Global variables

verbose = TRUE
graphics = TRUE
err = OptCst_define(ECst_NTCOL(),6)

Reading data

The data are stored in a CSV format in the file called Pollution.dat

At this stage: - it is not possible to pass the list of variable names c(“X”,“Y”). - to pass the FLAGS for DbStringFormat (they are not an ENUM) 

dlfile = "https://soft.minesparis.psl.eu/gstlearn/data/Pollution/Pollution.dat"
filepath = "Pollution.dat"
download.file(dlfile, filepath, quiet=TRUE)
mydb = Db_createFromCSV(filepath,CSVformat())
err = mydb$setLocator("X",ELoc_X(),0)
err = mydb$setLocator("Y",ELoc_X(),1)
err = mydb$setLocator("Zn",ELoc_Z())
if (verbose)
{
  dbfmt = DbStringFormat()
  dbfmt$setFlags(flag_extend = TRUE)
  mydb$display(dbfmt)
}
## 
## 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      = 102
## 
## Data Base Extension
## -------------------
## Coor #1 - Min =    109.850 - Max =    143.010 - Ext = 33.16
## Coor #2 - Min =    483.660 - Max =    513.040 - Ext = 29.38
## 
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = X - Locator = x1
## Column = 2 - Name = Y - Locator = x2
## Column = 3 - Name = Zn - Locator = z1
## Column = 4 - Name = Pb - Locator = p1
## NULL

Accessing to the variable names

cat("List of all variable names =",mydb$getAllNames())
## List of all variable names = rank X Y Zn Pb

Extracting the vector containing the Zn variable in order to perform a selection

tabZn = mydb$getColumn("Zn")
selZn = as.numeric(tabZn < 20)
mydb$addSelection(selZn,"sel")
## [1] 5
mydb$setLocator('Pb',ELoc_Z())
## NULL
if (verbose)
    mydb$display()
## 
## Data Base Characteristics
## =========================
## 
## Data Base Summary
## -----------------
## File is organized as a set of isolated points
## Space dimension              = 2
## Number of Columns            = 6
## Total number of samples      = 102
## Number of active samples     = 99
## 
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = X - Locator = x1
## Column = 2 - Name = Y - Locator = x2
## Column = 3 - Name = Zn - Locator = NA
## Column = 4 - Name = Pb - Locator = z1
## Column = 5 - Name = sel - Locator = sel
## NULL

Display my Data (with samples represented by color and size)

if (graphics)
    ggplot() + plot(mydb,nameColor="Pb") + plot.decoration(title="Data Set")
## Warning: Using size for a discrete variable is not advised.

Variograms

We first define the geometry of the variogram calculations

myVarioParamOmni = VarioParam()
mydir = DirParam_create(npas=10,dpas=1.)
myVarioParamOmni$addDir(mydir)
## NULL

We use the variogram definition in order to calculate the variogram cloud.

dbcloud = db_variogram_cloud(db=mydb, varioparam=myVarioParamOmni)

We recall that the Variogram cloud is calculated by filling an underlying grid where each cell is painted according to the number of pairs at the given distance and given variability. Representing the variogram cloud.

if (graphics)
    ggplot() + plot(dbcloud,nameRaster="Cloud*") + plot.decoration(title="Variogram Cloud")

Calculating the experimental omni-directional variogram

myVarioOmni = Vario(myVarioParamOmni)
err = myVarioOmni$compute(mydb, ECalcVario_VARIOGRAM())
if (verbose)
    myVarioOmni$display()
## 
## Variogram characteristics
## =========================
## Number of variable(s)       = 1
## Number of direction(s)      = 1
## Space dimension             = 2
## Variance-Covariance Matrix     2.881
## 
## Direction #1
## ------------
## Number of lags              = 10
## Direction coefficients      =      1.000     0.000
## Direction angles (degrees)  =      0.000     0.000
## Tolerance on direction      =     90.000 (degrees)
## Calculation lag             =      1.000
## Tolerance on distance       =     50.000 (Percent of the lag value)
## 
## For variable 1
##       Rank    Npairs  Distance     Value
##          0     3.000     0.389     0.462
##          1   123.000     1.081     1.495
##          2   183.000     2.038     1.620
##          3   205.000     3.006     2.526
##          4   231.000     4.013     2.240
##          5   229.000     5.036     2.524
##          6   198.000     5.962     2.396
##          7   187.000     7.000     2.708
##          8   204.000     7.996     2.772
##          9   184.000     8.990     2.868
## NULL

The variogram is represented graphically for a quick check

if (graphics)
    ggplot() + plot.varmod(myVarioOmni) + 
    plot.decoration(title="Omni-directional Variogram for Pb")

