| fields internal {fields} | R Documentation |
Listed below are supporting fucntions for the major methods in fields.
COR (dat)
D4transform.image (x, inv = F, transpose = F, cut.min = 8)
FIELDS.version
Krig.coef (out, u = NULL, lambda = NULL, yM = NULL)
Krig.df.to.lambda (df, D, guess = 1, tol = 1e-05)
Krig.discretize (x, m = 64, n = 64, xg = NULL, yg = NULL, grid
= NULL, expand = c(1, 1))
Krig.fdf (llam, info)
Krig.fgcv (lam, obj)
Krig.fgcv.model (lam, obj)
Krig.fgcv.one (lam, obj)
Krig.find.gcvmin (info, lambda.grid, gcv, gcv.fun, tol, verbose = F)
Krig.fs2hat (lam, obj)
Krig.ftrace (lam, D)
Krig.newy (out, newy, verbose = F)
Krig.replicates (out, verbose = F)
Krig.updateY (out, Y, verbose = F)
Krig.ynew (out, ynew, verbose = F)
US (xlim = c(-124.7, -67.1), ylim = c(25.2, 49.4), add = F, ...)
W.image.cov (ind1, ind2, Y, cov.obj = NULL, setup = F, m = 64,
n = 64, ...)
WD4 (x)
WD42d (x)
WD42di (x)
WD4i (x)
WQS (x)
WQS.T (x)
WQS.basis (N, cut.n = 8)
WQS2d (x, transpose = F)
WQS2di (x, transpose = F)
WQSdi (x, transpose = F)
WQSi (x)
WQSi.T (x)
Wtransform.D (nx, ny, weights = c(1), cut.min = 8, details = F)
bisection.search (x1, x2, f, tol = 1e-07, niter = 25, f.extra =
NA, upcross.level = 0)
bplot.obj (data, pos = NA, width = NULL, labels = NULL, srt =
,add = F, space = 0.25, sort.names = F, xlab = "", ylab = "",
label.cex = 1, xaxt = "n", outlier = T, horizontal = F, ...)
cat.matrix (mat, digits = 8)
cat.to.list (x, a)
ceiling2 (m)
conjugate.gradient (b, multAx, start = NULL, tol = 1e-05, kmax =
25, verbose = T, ...)
cover.design.S (R, nd, nruns = 1, nn = T, num.nn = 100, fixed =
,scale.type = "range", R.center, R.scale, P = -20, Q = 20,
start = NULL, DIST = NULL, return.grid = T, return.transform = T)
describe (x)
describe.bplot (temp, style = "tukey", outlier = T)
draw.bplot (temp, width, xpos, outlier = T, style = "tukey")
draw.bplot.obj (obj, width, xpos, outlier = T, horizontal = F)
dup (dat)
dup.matrix (mat)
dyn.load.fields (...)
exp.cov.F (x1, x2, theta = rep(1, ncol(x1)), p = 1, C = NA)
exp.cov.S (x1, x2, theta = 1)
exp.earth.cov (x1, x2, theta = 1)
fast.1way (lev, y, w = rep(1, length(y)))
find.upcross (fun, fun.info, upcross.level = 0, guess = 1, tol =
1e-05)
gauss.cov (...)
gcv.Krig (out, lambda.grid = NA, cost = 1, nstep.cv = 80, rmse =
NA,verbose = F, tol = 1e-05, offset = 0, y = NULL, lambda = NA)
gcv.sreg (out, lambda.grid = NA, cost = 1, nstep.cv = 20, rmse =
NA, offset = 0, trmin = NA, trmax = NA, verbose = T, tol = 1e-05,
find.min = T, method = "GCV")
golden.section.search (ax, bx, cx, f, niter = 25, f.extra = NA,
tol = 1e-05)
grey.level (n)
image.plot.info (...)
image.plot.plt ( add=F, legend.shrink = 0.9, legend.width = 0.04,
horizontal = F, offset = 2 * legend.width, bigplot = NULL, smallplot = NULL)
image.smooth.setup (nrow = 64, ncol = 64, dx = 1, dy = 1,
kernel.function = function(x) { exp(-abs(x))},
theta = 1, Mwidth = nrow, Nwidth = ncol, ...)
