rbfST.cv | R Documentation |
It generates the RMSPE value, which is given by the radial basis function
with smoothing eta
and robustness rho
parameters.
rbfST.cv(formula, data, eta, rho, n.neigh, func)
formula |
formula that defines the dependent variable as a linear model of independent variables (covariates or the principal coordinates); suppose the dependent variable has name z_{st}, for a rbfST detrended use z_{st} |
data |
SpatialPointsDataFrame: should contain the spatio-temporal dependent variable, independent variables (statics and/or dynamics), spatial coordinates and the time as an integer or numerical variable. |
eta |
the optimal smoothing parameter, we recommend using the parameter found by minimizing the root-mean-square prediction errors using cross-validation. |
rho |
optimal robustness parameter, we recommend using the value obtained by minimizing the root-mean-square prediction errors with cross-validation. eta and rho parameters can be optimized simultaneously, through
the |
n.neigh |
number of nearest observations that should be used for a rbfST prediction, where nearest is defined in terms of the spatio-temporal locations. |
func |
spatio-temporal radial basis function; model type: "GAU", "EXPON", "TRI", "TPS", "CRS", "ST", "IM" and "M", are currently available |
returns the RMSPE value
Melo, C. E. (2012). Analisis geoestadistico espacio tiempo basado en distancias y splines con aplicaciones. PhD. Thesis. Universitat de Barcelona. 276 p. [link]
rbfST
, graph.rbfST
data(croatiadb) coordinates(croatiadb) <- ~x+y rbfST.cv(MTEMP~X1+X2+X3+X4+X5+X6+X7+X8+X9+X10, croatiadb, eta=0.0108, rho=0.00004, n.neigh=25, func="TPS")
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