Calculate a variogram in several directions

myvarioParam = VarioParam()
mydirs = DirParam_createMultiple(ndir=4, npas=10, dpas=1.)
myvarioParam$addMultiDirs(mydirs)
## NULL
myvario = Vario(myvarioParam)
myvario$compute(mydb, ECalcVario_VARIOGRAM())
## [1] 0
if (verbose)
    myvario$display()
## 
## Variogram characteristics
## =========================
## Number of variable(s)       = 1
## Number of direction(s)      = 4
## Space dimension             = 2
## Variance-Covariance Matrix     2.881
## 
## Direction #1
## ------------
## Number of lags              = 10
## Direction coefficients      =      1.000     0.000
## Direction angles (degrees)  =      0.000     0.000
## Tolerance on direction      =     22.500 (degrees)
## Calculation lag             =      1.000
## Tolerance on distance       =     50.000 (Percent of the lag value)
## 
## For variable 1
##       Rank    Npairs  Distance     Value
##          0     1.000     0.410     0.180
##          1    29.000     1.094     1.634
##          2    47.000     2.079     1.415
##          3    53.000     3.003     2.824
##          4    63.000     3.999     2.348
##          5    66.000     5.035     2.319
##          6    60.000     5.978     3.115
##          7    52.000     7.045     2.746
##          8    52.000     8.020     3.927
##          9    37.000     8.980     2.554
## 
## Direction #2
## ------------
## Number of lags              = 10
## Direction coefficients      =      0.707     0.707
## Direction angles (degrees)  =     45.000     0.000
## Tolerance on direction      =     22.500 (degrees)
## Calculation lag             =      1.000
## Tolerance on distance       =     50.000 (Percent of the lag value)
## 
## For variable 1
##       Rank    Npairs  Distance     Value
##          0     1.000     0.344     0.080
##          1    31.000     1.051     1.113
##          2    50.000     1.960     1.890
##          3    62.000     2.999     2.443
##          4    58.000     4.014     2.701
##          5    51.000     5.016     2.702
##          6    36.000     5.999     1.833
##          7    37.000     7.015     2.130
##          8    50.000     7.997     2.060
##          9    53.000     8.995     2.381
## 
## Direction #3
## ------------
## Number of lags              = 10
## Direction coefficients      =      0.000     1.000
## Direction angles (degrees)  =     90.000     0.000
## Tolerance on direction      =     22.500 (degrees)
## Calculation lag             =      1.000
## Tolerance on distance       =     50.000 (Percent of the lag value)
## 
## For variable 1
##       Rank    Npairs  Distance     Value
##          1    32.000     1.149     1.631
##          2    39.000     2.080     1.670
##          3    39.000     2.979     2.511
##          4    48.000     4.012     2.120
##          5    51.000     5.029     3.055
##          6    47.000     5.939     2.856
##          7    49.000     6.965     2.386
##          8    42.000     7.952     2.708
##          9    41.000     9.018     2.320
## 
## Direction #4
## ------------
## Number of lags              = 10
## Direction coefficients      =     -0.707     0.707
## Direction angles (degrees)  =    135.000     0.000
## Tolerance on direction      =     22.500 (degrees)
## Calculation lag             =      1.000
## Tolerance on distance       =     50.000 (Percent of the lag value)
## 
## For variable 1
##       Rank    Npairs  Distance     Value
##          0     1.000     0.411     1.125
##          1    31.000     1.028     1.606
##          2    47.000     2.044     1.496
##          3    51.000     3.040     2.330
##          4    62.000     4.028     1.791
##          5    61.000     5.058     2.155
##          6    55.000     5.939     1.587
##          7    49.000     6.975     3.425
##          8    60.000     8.004     2.408
##          9    53.000     8.972     3.996
## NULL
if (graphics)
    ggplot() + plot.varmod(myvario) + 
    plot.decoration(title="Multi-Directional Variogram of Pb")

Calculating the Variogram Map

myvmap = db_vmap_compute(db=mydb,calcul_type=ECalcVario_VARIOGRAM(),nxx=c(20,20))
if (verbose)
    myvmap$display()
## 
## 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      = 1681
## 
## Grid characteristics:
## ---------------------
## Origin :    -33.160   -29.380
## Mesh   :      1.658     1.469
## Number :         41        41
## 
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = x1 - Locator = x1
## Column = 2 - Name = x2 - Locator = x2
## Column = 3 - Name = VMAP.Pb.Var - Locator = z1
## Column = 4 - Name = VMAP.Pb.Nb - Locator = NA
## NULL
if (graphics)
    ggplot() + plot(myvmap, nameRaster="*Var") + 
    plot.decoration(title="Variogram Map")

Model

Fitting a Model. We call the Automatic Fitting procedure providing the list of covariance functions to be tested.

mymodel = Model_createFromDb(mydb)
err = mymodel$fit(vario=myvario,types=ECov_fromKeys(c("EXPONENTIAL","SPHERICAL")))

Visualizing the resulting model, overlaid on the experimental variogram

if (graphics)
    ggplot() + plot.varmod(myvario,mymodel) + plot.decoration(title="Model for Pb")

Model with equality constraints

We can impose some constraints on the parameters during the fit. For instance here, we impose an equality constraint on the range (range = 1).

myModelConstrained = Model_createFromDb(mydb)
constr = Constraints()
paramid = CovParamId(0,0,EConsElem_RANGE(),0,0)
constr$addItem(ConsItem(paramid,EConsType_EQUAL(),1.))
## NULL
err = myModelConstrained$fit(vario=myVarioOmni,
                             types=ECov_fromKeys(c("EXPONENTIAL","SPHERICAL")),
                             constraints=constr)
myModelConstrained
## 
## Model characteristics
## =====================
## Space dimension              = 2
## Number of variable(s)        = 1
## Number of basic structure(s) = 2
## Number of drift function(s)  = 0
## Number of drift equation(s)  = 0
## 
## Covariance Part
## ---------------
## Exponential
## - Sill         =      1.032
## - Range        =      1.000
## - Theo. Range  =      0.334
## Spherical
## - Sill         =      1.605
## - Range        =      5.880
## Total Sill     =      2.638

We can impose inequality constraints by using EConsType.LOWER or EConsType.UPPER.

