Function reference • fields

All functions
`BD`	Data frame of the effect of buffer compositions on DNA strand displacement amplification. A 4-d regression data set with with replication. This is a useful test data set for exercising function fitting methods.
`COmonthlyMet` `CO.elev` `CO.id` `CO.loc` `CO.names` `CO.ppt` `CO.ppt.MAM` `CO.tmax` `CO.tmax.MAM` `CO.tmin` `CO.tmin.MAM` `CO.years` `CO.ppt.MAM.climate` `CO.tmax.MAM.climate` `CO.tmean.MAM.climate` `CO.tmin.MAM.climate` `CO.elevGrid` `CO.Grid`	Monthly surface meterology for Colorado 1895-1997
`CO2`	Simulated global CO2 observations
`ExponentialUpper()`	Evaluate covariance over upper triangle of distance matrix
`Exponential()` `Matern()` `Matern.cor.to.range()` `RadialBasis()`	Covariance functions
`Krig.Amatrix()`	Smoother (or "hat") matrix relating predicted values to the dependent (Y) values.
`Krig()` `fitted(<Krig>)` `coef(<Krig>)` `resid.Krig()`	Kriging surface estimate
`Krig.engine.default()` `Krig.engine.fixed()` `Krig.coef()` `Krig.make.u()` `Krig.check.xY()` `Krig.transform.xY()` `Krig.make.W()` `Krig.make.Wi()`	Basic linear algebra utilities and other computations supporting the Krig function.
`Krig.null.function()`	Default function to create fixed matrix part of spatial process model.
`Krig.replicates()`	Collapse repeated spatial locations into unique locations
`KrigFindLambda()` `gcv.sreg()`	Finds profile likelihood and GCV estimates of smoothing parameters for splines and Kriging.
`NorthAmericanRainfall`	Observed North American summer precipitation from the historical climate network.
`QTps()` `QSreg()`	Robust and Quantile smoothing using a thin-plate spline
`RCMexample`	3-hour precipitation fields from a regional climate model
`RMprecip` `RMelevation` `PRISMelevation`	Monthly total precipitation (mm) for August 1997 in the Rocky Mountain Region and some gridded 4km elevation data sets (m).
`Tps()` `fastTps()`	Thin plate spline regression
`US()`	Plot of the US with state boundaries
`US.dat`	Outline of coterminous US and states.
`Wendland()` `Wendland2.2()` `Wendland.beta()` `wendland.eval()` `fields.pochup()` `fields.pochdown()` `fields.D()`	Wendland family of covariance functions and supporting numerical functions
`WorldBankCO2`	Carbon emissions and demographic covariables by country for 1999.
`add.image()`	Adds an image to an existing plot.
`arrow.plot()`	Adds arrows to a plot
`as.image()`	Creates image from irregular x,y,z
`as.surface()`	Creates an "surface" object from grid values.
`bplot()`	boxplot
`bplot.xy()`	Boxplots for conditional distribution
`colorbar.plot()`	Adds color scale strips to an existing plot.
`compactToMat()`	Convert Matrix from Compact Vector to Standard Form
`cover.design()`	Computes Space-Filling "Coverage" designs using Swapping Algorithm
`drape.plot()` `drape.color()`	Perspective plot draped with colors in the facets.
`envelopePlot()`	Add a shaded the region between two functions to an existing plot
`Exp.cov()` `Exp.simple.cov()` `Rad.cov()` `cubic.cov()` `Rad.simple.cov()` `stationary.cov()` `stationary.taper.cov()` `wendland.cov()`	Exponential family, radial basis functions,cubic spline, compactly supported Wendland family and stationary covariances.
`fields.duplicated.matrix()` `fields.mkpoly()` `fields.derivative.poly()` `fields.evlpoly()` `fields.evlpoly2()`	Fields supporting functions
`fields-package` `fields`	fields - tools for spatial data
`mKrig.grid`	Using MKrig for predicting on a grid.
`fields.style()` `fields.color.picker()`	fields - graphics hints
`test.for.zero()`	Testing fields functions
`flame`	Response surface experiment ionizing a reagent
`glacier`	Franke's Glacier Elevation Data
`makeMultiIndex()` `parse.grid.list()` `fields.x.to.grid()` `fields.convert.grid()` `discretize.image()` `make.surface.grid()` `unrollZGrid()`	Some simple functions for working with gridded data and the grid format (grid.list) used in fields.
`stationary.image.cov()` `Exp.image.cov()` `Rad.image.cov()` `matern.image.cov()` `wendland.image.cov()`	Exponential, Matern and general covariance functions for 2-d gridded locations.
`image(<plot>)`	Draws an image plot with a legend strip for the color scale based on either a regular grid or a grid of quadrilaterals.
`image(<smooth>)` `setup.image.smooth()`	Kernel smoother for irregular 2-d data
`crop.image()` `which.max.matrix()` `which.max.image()` `get.rectangle()` `average.image()` `half.image()` `in.poly()` `in.poly.grid()`	Some simple functions for subsetting images
