fit_rf: Calibration sampling, and random forest model tuning and...

In philipp-baumann/simplerspec: Soil and plant spectroscopic model building and prediction

Calibration sampling, and random forest model tuning and evaluation

Description

Perform calibration sampling and use selected calibration set for model tuning

Usage

fit_rf(
  spec_chem,
  response,
  variable = NULL,
  evaluation_method = "test_set",
  validation = NULL,
  split_method = "ken_stone",
  ratio_val,
  ken_sto_pc = 2,
  pc = NULL,
  invert = TRUE,
  tuning_method = "resampling",
  resampling_seed = 123,
  cv = NULL,
  ntree_max = 500,
  print = TRUE,
  env = parent.frame()
)

Arguments

spec_chem	Tibble that contains spectra, metadata and chemical reference as list-columns. The tibble to be supplied to spec_chem can be generated by the ⁠join_chem_spc() function⁠
response	Response variable as symbol or name (without quotes, no character string). The provided response symbol needs to be a column name in the spec_chem tibble.
variable	Depreciated and replaced by response
evaluation_method	Character string stating evaluation method. Either "test_set" (default) or "resampling". "test_set" will split the data into a calibration (training) and validation (test) set, and evaluate the final model by predicting on the validation set. If "resampling", the finally selected model will be evaluated based on the cross-validation hold-out predictions.
validation	Depreciated and replaced by evaluation_method. Default is TRUE.
split_method	Method how to to split the data into a independent test set. Default is "ken_sto", which will select samples for calibration based on Kennard-Stone sampling algorithm of preprocessed spectra. The proportion of validation to the total number of samples can be specified in the argument ratio_val. split_method = "random" will create a single random split.
ratio_val	Ratio of validation (test) samples to total number of samples (calibration (training) and validation (test)).
ken_sto_pc	Number of component used for calculating mahalanobsis distance on PCA scores for computing Kennard-Stone algorithm. Default is ken_sto_pc = 2, which will use the first two PCA components.
pc	Depreciated; renamed argument is ken_sto_pc.
invert	Logical
tuning_method	Character specifying tuning method. Tuning method affects how caret selects a final tuning value set from a list of candidate values. Possible values are "resampling", which will use a specified resampling method such as repeated k-fold cross-validation (see argument resampling_method) and the generated performance profile based on the hold-out predictions to decide on the final tuning values that lead to optimal model performance. The value "none" will force caret to compute a final model for a predefined canditate PLS tuning parameter number of PLS components. In this case, the value supplied by ncomp_fixed' is used to set model complexity at a fixed number of components.
resampling_seed	Random seed (integer) that will be used for generating resampling indices, which will be supplied to caret::trainControl. This makes sure that modeling results are constant when re-fitting. Default is resampling_seed = 123.
cv	Depreciated. Use resampling_method instead.
ntree_max	Maximum random forest trees by caret::train. Caret will aggregate a performance profile using resampling for an integer sequence from 1 to ntree_max trees.
print	Logical expression whether model evaluation graphs shall be printed
env	Environment where function is evaluated. Default is parent.frame.

philipp-baumann/simplerspec documentation built on Oct. 3, 2023, 12:13 p.m.