Adding a drift

mymodel$addDrift(DriftM())
## NULL
if (verbose)
    mymodel$display()
## 
## Model characteristics
## =====================
## Space dimension              = 2
## Number of variable(s)        = 1
## Number of basic structure(s) = 2
## Number of drift function(s)  = 1
## Number of drift equation(s)  = 1
## 
## Covariance Part
## ---------------
## Exponential
## - Sill         =      1.035
## - Ranges       =      1.786     0.366
## - Theo. Ranges =      0.596     0.122
## - Angles       =     45.023     0.000
## - Rotation Matrix
##                [,  0]    [,  1]
##      [  0,]     0.707    -0.707
##      [  1,]     0.707     0.707
## Spherical
## - Sill         =      1.621
## - Ranges       =      7.051     5.132
## - Angles       =    136.897     0.000
## - Rotation Matrix
##                [,  0]    [,  1]
##      [  0,]    -0.730    -0.683
##      [  1,]     0.683    -0.730
## Total Sill     =      2.656
## 
## Drift Part
## ----------
## Universality_Condition
## NULL

Defining the Neighborhood

We initiate a Neigborhood (Moving with a small number of samples for Demonstration)

myneigh = NeighMoving_create(flag_xvalid=FALSE,nmaxi=6,radius=10)
if (verbose)
    myneigh$display()
## 
## Moving Neighborhood
## ===================
## Minimum number of samples           = 1
## Maximum number of samples           = 6
## Maximum horizontal distance         = 10
## NULL

Checking the Moving Neighborhood

We must first create a Grid which covers the area of interest

mygrid = DbGrid_createCoveringDb(dbin=mydb,nx=c(80,72),dx=c(0.5,0.5),
                                 x0=c(107.,481.),margin=c(2,2))
if (verbose)
    mygrid$display()
## 
## Data Base Grid Characteristics
## ==============================
## 
## Data Base Summary
## -----------------
## File is organized as a regular grid
## Space dimension              = 2
## Number of Columns            = 2
## Total number of samples      = 5913
## 
## Grid characteristics:
## ---------------------
## Origin :    105.000   479.000
## Mesh   :      0.500     0.500
## Number :         81        73
## 
## Variables
## ---------
## Column = 0 - Name = x1 - Locator = x1
## Column = 1 - Name = x2 - Locator = x2
## NULL

We can now test the neighborhood characteristics for each node of the previously defined grid.

err = test_neigh(mydb,mygrid,mymodel,myneigh)
if (verbose)
    mygrid$display()
## 
## Data Base Grid Characteristics
## ==============================
## 
## Data Base Summary
## -----------------
## File is organized as a regular grid
## Space dimension              = 2
## Number of Columns            = 7
## Total number of samples      = 5913
## 
## Grid characteristics:
## ---------------------
## Origin :    105.000   479.000
## Mesh   :      0.500     0.500
## Number :         81        73
## 
## Variables
## ---------
## Column = 0 - Name = x1 - Locator = x1
## Column = 1 - Name = x2 - Locator = x2
## Column = 2 - Name = Neigh.Pb.Number - Locator = NA
## Column = 3 - Name = Neigh.Pb.MaxDist - Locator = NA
## Column = 4 - Name = Neigh.Pb.MinDist - Locator = NA
## Column = 5 - Name = Neigh.Pb.NbNESect - Locator = NA
## Column = 6 - Name = Neigh.Pb.NbCESect - Locator = z1
## NULL

We can visualize some of the newly created variables, such as:

  • the number of points per neighborhood
if (graphics)
{
  p = ggplot()
  p = p + plot(mygrid,nameRaster="Neigh*Number")
  p = p + plot.decoration(title="Number of Samples per Neighborhood")
  ggPrint(p)
}

  • the one giving the maximum distance per neighborhood
if (graphics)
{
  p = ggplot()
  p = p + plot(mygrid,nameRaster="Neigh*MaxDist")
  p = p + plot.decoration(title="Maximum Distance per Neighborhood")
  ggPrint(p)
}

Cross-validation

We can now process the cross-validation step

err = xvalid(mydb,mymodel,myneigh)
if (verbose)
    mydb$display()
## 
## Data Base Characteristics
## =========================
## 
## Data Base Summary
## -----------------
## File is organized as a set of isolated points
## Space dimension              = 2
## Number of Columns            = 8
## Total number of samples      = 102
## Number of active samples     = 99
## 
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = X - Locator = x1
## Column = 2 - Name = Y - Locator = x2
## Column = 3 - Name = Zn - Locator = NA
## Column = 4 - Name = Pb - Locator = NA
## Column = 5 - Name = sel - Locator = sel
## Column = 6 - Name = Xvalid.Pb.esterr - Locator = z1
## Column = 7 - Name = Xvalid.Pb.stderr - Locator = NA
## NULL
if (graphics)
{
  p = ggplot()
  p = p + plot.hist(mydb,"Xvalid.Pb.stderr")
  p = p + plot.decoration(title="Histogram of Stdandrdized Errors")
  ggPrint(p)
}
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Estimating by Kriging

We now perform the Estimation by Ordinary Kriging. The Neighborhood is changed into a Unique Neighborhood.

mydb$setLocator("Pb",ELoc_Z())
## NULL
myneigh = NeighUnique_create()
err = kriging(mydb,mygrid,mymodel,myneigh)
if (verbose)
    mygrid$display()
## 
## Data Base Grid Characteristics
## ==============================
## 
## Data Base Summary
## -----------------
## File is organized as a regular grid
## Space dimension              = 2
## Number of Columns            = 9
## Total number of samples      = 5913
## 
## Grid characteristics:
## ---------------------
## Origin :    105.000   479.000
## Mesh   :      0.500     0.500
## Number :         81        73
## 
## Variables
## ---------
## Column = 0 - Name = x1 - Locator = x1
## Column = 1 - Name = x2 - Locator = x2
## Column = 2 - Name = Neigh.Pb.Number - Locator = NA
## Column = 3 - Name = Neigh.Pb.MaxDist - Locator = NA
## Column = 4 - Name = Neigh.Pb.MinDist - Locator = NA
## Column = 5 - Name = Neigh.Pb.NbNESect - Locator = NA
## Column = 6 - Name = Neigh.Pb.NbCESect - Locator = NA
## Column = 7 - Name = Kriging.Pb.estim - Locator = z1
## Column = 8 - Name = Kriging.Pb.stdev - Locator = NA
## NULL

