Usage
"splineUncertain"(object, grid, m = NULL, p = NULL, scale.type = "range", lon.lat = FALSE, miles = TRUE, method = "GCV", GCV = TRUE)
Arguments
object
Input data. An object of UncertainPoints class.
grid
Input grid type of dataframe.
m
A polynomial function of degree (m-1) will be included in
the model as the drift (or spatial trend) component. Default is
the value such that 2m-d is greater than zero where d is the dimension of x.
p
Polynomial power for Wendland radial basis functions. Default is
2m-d where d is the dimension of x.
scale.type
The independent variables and knots are scaled to the specified
scale.type. By default the scale type is "range", whereby the locations are
transformed to the interval (0,1) by forming (x-min(x))/range(x) for each x.
Scale type of "user" allows specification of an x.center and x.scale by the user.
The default for "user" is mean 0 and standard deviation 1. Scale type of "unscaled"
does not scale the data.
lon.lat
If TRUE locations are interpreted as lognitude and latitude and great circle distance is used to find distances among locations.
miles
If TRUE great circle distances are in miles if FALSE distances are in kilometers.
method
Determines what "smoothing" parameter should be used. The default is to estimate standard GCV Other choices are: GCV.model, GCV.one, RMSE, pure error and REML. The differences are explained in the Krig help file.
GCV
If TRUE the decompositions are done to efficiently evaluate the estimate, GCV function and likelihood at multiple values of lambda.