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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.
LENSExample
Surface temperature sensitivity from the NCAR Climate model.
NorthAmericanRainfall NorthAmericanRainfall2
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() Tps.cov() wendland.cov() Paciorek.cov()
Exponential family, radial basis functions,cubic spline, compactly supported Wendland family, stationary covariances and non-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() unrollXMatGrid()
Some simple functions for working with gridded data and the grid format (grid.list) used in fields.
stationaryImageCov() 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() divMap() colorBar() setupLegend() addLegend() addColorBarTriangle() plotMatrix()
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() interp.surface.FFT() fillGrid()
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() approximateCovariance2D() augmentPredictionGrid() findGridBox() 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
panelPlotExample()
Tips for a panel plot using base R graphics.
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> )
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()
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() Mines.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