Description Usage Arguments Value Author(s)
View source: R/train.spLearner.R
Train a spatial prediction and/or interpolation model using Ensemble Machine Learning from a regression/classification matrix
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | 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,
...
)
|
observations |
Data frame regression matrix, |
formulaString |
Model formula, |
covariates |
SpatialPixelsDataFrame object, |
SL.library |
List of learners, |
family |
Family e.g. |
method |
Ensemble stacking method (see makeStackedLearner), |
predict.type |
Prediction type 'prob' or 'response', |
super.learner |
Ensemble stacking model usually |
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 |
Object of class spLearner
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