SpatialML (version 0.1.5)

rf.mtry.optim: Optimal mtry

Description

This function calculates the optimal mtry for a given Random Forest (RF) model in a specified range of values. The optimal mtry value can then be used in the grf model.

Usage

rf.mtry.optim(formula, dataset, min.mtry=NULL, max.mtry=NULL, mtry.step,
       cv.method="repeatedcv", cv.folds=10, ...)

Value

A list is returned of class train as in the function train in the caret package.

Arguments

formula

the model to be fitted using the function train of the R package caret.

dataset

a numeric data frame of at least two suitable variables (one dependent and one independent)

min.mtry

the minimum mtry value for its optimisation (function expand.grid)

max.mtry

the maximum mtry value for its optimisation (function expand.grid)

mtry.step

the step in the sequence of mtry values for its optimisation (function expand.grid)

cv.method

the resampling method in the function trainControl of the R package caret. Default option is "repeatedcv" and alternative option is "cv".

cv.folds

the number of folds (argument "number" in the function trainControl). Default value is 10)

...

additional arguments affecting the function trainControl)

Author

Stamatis Kalogirou <[e-mail]>, Stefanos Georganos <[e-mail]>

Details

Based on the train function of the caret package, this function sets up a grid of tuning parameters for a number of random forest routines, fits each model and calculates a resampling based performance measure to choose the best mtry value.

References

Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28(5), 1 - 26. doi: <http://dx.doi.org/10.18637/jss.v028.i05>

Georganos, S. and Kalogirou, S. (2022) A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests. ISPRS, International Journal of Geo-Information, 2022, 11, 471. <https://www.mdpi.com/2220-9964/11/9/471>

Examples

  # \donttest{
      data(Income)
      Coords <- Income[ ,1:2]
      results <- rf.mtry.optim(Income01 ~ UnemrT01 + PrSect01, Income)
      # }