Tutorial on Estimation
gstlearn Team
Preamble
In this preamble, we load the gstlearn library, and clean the workspace.
rm(list=ls())
library(gstlearn)
library(ggplot2)
library(ggpubr)
library(ggnewscale)Then we download the data base dat.
fileNF = loadData("Scotland", "Scotland_Temperatures.NF")
dat = Db_createFromNF(fileNF)We also create a Db object containing a grid covering the data points in the data base dat. To do so, we start by displaying the minimal and maximal coordinates of the points in dat using the getExtremas() method from the Db class.
dat$getExtremas()## [[1]]
## [1] 78.2 460.7
##
## [[2]]
## [1] 530.4 1208.9The first (resp. second) element of the list contains the min and max coordinates in the first (resp. second) space dimension. Based on this information, we create a grid covering all the points using the DbGrid_create function. We specify the coordinates of the origin (i.e. lower left corner) of the grid (argument x0), the step sizes in each dimension (argument dx) and the number of points in each dimension (argument nx).
grid = DbGrid_create(x0=c(65,530),dx=c(4.94, 4.96),nx=c(82,138)) We then print a summary of the content of the grid using the display method of Db class, which we supply with a DbStringFormat object specifying that we would like information about the extent of the grid (argument flag_extend in the DbStringFormat_createFromFlags function).
dbfmt = DbStringFormat_createFromFlags(flag_extend=TRUE)
grid$display(dbfmt)##
## Data Base Grid Characteristics
## ==============================
##
## Data Base Summary
## -----------------
## File is organized as a regular grid
## Space dimension = 2
## Number of Columns = 3
## Total number of samples = 11316
##
## Grid characteristics:
## ---------------------
## Origin : 65.000 530.000
## Mesh : 4.940 4.960
## Number : 82 138
##
## Data Base Extension
## -------------------
## Coor #1 - Min = 65.000 - Max = 465.140 - Ext = 400.14
## Coor #2 - Min = 530.000 - Max = 1209.520 - Ext = 679.52
##
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = x1 - Locator = x1
## Column = 2 - Name = x2 - Locator = x2## NULLExperimental Variogram and fitted Model
We compute the experimental variogram vario2dir (in 2 directions) (cf. Variography for more details).
varioParamMulti = VarioParam_createMultiple(ndir=2, npas=15, dpas=15.)
vario2dir = Vario(varioParamMulti)
err = vario2dir$compute(dat)We then the fit a model fitmod
fitmod = Model()
types = ECov_fromKeys(c("NUGGET","EXPONENTIAL","GAUSSIAN"))
err = fitmod$fit(vario2dir,types=types)ggplot() + plot.varmod(vario2dir, fitmod)
Simple kriging prediction
To perform simple kriging, we use the function called kriging. We specify:
- the
Dbobject containing the data points (argumentdbin) : the variable used for kriging is the (unique) variable of the data base with azlocator (i.e. it should have locatorz1and the other variables should not have a locator starting withz) - the
Dbobject containing the target points, i.e. the points where the kriging predictor will be computed (argumentdbout) - the
Modelobject containing the model used to define the kriging predictor (argumentmodel): in particular, the mean used to define the predictor is the one set in theModelobject - the type of neighborhood used in the prediction (argument
neigh), eg. unique neighborhood (to use all the data points for each predictor) or moving neighborhood (to use only the data points in the vicinity of the target point in the prediction). This argument is defined using a “neighborhood” object (see example below).
Additionally, it is possible to specify whether we wish to compute, at each target point, the kriging predictor (argument flag_est, default=TRUE), the kriging standard-deviation (argument flag_std, default=TRUE) and the kriging variance (argument flag_varz, default=FALSE).
The kriging function then adds new variables to the Db entered in the dbout argument corresponding to these variables. The names of these newly created variables will start by Kriging, but this prefix can be changed using the namconv argument of the kriging function
In the next example, we perform a simple kriging prediction (with unique neighborhood) of the variable January_temp in the dat data base, on the grid defined in the grid data base. To do so, we start by selecting the variable January_temp in the dat data base (i.e. we make ensure that it is the only variable with a z locator).
dat$setLocator("January_temp",ELoc_Z())## NULLdat##
## Data Base Characteristics
## =========================
##
## Data Base Summary
## -----------------
## File is organized as a set of isolated points
## Space dimension = 2
## Number of Columns = 5
## Total number of samples = 236
##
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = Longitude - Locator = x1
## Column = 2 - Name = Latitude - Locator = x2
## Column = 3 - Name = Elevation - Locator = NA
## Column = 4 - Name = January_temp - Locator = z1We then create a “neighborhood” object corresponding to the specification of a unique neighborhood: this is done using the NeighUnique function.
