.cpu.time.ini: npsp internal and secondary functions

Description

Listed below are supporting functions for the major methods in npsp.

Usage

.cpu.time.ini()
revdim(a, d)
.compute.masked(bin, cov.bin = NULL, tol.mask = npsp.tolerance(2))
.wloss(est, teor, w, loss = c("MSE", "MRSE", "MAE", "MRAE"))
# S3 method for locpol.bin
residuals(object, ...)
.kriging.simple.solve(x, newx, svm)
# S3 method for np.geo
residuals(object, ...)
# S3 method for grid.par
print(x, ...)
# S3 method for grid.par
dim(x)
# S3 method for grid.par
dimnames(x)
# S3 method for grid.par
as.data.frame(x, row.names = dimnames(x), optional = FALSE, ...)
is.data.grid(x)
# S3 method for data.grid
dim(x)
# S3 method for data.grid
dimnames(x)
.rice.rule(x, a = 2, b = 3, ...)
splot.plt(
  horizontal = FALSE,
  legend.shrink = 0.9,
  legend.width = 1,
  legend.mar = ifelse(horizontal, 3.1, 5.1),
  bigplot = NULL,
  smallplot = NULL
)
.rev.colorRampPalette(colors, interpolate = "spline", ...)

Value

.compute.masked returns a list with components:

mask: logical vector bin$binw > tol.mask.
w: x$binw[mask].
sw: sum(w).
hat: (optional) bin$locpol$hat[mask, mask].
cov.bin: (optional) masked (aproximated) covariance matrix of the binned data.

Arguments

a: scale values.
cov.bin: (optional) covariance matrix of the binned data or semivariogram model (svarmod-class) of the (unbinned) data.
object: object used to select a method: local polynomial estimate of the trend (class locpol.bin) or nonparametric geostatistical model (class extending np.geo).
...: further arguments passed to or from other methods.
x: vector/matrix with data locations (each component/row is an observation location).
newx: vector/matrix with the (irregular) locations to predict (each component/row is a prediction location). or an object extending grid.par-class (data.grid).
svm: semivariogram model (of class extending svarmod).
b: exponent values.