Visualizing the results

if (graphics)
{
  p = ggplot()
  p = p + plot(mygrid,nameRaster="Kriging.Pb.estim")
  p = p + plot(mydb,nameSize="Pb", color="yellow")
  p = p + plot.decoration(title="Estimate of Pb")
  ggPrint(p)
}

if (graphics)
{
  p = ggplot()
  p = p + plot(mygrid,nameRaster="Kriging.Pb.stdev", flagLegendRaster=TRUE)
  p = p + plot(mydb,"Pb", color="yellow", flagCst=TRUE)
  p = p + plot.decoration(title="St. Deviation of Pb")
  ggPrint(p)
}

Simulations

We must first transform the Data into Gaussian

myanamPb = AnamHermite_create(nbpoly=30)
err = myanamPb$fitFromLocator(mydb)
if (verbose)
    myanamPb
## 
## Hermitian Anamorphosis
## ----------------------
## Minimum absolute value for Y  = -2.7
## Maximum absolute value for Y  = 2.6
## Minimum absolute value for Z  = 3.0029
## Maximum absolute value for Z  = 12.9777
## Minimum practical value for Y = -2.7
## Maximum practical value for Y = 2.6
## Minimum practical value for Z = 3.0029
## Maximum practical value for Z = 12.9777
## Mean                          = 5.65758
## Variance                      = 2.86296
## Number of Hermite polynomials = 30
## Normalized coefficients for Hermite polynomials (punctual variable)
##                [,  0]    [,  1]    [,  2]    [,  3]    [,  4]    [,  5]    [,  6]
##      [  0,]     5.658    -1.625     0.440    -0.069    -0.017     0.082    -0.061
##      [  7,]     0.001     0.036    -0.044     0.004     0.047    -0.030    -0.029
##      [ 14,]     0.037     0.007    -0.031     0.010     0.018    -0.019    -0.003
##      [ 21,]     0.019    -0.010    -0.014     0.019     0.006    -0.023     0.004
##      [ 28,]     0.022    -0.013

We can produce the Gaussian Anamorphosis graphically within its definition domain.

if (graphics)
    ggplot() + plot(myanamPb)

The next step consists in translating the target variable (‘Pb’) into its Gaussian transform

mydb$setLocator("Pb",ELoc_Z())
## NULL
err = myanamPb$rawToGaussianByLocator(mydb)
if (verbose)
    mydb$display()
## 
## Data Base Characteristics
## =========================
## 
## Data Base Summary
## -----------------
## File is organized as a set of isolated points
## Space dimension              = 2
## Number of Columns            = 9
## Total number of samples      = 102
## Number of active samples     = 99
## 
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = X - Locator = x1
## Column = 2 - Name = Y - Locator = x2
## Column = 3 - Name = Zn - Locator = NA
## Column = 4 - Name = Pb - Locator = NA
## Column = 5 - Name = sel - Locator = sel
## Column = 6 - Name = Xvalid.Pb.esterr - Locator = NA
## Column = 7 - Name = Xvalid.Pb.stderr - Locator = NA
## Column = 8 - Name = Y.Pb - Locator = z1
## NULL

We quickly calculate experimental (omni-directional) variograms using the already defined directions.

myvarioParam = VarioParam()
mydir = DirParam_create(npas=10,dpas=1.)
myvarioParam$addDir(mydir)
## NULL
myVario = Vario(myvarioParam)
err = myvario$compute(mydb,ECalcVario_VARIOGRAM())

We fit the model by automatic fit (with the constraints that the total sill be equal to 1).

mymodelG = Model_createFromDb(mydb)
err = mymodelG$fit(myvario,types=ECov_fromKeys(c("EXPONENTIAL")))
if (graphics)
    ggplot() + plot.varmod(myvario,mymodelG) + plot.decoration(title="Model for Gaussian Pb")

We perform a set of 10 conditional simulations using the Turning Bands Method.

err = simtub(mydb,mygrid,mymodel,myneigh,nbsimu=10)
if (verbose)
    mygrid$display()
## 
## Data Base Grid Characteristics
## ==============================
## 
## Data Base Summary
## -----------------
## File is organized as a regular grid
## Space dimension              = 2
## Number of Columns            = 19
## Total number of samples      = 5913
## 
## Grid characteristics:
## ---------------------
## Origin :    105.000   479.000
## Mesh   :      0.500     0.500
## Number :         81        73
## 
## Variables
## ---------
## Column = 0 - Name = x1 - Locator = x1
## Column = 1 - Name = x2 - Locator = x2
## Column = 2 - Name = Neigh.Pb.Number - Locator = NA
## Column = 3 - Name = Neigh.Pb.MaxDist - Locator = NA
## Column = 4 - Name = Neigh.Pb.MinDist - Locator = NA
## Column = 5 - Name = Neigh.Pb.NbNESect - Locator = NA
## Column = 6 - Name = Neigh.Pb.NbCESect - Locator = NA
## Column = 7 - Name = Kriging.Pb.estim - Locator = NA
## Column = 8 - Name = Kriging.Pb.stdev - Locator = NA
## Column = 9 - Name = Simu.Y.Pb.1 - Locator = z1
## Column = 10 - Name = Simu.Y.Pb.2 - Locator = z2
## Column = 11 - Name = Simu.Y.Pb.3 - Locator = z3
## Column = 12 - Name = Simu.Y.Pb.4 - Locator = z4
## Column = 13 - Name = Simu.Y.Pb.5 - Locator = z5
## Column = 14 - Name = Simu.Y.Pb.6 - Locator = z6
## Column = 15 - Name = Simu.Y.Pb.7 - Locator = z7
## Column = 16 - Name = Simu.Y.Pb.8 - Locator = z8
## Column = 17 - Name = Simu.Y.Pb.9 - Locator = z9
## Column = 18 - Name = Simu.Y.Pb.10 - Locator = z10
## NULL

