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

Description Usage Arguments Value Author(s)

View source: R/train.spLearner.R

Description

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

Usage

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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 for geoR (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


landmap documentation built on Oct. 14, 2021, 5:24 p.m.