autoKrige.cv(log(zinc)~sqrt(dist), meuse, model = c("Exp"), nfold = 10) kr_dist_ffreq.cv = autoKrige.cv(log(zinc)~sqrt(dist)+ffreq, meuse, model = c("Exp"), nfold = 10)
This function wraps around spplot and creates a blue-to-whitish colorscale instead of the standard bpy colorscale.
automapPlot(plot_data, zcol, col.regions, sp.layout, points, ...)automapPlot(plot_data, zcol, col.regions, sp.layout, points, ...)
plot_data |
|
zcol |
The name of the column from |
col.regions |
Choose a colors that specify the fill colours. |
sp.layout |
An sp.layout object that can be passed to spplot, to be added to the plot |
points |
Points that can be added to the plot |
... |
other possible arguments that can be passed on to spplot. |
The classIntervals function from the classInt package is
a good function to calculate the position of the colorbreaks.
Paul Hiemstra, [email protected]
spplot, plot.autoKrige, plot.posPredictionInterval
# Ordinary kriging library(sp) data(meuse) coordinates(meuse) =~ x+y data(meuse.grid) gridded(meuse.grid) =~ x+y kriging_result = autoKrige(zinc~1, meuse, meuse.grid) # Adding the sp.layout parameter shows the locations of the measurements automapPlot(kriging_result$krige_output, "var1.pred", sp.layout = list("sp.points", meuse))# Ordinary kriging library(sp) data(meuse) coordinates(meuse) =~ x+y data(meuse.grid) gridded(meuse.grid) =~ x+y kriging_result = autoKrige(zinc~1, meuse, meuse.grid) # Adding the sp.layout parameter shows the locations of the measurements automapPlot(kriging_result$krige_output, "var1.pred", sp.layout = list("sp.points", meuse))
Allows comparison of the results from several outcomes of autoKrige.cv in both statistics and spatial plots
(bubble plots).
compare.cv(..., col.names, bubbleplots = FALSE, zcol = "residual", layout, key.entries, reference = 1, plot.diff = FALSE, digits = 4, ggplot = FALSE, addPoly = NULL)compare.cv(..., col.names, bubbleplots = FALSE, zcol = "residual", layout, key.entries, reference = 1, plot.diff = FALSE, digits = 4, ggplot = FALSE, addPoly = NULL)
... |
|
col.names |
Names for the different objects in |
bubbleplots |
logical, if |
zcol |
Which column in the objects in |
layout |
|
key.entries |
A list of numbers telling what the key entries in the bubbleplots are. See |
reference |
An integer telling which of the objects should be taken as a reference if |
plot.diff |
logical, if |
digits |
The number of significant digits in the resulting data.frame. |
ggplot |
logical, determines if spplot or ggplot2 is used to make the spatial plot of the cross-validation residuals.
Note that the |
addPoly |
if this object contains a |
A data.frame with for each cross-validation result a number of diagnostics:
mean_error |
The mean of the cross-validation residual. Ideally small. |
me_mean |
mean error divided by the mean of the observed values, measure for how large the mean_error is in contrast to the mean of the dataset |
MSE |
Mean Squared error. |
MSNE |
Mean Squared Normalized Error, mean of the squared z-scores. Ideally small. |
cor_obspred |
Correlation between the observed and predicted values. Ideally 1. |
cor_predres |
Correlation between the predicted and the residual values. Ideally 0. |
RMSE |
Root Mean Squared Error of the residual. Ideally small. |
RMSE_sd |
RMSE divided by the standard deviation of the observed values. Provides a measure variation of the residuals vs the variation of the observed values. |
URMSE |
Unbiased Root Mean Squared Error of the residual. Ideally small. |
iqr |
Interquartile Range of the residuals. Ideally small. |
Paul Hiemstra, [email protected]
krige.cv, bubble, autofitVariogram, autoKrige.cv,
# Load the data library(sp) data(meuse) coordinates(meuse) = ~x+y data(meuse.grid) gridded(meuse.grid) = ~x+y # Perform cross-validation kr.cv = autoKrige.cv(log(zinc)~1, meuse, model = c("Exp"), nfold = 10) kr_dist.cv = autoKrige.cv(log(zinc)~sqrt(dist), meuse, model = c("Exp"), nfold = 10) kr_dist_ffreq.cv = autoKrige.cv(log(zinc)~sqrt(dist)+ffreq, meuse, model = c("Exp"), nfold = 10) # Compare the results compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv) compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, bubbleplots = TRUE) compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, bubbleplots = TRUE, col.names = c("OK","UK1","UK2")) compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, bubbleplots = TRUE, col.names = c("OK","UK1","UK2"), plot.diff = TRUE) library(ggplot2) compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, bubbleplots = TRUE, col.names = c("OK","UK1","UK2"), ggplot = TRUE)# Load the data library(sp) data(meuse) coordinates(meuse) = ~x+y data(meuse.grid) gridded(meuse.grid) = ~x+y # Perform cross-validation kr.cv = autoKrige.cv(log(zinc)~1, meuse, model = c("Exp"), nfold = 10) kr_dist.cv = autoKrige.cv(log(zinc)~sqrt(dist), meuse, model = c("Exp"), nfold = 10) kr_dist_ffreq.cv = autoKrige.cv(log(zinc)~sqrt(dist)+ffreq, meuse, model = c("Exp"), nfold = 10) # Compare the results compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv) compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, bubbleplots = TRUE) compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, bubbleplots = TRUE, col.names = c("OK","UK1","UK2")) compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, bubbleplots = TRUE, col.names = c("OK","UK1","UK2"), plot.diff = TRUE) library(ggplot2) compare.cv(kr.cv, kr_dist.cv, kr_dist_ffreq.cv, bubbleplots = TRUE, col.names = c("OK","UK1","UK2"), ggplot = TRUE)
Defines methods to plot objects in automap.
