train.spLearner.matrix: Train a spatial prediction and/or interpolation model using...

In Envirometrix/landmap: Automated Spatial Prediction using Ensemble Machine Learning

Train a spatial prediction and/or interpolation model using Ensemble Machine Learning from a regression/classification matrix

Description

Train a spatial prediction and/or interpolation model using Ensemble Machine Learning from a regression/classification matrix

Usage

train.spLearner.matrix(
  observations,
  formulaString,
  covariates,
  SL.library,
  family = stats::gaussian(),
  method = "stack.cv",
  predict.type,
  super.learner,
  subsets = 5,
  lambda = 0.5,
  cov.model = "exponential",
  subsample = 10000,
  parallel = "multicore",
  cell.size,
  id = NULL,
  weights = NULL,
  quantreg = TRUE,
  ...
)

Arguments

observations	Data frame regression matrix,
formulaString	Model formula,
covariates	SpatialPixelsDataFrame object,
SL.library	List of learners,
family	Family e.g. gaussian(),
method	Ensemble stacking method (see makeStackedLearner),
predict.type	Prediction type 'prob' or 'response',
super.learner	Ensemble stacking model usually regr.lm,
subsets	Number of subsets for repeated CV,
lambda	Target variable transformation lambda (0.5 or 1),
cov.model	Covariance model for variogram fitting,
subsample	For large datasets consider random subsetting training data,
parallel	Initiate parellel processing,
cell.size	Block size for spatial Cross-validation,
id	Id column name to control clusters of data,
weights	Optional weights (per row) that learners will use to account for variable data quality,
quantreg	Fit additional ranger model as meta-learner to allow for derivation of prediction intervals,
...	other arguments that can be passed on to mlr::makeStackedLearner,

Value

Object of class spLearner

Author(s)

Tom Hengl

Envirometrix/landmap documentation built on June 10, 2022, 10:12 p.m.