hs_RF_pred: Train random forest model on hyperspec object

In CMET-UGent/MicroRaman: Phenotyping of microbial Raman spectroscopy data

Description

Train random forest model on hyperspec object

Usage

hs_RF_pred(hs.x, model, ...)

Arguments

hs.x	Hyperspec object on which to perform predictions
model	Model to use for predictions. Output from hs_RF.
...	additional parameters passed on to stats::predict

Examples

# Short example
data("hs_example")

# Preprocess
hs_example <- hs_preprocess(hs_example)

# Mock-up metadata
mock_meta <- data.frame(Spectrum_ID = rownames(hs_example@data$spc),
group = factor(c(rep(1,30),rep(2,34))))

# Calculate metrics
hs.RF <- hs_RF(hs.x = hs_example, metadata = mock_meta, spectrumID_col= "Spectrum_ID",
 target_var = "group")

 # Trained model
 print(hs.RF[[1]])

 # Confusion matrix
 print(hs.RF[[2]])

 # Variable importance metric
 caret::varImp(hs.RF[[1]])

 # Perform predictions
 hs_RF_pred(hs.x = hs_example, model = hs.RF[[1]])

CMET-UGent/MicroRaman documentation built on July 25, 2020, 6:20 p.m.