## S3 method for class 'autoKrige' plot(x, sp.layout = NULL, ...) ## S3 method for class 'posPredictionInterval' plot(x, sp.layout = NULL, justPosition = TRUE, main = "Position prediction interval", ...)## S3 method for class 'autoKrige' plot(x, sp.layout = NULL, ...) ## S3 method for class 'posPredictionInterval' plot(x, sp.layout = NULL, justPosition = TRUE, main = "Position prediction interval", ...)
x |
the object to plot (of class |
sp.layout |
An object that can contain lines, points and polygons that function as extra layout. |
justPosition |
logical, if FALSE: not only the plot with the position of the prediction interval is plotted, but also plots with the upper and lower limits of the prediction interval. |
main |
Title of the plot for the position of the prediction interval. |
... |
arguments passed to lattice functions xyplot, spplot or plot.sf |
For a detailed description of how sp.layout is constructed see spplot.
Paul Hiemstra, [email protected]
spplot, autoKrige, posPredictionInterval
# Ordinary kriging library(sp) library(sf) data(meuse) coordinates(meuse) =~ x+y data(meuse.grid) gridded(meuse.grid) =~ x+y kriging_result = autoKrige(log(zinc)~1, meuse, meuse.grid) # Adding the sp.layout parameter shows the locations of the measurements plot(kriging_result, sp.layout = list(pts = list("sp.points", meuse))) meuse = as(meuse, "sf") meuse.grid = as(meuse.grid, "sf") kriging_result = autoKrige(log(zinc)~1, meuse, meuse.grid) # Adding the meuse points shows the locations of the measurements plot(kriging_result, points = meuse)# Ordinary kriging library(sp) library(sf) data(meuse) coordinates(meuse) =~ x+y data(meuse.grid) gridded(meuse.grid) =~ x+y kriging_result = autoKrige(log(zinc)~1, meuse, meuse.grid) # Adding the sp.layout parameter shows the locations of the measurements plot(kriging_result, sp.layout = list(pts = list("sp.points", meuse))) meuse = as(meuse, "sf") meuse.grid = as(meuse.grid, "sf") kriging_result = autoKrige(log(zinc)~1, meuse, meuse.grid) # Adding the meuse points shows the locations of the measurements plot(kriging_result, points = meuse)
This function calculates the p% prediction interval and
determines the position of this interval relative to value. This can be
higher, lower or not distinguishable.
posPredictionInterval(krige_object, p = 95, value = median(krige_object$krige_output$var1.pred))posPredictionInterval(krige_object, p = 95, value = median(krige_object$krige_output$var1.pred))
krige_object |
The result of from the autoKrige procedure. This is expected to be a autoKrige-object. |
p |
The |
value |
The value to which the the |
The output object is of class posPredictionInterval and contains the results
of the function in an Spatial-class object similar to the one in the input object. This means that
if the input object containes a grid, the results are also returned on that same grid.
Also included in the return object are the values for p and value.
Paul Hiemstra, [email protected]
library(sp) data(meuse) coordinates(meuse) =~ x+y data(meuse.grid) gridded(meuse.grid) =~ x+y kriging_result = autoKrige(zinc~1, meuse, meuse.grid) pos = posPredictionInterval(kriging_result, 95, 75) plot(pos)library(sp) data(meuse) coordinates(meuse) =~ x+y data(meuse.grid) gridded(meuse.grid) =~ x+y kriging_result = autoKrige(zinc~1, meuse, meuse.grid) pos = posPredictionInterval(kriging_result, 95, 75) plot(pos)