Some statistics on the Conditional simulations in Gaussian scale

if (verbose)
  dbStatisticsMono(mygrid, mygrid$getNamesByLocator(ELoc_Z()),
        opers = EStatOption_fromKeys(c("MEAN","STDV","MINI","MAXI")))$display()
##                    Mean   St. Dev.    Minimum    Maximum
##  Simu.Y.Pb.1     -0.052      1.582     -6.234      6.963
##  Simu.Y.Pb.2      0.081      1.451     -5.167      6.113
##  Simu.Y.Pb.3      0.133      1.529     -5.363      5.639
##  Simu.Y.Pb.4      0.453      1.478     -6.322      5.836
##  Simu.Y.Pb.5     -0.084      1.577     -6.403      5.578
##  Simu.Y.Pb.6      0.316      1.551     -4.816      6.297
##  Simu.Y.Pb.7      0.185      1.598     -6.441      6.127
##  Simu.Y.Pb.8      0.705      1.599     -4.713      7.175
##  Simu.Y.Pb.9      0.159      1.639     -6.660      7.135
## Simu.Y.Pb.10      0.310      1.553     -5.955      4.807
## NULL

We visualize a conditional simulation in Gaussian scale

if (graphics)
{
    p = ggplot()
    p = p + plot(mygrid,nameRaster="Simu.Y.Pb.1")
    p = p + plot(mydb,"Pb")
    p = p + plot.decoration(title="One Simulation of Pb in Gaussian Scale")
    ggPrint(p)
}

We turn the Gaussian conditional simulations into Raw scale (using the Anamorphosis back transform) and get rid of the Gaussian conditional simulations.

err = myanamPb$gaussianToRaw(mygrid,name="Simu.Y.*")
mygrid$deleteColumn("Simu.Y.*")
## NULL
if (verbose)
    mygrid$display()
## 
## Data Base Grid Characteristics
## ==============================
## 
## Data Base Summary
## -----------------
## File is organized as a regular grid
## Space dimension              = 2
## Number of Columns            = 19
## Total number of samples      = 5913
## 
## Grid characteristics:
## ---------------------
## Origin :    105.000   479.000
## Mesh   :      0.500     0.500
## Number :         81        73
## 
## Variables
## ---------
## Column = 0 - Name = x1 - Locator = x1
## Column = 1 - Name = x2 - Locator = x2
## Column = 2 - Name = Neigh.Pb.Number - Locator = NA
## Column = 3 - Name = Neigh.Pb.MaxDist - Locator = NA
## Column = 4 - Name = Neigh.Pb.MinDist - Locator = NA
## Column = 5 - Name = Neigh.Pb.NbNESect - Locator = NA
## Column = 6 - Name = Neigh.Pb.NbCESect - Locator = NA
## Column = 7 - Name = Kriging.Pb.estim - Locator = NA
## Column = 8 - Name = Kriging.Pb.stdev - Locator = NA
## Column = 9 - Name = Z.Simu.Y.Pb.1 - Locator = z1
## Column = 10 - Name = Z.Simu.Y.Pb.2 - Locator = z2
## Column = 11 - Name = Z.Simu.Y.Pb.3 - Locator = z3
## Column = 12 - Name = Z.Simu.Y.Pb.4 - Locator = z4
## Column = 13 - Name = Z.Simu.Y.Pb.5 - Locator = z5
## Column = 14 - Name = Z.Simu.Y.Pb.6 - Locator = z6
## Column = 15 - Name = Z.Simu.Y.Pb.7 - Locator = z7
## Column = 16 - Name = Z.Simu.Y.Pb.8 - Locator = z8
## Column = 17 - Name = Z.Simu.Y.Pb.9 - Locator = z9
## Column = 18 - Name = Z.Simu.Y.Pb.10 - Locator = z10
## NULL

We calculate some statistics on the Conditional Simulations in Raw scale.

if (verbose)
  dbStatisticsMono(mygrid, mygrid$getNamesByLocator(ELoc_Z()),
        opers = EStatOption_fromKeys(c("MEAN","STDV","MINI","MAXI")))$display()
##                      Mean   St. Dev.    Minimum    Maximum
##  Z.Simu.Y.Pb.1      5.965      2.647      3.003     12.978
##  Z.Simu.Y.Pb.2      6.113      2.550      3.003     12.978
##  Z.Simu.Y.Pb.3      6.238      2.703      3.003     12.978
##  Z.Simu.Y.Pb.4      6.777      2.817      3.003     12.978
##  Z.Simu.Y.Pb.5      5.937      2.599      3.003     12.978
##  Z.Simu.Y.Pb.6      6.568      2.858      3.003     12.978
##  Z.Simu.Y.Pb.7      6.384      2.829      3.003     12.978
##  Z.Simu.Y.Pb.8      7.270      3.087      3.003     12.978
##  Z.Simu.Y.Pb.9      6.362      2.862      3.003     12.978
## Z.Simu.Y.Pb.10      6.575      2.851      3.003     12.978
## NULL

We visualize a Conditional Simulation in Raw Scale

if (graphics)
{
  p = ggplot()
  p = p + plot(mygrid,nameRaster="Z.Simu.Y.Pb.1")
  p = p + plot(mydb,"Pb")
  p = p + plot.decoration(title="One simulation of Pb in Raw Scale")
  ggPrint(p)
}

