spde {RGeostats} | R Documentation |
Perform Estimation or Simulations using SPDE technology
spde(dbin = NA, dbout, model, triswitch = "nqQ", nostat = NA, gext=NA, mean=NA, seed = 232131, mesh.dbin=TRUE, mesh.dbout=TRUE, cov.extract=NA, flag.est= FALSE, flag.std=FALSE, flag.gibbs=FALSE, flag.modif=FALSE, nbsimu = 1, ngburn = 50, ngiter = 100, ngint=5, verbose = FALSE, mesh=NA, Q1rows=NA, Q1cols=NA, Q1vals=NA, Q1nrow=0, Q1ncol=0, Q2rows=NA, Q2cols=NA, Q2vals=NA, Q2nrow=0, Q2ncol=0, Q3rows=NA, Q3cols=NA, Q3vals=NA, Q3nrow=0, Q3ncol=0, A1rows=NA, A1cols=NA, A1vals=NA, A1nrow=0, A1ncol=0, A2rows=NA, A2cols=NA, A2vals=NA, A2nrow=0, A2ncol=0, A3rows=NA, A3cols=NA, A3vals=NA, A3nrow=0, A3ncol=0, radix = "SPDE", modify.target = db.locmod())
dbin |
The |
dbout |
The |
model |
The |
triswitch |
Command line for the internal triangulation step. For more information see
|
nostat |
List of non-stationary parameters.
For details see |
gext |
The 'dbout' may be dilated by gext. This argument designates an array, with its dimension equal to the dimension of the space and which contains the extension in each direction. If not defined, the 'dbout' is not dilated and the simulated results may suffer some edge effect problems. |
mean |
Array containing the mean of each variable. |
seed |
Seed for the random number generation. |
mesh.dbin |
When TRUE, the location corresponding to the Input Data are systematically included in the meshing |
mesh.dbout |
When TRUE, the location corresponding to the Output Targets are systematically included in the meshing |
cov.extract |
List of the ranks of the basic covariance structures to be extracted. For the time being, only the nugget component (if any) can be filtered. |
flag.est |
When TRUE, the estimation is calculated. |
flag.std |
When TRUE, the standard deviation of the estimation error is calculated. |
flag.gibbs |
When TRUE, the iterative Gibbs method is used. |
flag.modif |
When TRUE, the simulation outcomes are not stored individually. Instead the simulations outcomes are summarized in two output variables, i.e. the mean and standard deviation of dispersion of the simulations, which can be considered respectively as the conditional expectation and the conditional variance. |
nbsimu |
Number of simulations. |
ngburn |
Number of burning iterations when the iterative Gibbs method is used as a simulation procedure. During these burning simulations, the intervals are gradually restrained from almost no constraint down to the final constraints. |
ngiter |
Number of iterations when the iterative Gibbs method is used as a simulation procedure. |
ngint |
Number of iterations inside the Gibbs sampler iterative algorithm |
verbose |
Verbose option |
mesh |
A |
Q1nrow, Q1ncol, Q1rows, Q1cols, Q1vals |
For the first internal Q matrix, If Q1rows (first argument) is a sparse matrix (from 'Matrix' package), then the following arguments ('Q1cols', 'Q1vals', 'Q1nrow' and 'Q1ncol') are useless. Otherwise three arrays (same dimension) which provide respectively the row, the column indices, as well as the value for the corresponding cell element. |
Q2nrow, Q2ncol, Q2rows, Q2cols, Q2vals |
For the second internal Q matrix, If Q2rows (first argument) is a sparse matrix (from 'Matrix' package), then the following arguments ('Q2cols', 'Q2vals', 'Q2nrow' and 'Q2ncol') are useless. Otherwise three arrays (same dimension) which provide respectively the row, the column indices, as well as the value for the corresponding cell element. |
Q3nrow, Q3ncol, Q3rows, Q3cols, Q3vals |
For the third internal Q matrix, If Q2rows (first argument) is a sparse matrix (from 'Matrix' package), then the following arguments ('Q2cols', 'Q2vals', 'Q2nrow' and 'Q2ncol') are useless. Otherwise three arrays (same dimension) which provide respectively the row, the column indices, as well as the value for the corresponding cell element. |
A1nrow, A1ncol, A1rows, A1cols, A1vals |
For the first internal A interpolation matrix, If A1rows (first argument) is a sparse matrix (from 'Matrix' package), then the following arguments ('A1cols', 'A1vals', 'A1nrow' and 'A1ncol') are useless. Otherwise three arrays (same dimension) which provide respectively the row, the column indices, as well as the value for the corresponding cell element. |
A2nrow, A2ncol, A2rows, A2cols, A2vals |
For the second internal A interpolation matrix, If A2rows (first argument) is a sparse matrix (from 'Matrix' package), then the following arguments ('A2cols', 'A2vals', 'A2nrow' and 'A2ncol') are useless. Otherwise three arrays (same dimension) which provide respectively the row, the column indices, as well as the value for the corresponding cell element. |
A3nrow, A3ncol, A3rows, A3cols, A3vals |
For the third internal A interpolation matrix, If A3rows (first argument) is a sparse matrix (from 'Matrix' package), then the following arguments ('A3cols', 'A3vals', 'A3nrow' and 'A3ncol') are useless. Otherwise three arrays (same dimension) which provide respectively the row, the column indices, as well as the value for the corresponding cell element. |
radix |
Radix of the name given to the variables storing the results in the target Db. |
modify.target |
Decides whether or not the newly created variables will have their
locator defined or not. For more information, see |
The Double Quilting can be switched OFF by using the function set.keypair() with the keyword "Flag_Double_Quilt". By default, its value is set to 1.
The keypair mechanism has also been used to transmit the some values calculated internally to the user with the keywords:
SPDE_alpha_value The value of the Alpha parameter
SPDE_blin_coefficients The array of b_lin coefficients
Another use of the keypair mechanism is used to introduce a set of faults (that can only be used in the 2-D procedure using triangulation). For that sake, it suffices to use: set.keypair("Intercept_Faults",segments,nseg,2) where the argument 'segments' is a matrix with 2 columns and as many rows as they are fault vertices.
Using set.keypair("Save_Indices",1) will allow saving the indices of the points (target, data, steiner) involved in the establishment of the Q matrix for each one of the parts.
Using set.keypair("Flag_Simu_Chol",0) specifies if either Chebychev (0) or Cholesky (1) algorithm is used for the non conditional simulations.
Using set.keypair("Number_Polynomials_Chebychev",10001), we can define explicitely the number of polynomials used in the Chebychev approximation.
Using set.keypair("Chebychev_Tolerance",1e-6), we can define explicitely the tolerance of the polynomial approximation.
Using get.keypair("B.maxcol.sumabsrow"), we obtain the maximum (over the lines) of the sum of the absolute value of the elements of each row.
Using get.keypair("SPDE_DEBUG"), we set the verbose level enabling DEBUG option.
The output db-class
where the following variables have
been added:
In the case of Estimation
the estimation variable (if flag.est=TRUE)
the standard deviation variable (if flag.std=TRUE)
In the case of Simulations
either the individual simulations
or two variables corresponding to the mean of the standard deviation of dispersion of the simulations