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Run Random Forest Classification on Cytokine Data,

Source: R/cyt_rf.R
cyt_rf.Rd

This function trains and evaluates a Random Forest classification model on cytokine data. It includes feature importance visualization, cross- validation for feature selection, and performance metrics such as accuracy, sensitivity, and specificity. Optionally, for binary classification, the function also plots the ROC curve and computes the AUC.

Usage

cyt_rf(
  data,
  group_col,
  ntree = 500,
  mtry = 5,
  train_fraction = 0.7,
  plot_roc = FALSE,
  k_folds = 5,
  step = 0.5,
  run_rfcv = TRUE,
  verbose = FALSE,
  seed = 123,
  cv = FALSE,
  cv_folds = 5,
  scale = c("none", "log2", "log10", "zscore", "custom"),
  custom_fn = NULL
)

Arguments

data

A data frame containing the cytokine measurements. One column should correspond to the grouping variable (the outcome) and the remaining columns should be numeric predictors.

group_col

A string naming the column in data that contains the grouping variable.

ntree

Integer specifying the number of trees to grow. Default is 500.

mtry

Integer specifying the number of variables randomly sampled at each split. Default is 5.

train_fraction

Numeric between 0 and 1 giving the proportion of data used for training. The remainder is used for testing. Default is 0.7.

plot_roc

Logical. If TRUE and the problem is binary, an ROC curve and AUC will be computed and plotted for the test set. Default is FALSE.

k_folds

Integer specifying the number of folds for rfcv when run_rfcv = TRUE. Default is 5.

step

Numeric specifying the fraction of variables removed at each step during rfcv. Default is 0.5.

run_rfcv

Logical indicating whether to run Random Forest cross-validation for feature selection. Default is TRUE.

verbose

Logical indicating whether to print intermediate results. When TRUE, training and test performance metrics, confusion matrices and cross-validation details are printed. Default is FALSE.

seed

Optional integer seed for reproducibility. Default is 123.

cv

Logical indicating whether to perform a separate k-fold classification cross-validation using caret. Default is FALSE.

cv_folds

Integer specifying the number of folds for classification cross-validation when cv = TRUE. Default is 5.

scale

Character string specifying a transformation to apply to the numeric predictor columns prior to model fitting. Options are "none" (no transformation), "log2", "log10", "zscore", or "custom". When "custom" is selected a user defined function must be supplied via custom_fn. Defaults to "none".

custom_fn

A custom transformation function used when scale = "custom". The function should take a numeric vector and return a numeric vector of the same length. Ignored for other values of scale.

Value

An invisible list with components:

model

The fitted randomForest model.

confusion_matrix

Confusion matrix on the test set.

importance_plot

A ggplot2 object of the variable importance (mean decrease in Gini).

importance_data

A data frame of variable importance values.

rfcv_result

The rfcv object returned when run_rfcv = TRUE.

rfcv_plot

A ggplot2 object of cross-validation error versus number of variables, returned when run_rfcv = TRUE.

rfcv_data

A data frame summarizing the rfcv error curve.

roc_plot

A ggplot2 object of the ROC curve for binary classification when plot_roc = TRUE.

cv_results

A caret train object returned when cv = TRUE or NULL otherwise.

Details

The function first coerces the grouping variable to a factor and splits the dataset into training and test subsets according to train_fraction. A Random Forest classifier is fit to the training data using the specified ntree and mtry parameters. The model performance is assessed on both the training and test sets, and results are printed when verbose = TRUE. If plot_roc = TRUE and the grouping variable has exactly two levels, an ROC curve is computed on the test set and a plot is returned. Variable importance is extracted and visualized with a bar plot. Optionally, cross- validation for feature selection (rfcv) is performed and the error curve is plotted. A separate k-fold classification cross- validation using caret::train can be requested via cv = TRUE.

Examples

data.df0 <- ExampleData1
data.df <- data.frame(data.df0[, 1:3], log2(data.df0[, -c(1:3)]))
data.df <- data.df[, -c(2:3)]
data.df <- dplyr::filter(data.df, Group != "ND")

cyt_rf(
  data = data.df, group_col = "Group", k_folds = 5, ntree = 1000,
  mtry = 4, run_rfcv = TRUE, plot_roc = TRUE, verbose = FALSE
)