Man pages for SpatPCA
Regularized Principal Component Analysis for Spatial Data

checkInputDataInternal function: Validate input data for a spatpca object
checkNewLocationsForSpatpcaObjectInternal function: Validate new locations for a spatpca...
detrendInternal function: Detrend Y by column-wise centering
eigenFunctionInterpolated Eigen-function
fetchUpperBoundNumberEigenfunctionsInternal function: Fetch the upper bound of the number of...
plot.spatpcaDisplay the cross-validation results
predictSpatial predictions on new locations
predictEigenfunctionSpatial dominant patterns on new locations
scaleLocationInternal function: Scale one-dimension locations
setCoresInternal function: Set the number of cores for parallel...
setGammaInternal function: Set tuning parameter - gaama
setL2Internal function: Set tuning parameter - l2
setNumberEigenfunctionsInternal function: Set the number of eigenfunctions for a...
setTau1Internal function: Set tuning parameter - tau1
setTau2Internal function: Set tuning parameter - tau2
spatialPredictionInternal function: Spatial prediction
spatpcaRegularized PCA for spatial data
spatpcaCVInternal function: M-fold Cross-validation
spatpcaCVWithSelectingKInternal function: M-fold CV of SpatPCA with selecting K
SpatPCA-packageRegularized Principal Component Analysis for Spatial Data
thinPlateSplineMatrixThin-plane spline matrix
SpatPCA documentation built on Jan. 31, 2021, 5:05 p.m.