Let us now average the conditional simulations in order to have a comparison with the estimation by kriging.

mygrid$statisticsBySample(mygrid$getNamesByLocator(ELoc_Z()),
                                 opers = EStatOption_fromKeys(c("MEAN")))
## NULL
if (verbose)
    mygrid$display()
## 
## Data Base Grid Characteristics
## ==============================
## 
## Data Base Summary
## -----------------
## File is organized as a regular grid
## Space dimension              = 2
## Number of Columns            = 20
## Total number of samples      = 5913
## 
## Grid characteristics:
## ---------------------
## Origin :    105.000   479.000
## Mesh   :      0.500     0.500
## Number :         81        73
## 
## Variables
## ---------
## Column = 0 - Name = x1 - Locator = x1
## Column = 1 - Name = x2 - Locator = x2
## Column = 2 - Name = Neigh.Pb.Number - Locator = NA
## Column = 3 - Name = Neigh.Pb.MaxDist - Locator = NA
## Column = 4 - Name = Neigh.Pb.MinDist - Locator = NA
## Column = 5 - Name = Neigh.Pb.NbNESect - Locator = NA
## Column = 6 - Name = Neigh.Pb.NbCESect - Locator = NA
## Column = 7 - Name = Kriging.Pb.estim - Locator = NA
## Column = 8 - Name = Kriging.Pb.stdev - Locator = NA
## Column = 9 - Name = Z.Simu.Y.Pb.1 - Locator = NA
## Column = 10 - Name = Z.Simu.Y.Pb.2 - Locator = NA
## Column = 11 - Name = Z.Simu.Y.Pb.3 - Locator = NA
## Column = 12 - Name = Z.Simu.Y.Pb.4 - Locator = NA
## Column = 13 - Name = Z.Simu.Y.Pb.5 - Locator = NA
## Column = 14 - Name = Z.Simu.Y.Pb.6 - Locator = NA
## Column = 15 - Name = Z.Simu.Y.Pb.7 - Locator = NA
## Column = 16 - Name = Z.Simu.Y.Pb.8 - Locator = NA
## Column = 17 - Name = Z.Simu.Y.Pb.9 - Locator = NA
## Column = 18 - Name = Z.Simu.Y.Pb.10 - Locator = NA
## Column = 19 - Name = Stats.MEAN - Locator = z1
## NULL

Displaying the average of the Conditional Simulations

if (graphics)
{
  p = ggplot()
  p = p + plot(mygrid,nameRaster="Stats*MEAN")
  p = p + plot(mydb,"Pb")
  p = p + plot.decoration(title="Mean of Pb simulations")
  ggPrint(p)
}

Multivariate case

The Gaussian transform of the Pb variable has already been calculated. It suffices to perform the Gaussian transform of the Zn variable.

mydb$setLocator("Zn",ELoc_Z())
## NULL
myanamZn = AnamHermite(nbpoly=30)
myanamZn$fit(mydb, "Zn")
## [1] 0
if (verbose)
    myanamZn
## 
## Hermitian Anamorphosis
## ----------------------
## Minimum absolute value for Y  = -2.5
## Maximum absolute value for Y  = 2.6
## Minimum absolute value for Z  = 1.1469
## Maximum absolute value for Z  = 12.1276
## Minimum practical value for Y = -2.5
## Maximum practical value for Y = 2.6
## Minimum practical value for Z = 1.1469
## Maximum practical value for Z = 12.1276
## Mean                          = 2.88061
## Variance                      = 2.76263
## Number of Hermite polynomials = 30
## Normalized coefficients for Hermite polynomials (punctual variable)
##                [,  0]    [,  1]    [,  2]    [,  3]    [,  4]    [,  5]    [,  6]
##      [  0,]     2.881    -1.277     0.877    -0.447    -0.095     0.294    -0.121
##      [  7,]    -0.087     0.134    -0.029    -0.087     0.069     0.034    -0.065
##      [ 14,]     0.005     0.044    -0.026    -0.020     0.034     0.001    -0.033
##      [ 21,]     0.010     0.027    -0.016    -0.019     0.016     0.012    -0.014
##      [ 28,]    -0.005     0.011
if (graphics)
{
  p = ggplot()
  p = p + plot(myanamZn)
  p = p + plot.decoration(title="Gaussian Anamorphosis for Zn")
  ggPrint(p)
}

We convert the raw data into its Gaussian equivalent

mydb$setLocator("Zn",ELoc_Z())
## NULL
err = myanamZn$rawToGaussianByLocator(mydb)
if (verbose)
    mydb$display()
## 
## Data Base Characteristics
## =========================
## 
## Data Base Summary
## -----------------
## File is organized as a set of isolated points
## Space dimension              = 2
## Number of Columns            = 10
## Total number of samples      = 102
## Number of active samples     = 99
## 
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = X - Locator = x1
## Column = 2 - Name = Y - Locator = x2
## Column = 3 - Name = Zn - Locator = NA
## Column = 4 - Name = Pb - Locator = NA
## Column = 5 - Name = sel - Locator = sel
## Column = 6 - Name = Xvalid.Pb.esterr - Locator = NA
## Column = 7 - Name = Xvalid.Pb.stderr - Locator = NA
## Column = 8 - Name = Y.Pb - Locator = NA
## Column = 9 - Name = Y.Zn - Locator = z1
## NULL

We now perform the multivariate variogram calculation

mydb$setLocator("Y.Pb",ELoc_Z(),0)
## NULL
mydb$setLocator("Y.Zn",ELoc_Z(),1)
## NULL
myvario = Vario(myvarioParam)
err = myvario$compute(mydb,ECalcVario_VARIOGRAM())
mymodelM = Model_createFromDb(mydb)
err = mymodelM$fit(myvario,ECov_fromKeys(c("EXPONENTIAL")))
if (graphics)
    multi.varmod(myvario)