uniqueNeigh = NeighUnique()We now call the kriging function to perform the kriging prediction. We use the model fitmod that we previously fitted on our data, require to compute the kriging predictor and its standard-deviation (but not its variance), and change the prefix of the newly created variables to “SK”.
err = kriging(dbin=dat, dbout=grid, model=fitmod,
neigh=uniqueNeigh,
flag_est=TRUE, flag_std=TRUE, flag_varz=FALSE,
namconv=NamingConvention("SK")
)We see that the kriging predictor and its standard deviation have been added to the grid data base.
grid##
## Data Base Grid Characteristics
## ==============================
##
## Data Base Summary
## -----------------
## File is organized as a regular grid
## Space dimension = 2
## Number of Columns = 5
## Total number of samples = 11316
##
## Grid characteristics:
## ---------------------
## Origin : 65.000 530.000
## Mesh : 4.940 4.960
## Number : 82 138
##
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = x1 - Locator = x1
## Column = 2 - Name = x2 - Locator = x2
## Column = 3 - Name = SK.January_temp.estim - Locator = z1
## Column = 4 - Name = SK.January_temp.stdev - Locator = NAFinally, we plot the kriging prediction over the grid using the plot.grid function and the data points.
p = ggDefaultGeographic()
p = p + plot.grid(grid,
flagLegendRaster = TRUE, palette="Spectral",legendNameRaster="°C")
p = p + plot.point(dat,flagCst = T,pch=18,cex=1.5)
p = p + plot.decoration(title="Simple Kriging over whole Grid")
ggPrint(p)
By default, the plotting function plots the variable with locator z1, which in our case corresponds to the kriging predictor (as the kriging function automatically assigns the locator z1 to it). To plot another variable, we can simply specify their name.
For instance, we can plot the kriging standard deviation using the following code.
p = ggDefaultGeographic()
p = p + plot.grid(grid,nameRaster="SK.January_temp.stdev",
flagLegendRaster = TRUE, palette="Spectral",legendNameRaster="°C")
p = p + plot.point(dat,flagCst = T,pch=18,cex=1.5)
p = p + plot.decoration(title="Simple Kriging std-dev over whole Grid")
ggPrint(p)
As mentioned above, the mean used in the simple kriging predictor is the one set in the Model object supplied in the kriging function. By default, this mean is zero. It can be changed using the setMean method of the Model object.
For instance, considering the model fitmod previously fitted on the data, we can clone it (using the clone method), and assign it a new mean (equal to 4) as follows.
fitmodSK = fitmod$clone()
err = fitmodSK$setMean(mean=4)Then, simple kriging is performed using the same command as before, but with the newly created model.
err = kriging(dbin=dat, dbout=grid, model=fitmodSK,
neigh=uniqueNeigh,
flag_est=TRUE, flag_std=TRUE, flag_varz=FALSE,
namconv=NamingConvention("Mean4_SK")
)Finally, we plot the new kriging prediction over the grid and the data points.
p = ggDefaultGeographic()
p = p + plot.grid(grid,
flagLegendRaster = TRUE, palette="Spectral",legendNameRaster="°C")
p = p + plot.point(dat,flagCst = T,pch=18,cex=1.5)
p = p + plot.decoration(title="Simple Kriging over whole Grid: Mean=4")
ggPrint(p)
Ordinary kriging
In gstlearn, ordinary kriging is seen as the formulation of a kriging predictor on a model with a constant drift. Hence, to perform ordinary kriging, we use the kriging function with the same syntax as for simple kriging, but call it with a model that includes a constant drift. Adding a constant drift to a model is done with the addDrift() method.
Note: Removing a drift from a model can be done using the delDrift method (while specifying the index of the drift we wish to remove) or using the delAllDrifts() (to remove all the drifts at once).
Let us go back to our running example. Considering the model fitmod previously fitted on the data, we can clone it (using the clone method), and add a constant drift as follows.
fitmodOK = fitmod$clone()
err = fitmodOK$addDrift(DriftM())Then, ordinary kriging is performed using the same command as before, but with the newly created model.
err = kriging(dbin=dat, dbout=grid, model=fitmodOK,
neigh=uniqueNeigh,
flag_est=TRUE, flag_std=TRUE, flag_varz=FALSE,
namconv=NamingConvention("OK")
)Finally, we plot the new kriging prediction over the grid and the data points.
p = ggDefaultGeographic()
p = p + plot.grid(grid,
flagLegendRaster = TRUE, palette="Spectral",legendNameRaster="°C")
p = p + plot.point(dat,flagCst = T,pch=18,cex=1.5)
p = p + plot.decoration(title="Ordinary Kriging over whole Grid")
ggPrint(p)