| .cpu.time.ini | R Documentation | 
Listed below are supporting functions for the major methods in npsp.
.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 class 'locpol.bin'
residuals(object, ...)
.kriging.simple.solve(x, newx, svm)
## S3 method for class 'np.geo'
residuals(object, ...)
## S3 method for class 'grid.par'
print(x, ...)
## S3 method for class 'grid.par'
dim(x)
## S3 method for class 'grid.par'
dimnames(x)
## S3 method for class 'grid.par'
as.data.frame(x, row.names = dimnames(x), optional = FALSE, ...)
is.data.grid(x)
## S3 method for class 'data.grid'
dim(x)
## S3 method for class '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", ...)
| a | scale values. | 
| cov.bin | (optional) covariance matrix of the binned data or semivariogram model
( | 
| object | object used to select a method: 
local polynomial estimate of the trend (class  | 
| ... | 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  | 
| svm | semivariogram model (of class extending  | 
| b | exponent values. | 
.compute.masked returns a list with components:
| mask | logical vector  | 
| w | 
 | 
| sw | 
 | 
| hat | (optional)  | 
| cov.bin | (optional) masked (aproximated) covariance matrix of the binned data. | 
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