We perform 10 bivariate conditional simulations (deleting the previous monovariate simulation outcomes first for better legibility).

mygrid$deleteColumn("Z.Simu*")
## NULL
err = simtub(mydb,mygrid,mymodelM,myneigh,nbsimu=10)
if (verbose)
    mygrid$display()
## 
## Data Base Grid Characteristics
## ==============================
## 
## Data Base Summary
## -----------------
## File is organized as a regular grid
## Space dimension              = 2
## Number of Columns            = 30
## Total number of samples      = 5913
## 
## Grid characteristics:
## ---------------------
## Origin :    105.000   479.000
## Mesh   :      0.500     0.500
## Number :         81        73
## 
## Variables
## ---------
## Column = 0 - Name = x1 - Locator = x1
## Column = 1 - Name = x2 - Locator = x2
## Column = 2 - Name = Neigh.Pb.Number - Locator = NA
## Column = 3 - Name = Neigh.Pb.MaxDist - Locator = NA
## Column = 4 - Name = Neigh.Pb.MinDist - Locator = NA
## Column = 5 - Name = Neigh.Pb.NbNESect - Locator = NA
## Column = 6 - Name = Neigh.Pb.NbCESect - Locator = NA
## Column = 7 - Name = Kriging.Pb.estim - Locator = NA
## Column = 8 - Name = Kriging.Pb.stdev - Locator = NA
## Column = 9 - Name = Stats.MEAN - Locator = NA
## Column = 10 - Name = Simu.Y.Pb.1 - Locator = z1
## Column = 11 - Name = Simu.Y.Pb.2 - Locator = z2
## Column = 12 - Name = Simu.Y.Pb.3 - Locator = z3
## Column = 13 - Name = Simu.Y.Pb.4 - Locator = z4
## Column = 14 - Name = Simu.Y.Pb.5 - Locator = z5
## Column = 15 - Name = Simu.Y.Pb.6 - Locator = z6
## Column = 16 - Name = Simu.Y.Pb.7 - Locator = z7
## Column = 17 - Name = Simu.Y.Pb.8 - Locator = z8
## Column = 18 - Name = Simu.Y.Pb.9 - Locator = z9
## Column = 19 - Name = Simu.Y.Pb.10 - Locator = z10
## Column = 20 - Name = Simu.Y.Zn.1 - Locator = z11
## Column = 21 - Name = Simu.Y.Zn.2 - Locator = z12
## Column = 22 - Name = Simu.Y.Zn.3 - Locator = z13
## Column = 23 - Name = Simu.Y.Zn.4 - Locator = z14
## Column = 24 - Name = Simu.Y.Zn.5 - Locator = z15
## Column = 25 - Name = Simu.Y.Zn.6 - Locator = z16
## Column = 26 - Name = Simu.Y.Zn.7 - Locator = z17
## Column = 27 - Name = Simu.Y.Zn.8 - Locator = z18
## Column = 28 - Name = Simu.Y.Zn.9 - Locator = z19
## Column = 29 - Name = Simu.Y.Zn.10 - Locator = z20
## NULL

We back-transform each set of simulation outcomes using its own Gaussian Anamorphosis function. Finally we delete the Gaussian variables and ask for the statistics on the simulated variables in the Raw Scale.

err = myanamZn$gaussianToRaw(mygrid,"Simu.Y.Zn*")
err = myanamPb$gaussianToRaw(mygrid,"Simu.Y.Pb*")
mygrid$deleteColumn("Simu.Y*")
## NULL
if (verbose)
    dbStatisticsMono(mygrid, mygrid$getNamesByLocator(ELoc_Z()),
          opers = EStatOption_fromKeys(c("MEAN","STDV","MINI","MAXI")))$display()
##                      Mean   St. Dev.    Minimum    Maximum
##  Z.Simu.Y.Pb.1      5.854      1.880      3.003     12.978
##  Z.Simu.Y.Pb.2      5.731      1.743      3.003     12.978
##  Z.Simu.Y.Pb.3      5.630      1.681      3.003     12.978
##  Z.Simu.Y.Pb.4      5.811      1.906      3.003     12.978
##  Z.Simu.Y.Pb.5      5.724      1.728      3.003     12.978
##  Z.Simu.Y.Pb.6      5.717      1.805      3.003     12.978
##  Z.Simu.Y.Pb.7      5.922      1.825      3.003     12.978
##  Z.Simu.Y.Pb.8      5.742      1.803      3.003     12.978
##  Z.Simu.Y.Pb.9      5.788      1.841      3.003     12.978
## Z.Simu.Y.Pb.10      5.778      1.774      3.003     12.978
## NULL

Categorical Variable

We compare the initial variable ‘Pb’ with a set of disjoint intervals. The ‘Pb’ values varying from 3 to 12.7, we consider three classes:

We first build the indicators for each class.

limits = Limits(c(NA, 4., 6., NA))
if (verbose)
    limits$display()
## Bound( 1 ) : ] -Inf ; 4 [
## Bound( 2 ) : [ 4 ; 6 [
## Bound( 3 ) : [ 6 ;  +Inf [
## NULL

We apply the set of limits previously defined in order to transform the input variable into Indicators of the different classes.