`imagePlot()` `colorBar()` `setupLegend()` `addLegend()` `addColorBarTriangle()`	Draws an image plot with a legend strip for the color scale based on either a regular grid or a grid of quadrilaterals.
`interp.surface()` `interp.surface.grid()`	Fast bilinear interpolator from a grid.
`lennon`	Gray image of John Lennon.
`mKrig()` `predict(<mKrig>)` `summary(<mKrig>)` `print(<mKrig>)` `print(<mKrigSummary>)` `mKrig.coef()` `mKrig.trace()` `mKrigCheckXY()`	"micro Krig" Spatial process estimate of a curve or surface, "kriging" with a known covariance function.
`mKrigMLEGrid()` `mKrigMLEJoint()` `profileCI()` `mKrigJointTemp.fn()`	Maximizes likelihood for the process marginal variance (sigma) and nugget standard deviation (tau) parameters (e.g. lambda) over a many covariance models or covariance parameter values.
`minitri`	Mini triathlon results
`offGridWeights()` `offGridWeights1D()` `offGridWeights2D()` `addMarginsGridList()` `mKrigFastPredictSetup()`	Utilities for fast spatial prediction.
`ChicagoO3` `ozone`	Data set of ozone measurements at 20 Chicago monitoring stations.
`ozone2`	Daily 8-hour ozone averages for sites in the Midwest
`plot(<Krig>)` `plot(<sreg>)`	Diagnostic and summary plots of a Kriging, spatialProcess or spline object.
`plot(<surface>)`	Plots a surface
`poly.image()` `poly.image.regrid()`	Image plot for cells that are irregular quadrilaterals.
`predict(<Krig>)` `predictDerivative.Krig()` `predict(<Tps>)` `predict(<fastTps>)`	Evaluation of Krig spatial process estimate.
`predictSE()`	Standard errors of predictions for Krig spatial process estimate
`predictSurface(<default>)` `predictSurface(<fastTps>)` `predictSurface(<Krig>)` `predictSurface(<mKrig>)` `mKrigFastPredict()` `predictSurfaceSE(<default>)` `predict(<surface>)`	Evaluates a fitted function or the prediction error as a surface that is suitable for plotting with the image, persp, or contour functions.
`print(<Krig>)`	Print kriging fit results.
`pushpin()`	Adds a "push pin" to an existing 3-d plot
`qsreg()`	Quantile or Robust spline regression
`quilt.plot()` `bubblePlot()`	Useful plots for visualizing irregular spatial data.
`rat.diet`	Experiment studying an appetite supressant in rats.
`rdist()` `fields.rdist.near()` `rdist.vec()`	Euclidean distance matrix or vector
`rdist.earth()` `RdistEarth()` `rdist.earth.vec()`	Great circle distance matrix or vector
`addToDiagC` `ExponentialUpperC` `compactToMatC` `multebC` `multwendlandg` `mltdrb` `RdistC` `distMatHaversin` `distMatHaversin2`	Information objects that register C and FORTRAN functions.
`ribbon.plot()`	Adds to an existing plot, a ribbon of color, based on values from a color scale, along a sequence of line segments.
`set.panel()`	Specify a panel of plots
`simSpatialData()` `sim.spatialProcess()` `sim.Krig()` `simLocal.spatialProcess()` `checkPredictGrid()` `makePredictionGridList()` `makeSimulationGrid()`	Unconditional and conditional simulation of a spatial process
`sim.rf()` `circulantEmbedding()` `circulantEmbeddingSetup()`	Efficiently Simulates a Stationary 1 and 2D Gaussian random fields
`smooth.2d()`	Kernel smoother for irregular 2-d data
`spind2full()` `spam2full()` `spind2spam()` `spam2spind()`	Conversion of formats for sparse matrices
`spatialProcess()` `summary(<spatialProcess>)` `print(<spatialProcess>)` `print(<spatialProcessSummary>)` `plot(<spatialProcess>)` `spatialProcessSetDefaults()` `confidenceIntervalMLE()` `profileMLE()`	Estimates a spatial process model.
`splint()`	Cubic spline interpolation
`sreg()` `predict(<sreg>)`	Cubic smoothing spline regression
`stats()`	Calculate summary statistics
`stats.bin()`	Bins data and finds some summary statistics.
`summary(<Krig>)`	Summary for Krig or spatialProcess estimated models.
`summary(<ncdf>)`	Summarizes a netCDF file handle
`supportsArg()`	Tests if function supports a given argument
`surface(<Krig>)` `surface(<mKrig>)`	Plots a surface and contours
`tim.colors()` `larry.colors()` `snow.colors()` `two.colors()` `designer.colors()` `color.scale()` `fieldsPlotColors()`	Some useful color tables for images and tools to handle them.
`transformx()`	Linear transformation
`vgram()` `crossCoVGram()` `boxplotVGram()` `plot(<vgram>)` `getVGMean()`	Traditional or robust variogram methods for spatial data
`vgram.matrix()` `plot(<vgram.matrix>)`	Computes a variogram from an image
`world()` `world.land()` `world.color()` `in.land.grid()`	Plot of the world
`xline()`	Draw a vertical line
`yline()`	Draw horizontal lines

Reference

All functions