Nothing
okfd <-
function(new.coords, coords, data,
smooth.type=NULL, nbasis=max(50,dim(data)[1]), argvals=seq(0, 1, len = dim(data)[1]), lambda=0,
cov.model=NULL, fix.nugget=FALSE, nugget=0, fix.kappa=TRUE, kappa=0.5, max.dist.variogram=NULL)
# argnames=c("argument", "sites", "values"),
{
# Loading required libraries
## require(fda)
## require(geoR)
# Argument validation
smooth.type <- match.arg(smooth.type, c("bsplines","fourier"))
# The argument cov.model is validated if it is NOT null
if(!is.null(cov.model)){
cov.model <- match.arg(cov.model, c("spherical","exponential","gaussian","matern"))
}
if(is.null(new.coords)) stop("new.coords is not an optional parameter")
if(ncol(new.coords)!=2) stop("new.coords must be an n x 2 matrix")
# nbasis, argvals and lambda are validated in runtime on their corresponding using function
# max.dist, fix.nugget and nugget does not seem to be validated
# Argument type conversion
new.coords <- as.matrix(new.coords)
coords <- as.matrix(coords)
# Number of sites
s <- dim(data)[2]
fdmodel <- .simple.fdmodel(new.coords, coords, data, smooth.type, nbasis, argvals, lambda, cov.model, fix.nugget, nugget, fix.kappa, kappa, max.dist.variogram)
##################################################################
# Doing prediction
##################################################################
prediction <- .okfd.predict(argvals, fdmodel$fdobjects$datafd, coords, new.coords, fdmodel$trace.vari.objects$best, fdmodel$emp.trace.vari$Eu.d)
##################################################################
# Return
##################################################################
return.list <- list(
coords=coords,
data=data,
argvals=argvals,
nbasis=nbasis,
lambda=lambda,
new.coords=new.coords,
emp.trace.vari=fdmodel$emp.trace.vari,
trace.vari=fdmodel$trace.vari.objects$best,
# In parameter $u there are the distances
# Eu.d=Eu.d,
new.Eu.d=prediction$new.Eu.d,
functional.kriging.weights=prediction$functional.kriging.weights,
krig.new.data=prediction$pred,
pred.var=prediction$var,
trace.vari.array=fdmodel$trace.vari.objects$fitted,
datafd=fdmodel$fdobjects$datafd
)
#return.list$argnames <- argnames
class(return.list) <- "geofd"
return(return.list)
}
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