| rbf.tcv {geospt} | R Documentation |
table of rbf cross validation, leave-one-out
Description
Generates a table with the results of the evaluation of radial basis functions (rbf): gaussian (GAU), exponential (EXPON), trigonometric (TRI), thin plate spline (TPS), completely regularized spline (CRS), spline with tension (ST), inverse multiquadratic (IM), and multiquadratic (M) from the leave-one-out cross validation method.
Usage
rbf.tcv(formula, data, eta, rho, n.neigh, func)
Arguments
formula |
formula that defines the dependent variable as a linear model of independent variables; suppose the dependent variable has name z, for a rbf detrended use z~1, for a rbf with trend, suppose z is linearly dependent on x and y, use the formula z~x+y (linear trend). |
data |
SpatialPointsDataFrame: should contain the dependent variable, independent variables, and coordinates. |
eta |
the optimal smoothing parameter; we recommend using the parameter found by minimizing the root-mean-square prediction errors using cross-validation |
rho |
value of optimal parameter robustness; we recommend using the parameter
found by minimizing the root-mean-square prediction errors using cross-validation.
eta and rho parameters can be optimized simultaneously, through the |
n.neigh |
number of nearest observations that should be used for a rbf prediction, where nearest is defined in terms of the spatial locations |
func |
radial basis function model type, e.g. "GAU", "EXPON", "TRI", "TPS", "CRS", "ST", "MI" and "M", are currently available |
Details
Leave-one-out cross validation (LOOCV) visits a data point, predicts the value at that location by leaving out the observed value, and proceeds with the next data point. The observed value is left out because rbf would otherwise predict the same value
Value
data frame contain the data coordinates, prediction columns, observed values, residuals, the prediction variance, zscore (residual divided by standard error) which left with NA's, and the fold column which is associated to cross-validation count. Prediction columns and residuals are obtained from cross-validation data points
See Also
Examples
data(preci)
coordinates(preci)<-~x+y
rbf.tcv(prec~x+y, preci, eta=0.1460814, rho=0, n.neigh=9, func="TPS")