in.poly (xd, xp, convex.hull = F)
krig.image.parameters (out)
lonlat2xy (lnlt, miles = F)
make.Amatrix.Krig (out, x0 = out$x, lambda)
make.surface.grid (grid.list, X, nx = 30, ny = 30,
info.list = F, FUN = median)
make.tmatrix (x, m = 2)
make.tmatrix.F (x, m = 2)
make.tmatrix.S (x, m = 2)
matern (x = seq(0, 4 * range, len = 100), scale = 1, range = 1,
smoothness = 0.5)
minimax.crit (obj, des = T, R)
nkden (data, bandwidth, n.points, grid)
periodic.cov.1d (x1, x2, a, b)
periodic.cov.cyl (x1, x2, a = 0, b = 365, theta = 1)
periodic.plane.3d (x1, x2, a = 0, b = 365, theta = 1)
plot.coef (x, cut.min = 8, graphics.reset = T, common.range = F)
plot.krig.image (out, main = NA, digits = 4, which = rep(T, 4),
graphics.reset = T, ...)
plot.qsreg (out, pch = "*", main = NA)
plot.sim.krig.image (obj)
plot.spatial.design (out)
predict.interp.surface (obj, loc)
predict.krig.image (obj, x, ...)
predict.qsreg (out, x, derivative = 0, model = 1)
predict.sreg (out, x, derivative = 0, model = 1)
predict.tps (out, x, y, lambda, df, omega, u = NA,
derivative = 0, model = NA)
print.krig.image (obj)
print.qsreg (x, ...)
print.spatial.design (x)
print.sreg (x, ...)
print.summary.Krig (x, ...)
print.summary.krig.image (x, ...)
print.summary.spatial.design (x, digits = 4)
print.summary.sreg (x, ...)
qr.q2ty (qr, y)
qr.yq2 (qr, y)
qsreg.trace (x, y, lam, maxit = 50, maxit.cv = 10, tol = 1e-04,
offset = 0, sc = sqrt(var(y)) * 1e-07, alpha = 0.5,
wt = rep(1, length(x)), cost = 1)
rad.cov (x1, x2, p = 1, with.log = T, with.constant = T, C =NULL)
rad.cov.F (x1, x2, p = 1, with.log = T, with.constant = T, C = NULL)
rad.cov.S (x1, x2, p = 1, with.log = T, with.constant = T)
rad.covC (x1, x2, C, p = 1, with.log = T, with.constant = T)
rad.image.cov (ind1, ind2, Y, cov.obj = NULL, setup = F, grid,...)
radbas.constant (m, d)
rdist.F (x1, x2)
rdist.S (x1, x2, lon.lat = F)
replace.args.function (fun, ...)
setup.W.image.cov (m, n, weights = 1, D = NULL, cut.min = 8,
max.m = m,max.n = n, x = NULL, grid = NULL, normalize = T, ...)
setup.Wtransform.cov (m, n, weights = 1, cut.min = 8, max.m = m,
max.n = n,x = NULL, grid = NULL, normalize = T, ...)
sim.krig.image (out, nreps = 10)
sim.rf.W (cov.obj)
sim.rf.default (obj)
sreg.df.to.lambda (df, x, wt, guess = 1, tol = 1e-05)
sreg.fdf (h, info)
sreg.fgcv (lam, obj)
sreg.fgcv.model (lam, obj)
sreg.fgcv.one (lam, obj)
sreg.fit (lam, info)
sreg.fs2hat (lam, obj)
sreg.trace (h, info)
stats.bplot (x, by, style = "tukey", outlier = T)
stats.sim.krig.image (obj)
summary.Krig (x, digits = 4)
summary.gcv.Krig (out, lambda, cost = 1, verbose = F, offset =
0, y = NULL)
summary.gcv.sreg (out, lambda, cost = 1, nstep.cv = 20, offset =
0, verbose = T)
summary.krig.image (x, digits = 4, ...)
summary.qsreg (x, ...)
summary.spatial.design (x, digits = 4, ...)
summary.sreg (x, digits = 4, ...)
surface (x, grid.list = NA, extrap = F, xlab = NULL, ylab =
,zlab = NULL, main = NULL, levels = NULL, zlim = NULL, ...)
surface.default (...)
surface.krig.image (obj, grid.list = NA, extrap = T,
graphics.reset = F, xlab = NULL, ylab = NULL, main = NULL,
zlab = NULL,zlim = NULL,levels = NULL, ptype = "I", ...)
surface.surface (obj, lab = NA, type = "b", zlab, xlab, ylab,
graphics.reset = F, ...)
test.data
test.data2
unique.matrix (x)
unscale (x, x.center, x.scale)
variogram.matrix (dat, R = 5)
world.dat