err = limits$toIndicator(mydb,name="Pb")
if (verbose)
    mydb$display()
## 
## Data Base Characteristics
## =========================
## 
## Data Base Summary
## -----------------
## File is organized as a set of isolated points
## Space dimension              = 2
## Number of Columns            = 13
## Total number of samples      = 102
## Number of active samples     = 99
## 
## Variables
## ---------
## Column = 0 - Name = rank - Locator = NA
## Column = 1 - Name = X - Locator = x1
## Column = 2 - Name = Y - Locator = x2
## Column = 3 - Name = Zn - Locator = NA
## Column = 4 - Name = Pb - Locator = NA
## Column = 5 - Name = sel - Locator = sel
## Column = 6 - Name = Xvalid.Pb.esterr - Locator = NA
## Column = 7 - Name = Xvalid.Pb.stderr - Locator = NA
## Column = 8 - Name = Y.Pb - Locator = NA
## Column = 9 - Name = Y.Zn - Locator = NA
## Column = 10 - Name = Indicator.Pb.Class.1 - Locator = z1
## Column = 11 - Name = Indicator.Pb.Class.2 - Locator = z2
## Column = 12 - Name = Indicator.Pb.Class.3 - Locator = z3
## NULL

We calculate the variogram of the Indicators for future use

myvarioindParam = VarioParam()
myvarioindParam$addDir(mydir)
## NULL
myvarioInd = Vario(myvarioindParam)
err = myvarioInd$compute(mydb,ECalcVario_VARIOGRAM())
if (verbose)
    myvarioInd$display()
## 
## Variogram characteristics
## =========================
## Number of variable(s)       = 3
## Number of direction(s)      = 1
## Space dimension             = 2
## Variance-Covariance Matrix
##                [,  0]    [,  1]    [,  2]
##      [  0,]     0.107    -0.062    -0.044
##      [  1,]    -0.062     0.250    -0.187
##      [  2,]    -0.044    -0.187     0.231
## 
## Direction #1
## ------------
## Number of lags              = 10
## Direction coefficients      =      1.000     0.000
## Direction angles (degrees)  =      0.000     0.000
## Tolerance on direction      =     90.000 (degrees)
## Calculation lag             =      1.000
## Tolerance on distance       =     50.000 (Percent of the lag value)
## 
## For variable 1
##       Rank    Npairs  Distance     Value
##          0     3.000     0.389     0.000
##          1   123.000     1.081     0.081
##          2   183.000     2.038     0.126
##          3   205.000     3.006     0.156
##          4   231.000     4.013     0.132
##          5   229.000     5.036     0.159
##          6   198.000     5.962     0.152
##          7   187.000     7.000     0.107
##          8   204.000     7.996     0.096
##          9   184.000     8.990     0.068
## 
## For variables 2 and 1
##       Rank    Npairs  Distance     Value
##          0     3.000     0.389     0.000
##          1   123.000     1.081    -0.065
##          2   183.000     2.038    -0.077
##          3   205.000     3.006    -0.085
##          4   231.000     4.013    -0.093
##          5   229.000     5.036    -0.085
##          6   198.000     5.962    -0.061
##          7   187.000     7.000    -0.045
##          8   204.000     7.996    -0.042
##          9   184.000     8.990    -0.038
## 
## For variable 2
##       Rank    Npairs  Distance     Value
##          0     3.000     0.389     0.167
##          1   123.000     1.081     0.199
##          2   183.000     2.038     0.221
##          3   205.000     3.006     0.251
##          4   231.000     4.013     0.292
##          5   229.000     5.036     0.258
##          6   198.000     5.962     0.237
##          7   187.000     7.000     0.254
##          8   204.000     7.996     0.228
##          9   184.000     8.990     0.234
## 
## For variables 3 and 1
##       Rank    Npairs  Distance     Value
##          0     3.000     0.389     0.000
##          1   123.000     1.081    -0.016
##          2   183.000     2.038    -0.049
##          3   205.000     3.006    -0.071
##          4   231.000     4.013    -0.039
##          5   229.000     5.036    -0.074
##          6   198.000     5.962    -0.091
##          7   187.000     7.000    -0.061
##          8   204.000     7.996    -0.054
##          9   184.000     8.990    -0.030
## 
## For variables 3 and 2
##       Rank    Npairs  Distance     Value
##          0     3.000     0.389    -0.167
##          1   123.000     1.081    -0.134
##          2   183.000     2.038    -0.145
##          3   205.000     3.006    -0.166
##          4   231.000     4.013    -0.199
##          5   229.000     5.036    -0.172
##          6   198.000     5.962    -0.177
##          7   187.000     7.000    -0.209
##          8   204.000     7.996    -0.186
##          9   184.000     8.990    -0.196
## 
## For variable 3
##       Rank    Npairs  Distance     Value
##          0     3.000     0.389     0.167
##          1   123.000     1.081     0.150
##          2   183.000     2.038     0.194
##          3   205.000     3.006     0.237
##          4   231.000     4.013     0.238
##          5   229.000     5.036     0.247
##          6   198.000     5.962     0.268
##          7   187.000     7.000     0.270
##          8   204.000     7.996     0.240
##          9   184.000     8.990     0.226
## NULL
if (graphics)
    multi.varmod(myvarioInd)

Then we build a categorical variable which gives the index of the class to which each sample belongs

err = limits$toCategory(mydb,"Pb")
if (verbose)
{
  dbfmt = DbStringFormat()
  dbfmt$setFlags(flag_resume = FALSE,
                 flag_vars = FALSE,
                 flag_stats = TRUE)
  dbfmt$setNames("Category*")
  dbfmt$setMode(mode=2) # Consider the variable categorical
  mydb$display(dbfmt)
}
## 
## Data Base Characteristics
## =========================
## 
## Data Base Statistics
## --------------------
## 14 - Name Category.Pb - Locator z1
##  Nb of data          =        102
##  Nb of active values =         99
##  Class         1 =         12 (    12.121%)
##  Class         2 =         51 (    51.515%)
##  Class         3 =         36 (    36.364%)
## NULL