plot.mcfs: Plots various MCFS result components

In rmcfs: The MCFS-ID Algorithm for Feature Selection and Interdependency Discovery

Plots various MCFS result components

Description

Plots various aspects of the MCFS-ID result.

Usage

  ## S3 method for class 'mcfs'
plot(x, type = c("features", "ri", "id", "distances", "cv", "cmatrix", "heatmap"), 
        size = NA, 
        ri_permutations = c("max", "all", "sorted", "none"),
        diff_bars = TRUE,
        features_margin = 10,
        cv_measure = c("wacc", "acc", "pearson", "MAE", "RMSE", "SMAPE"),
        heatmap_norm = c('none', 'norm', 'scale'),
        heatmap_fun = c('median', 'mean'),
        color = c('darkred'),
        gg = TRUE,
        cex = 1, ...)

Arguments

x	'mcfs' S3 object - result of the MCFS-ID experiment returned by mcfs function.
type	features plots top features set along with their RI. It is a horizontal barplot that shows important features in red color and unimportant in grey. ri plots top features set with their RIs as well as max RI obtained from permutation experiments. Red color denotes important features. id plots top ID values obtained from the MCFS-ID. distances plots distances (convergence diagnostics of the algorithm) between subsequent feature rankings obtained during the MCFS-ID experiment. cv plots cross validation results based on top features. cmatrix plots the confusion matrix obtained on all s \cdot t trees. heatmap plots heatmap results based on top features. Only numeric features can be presented on the heatmap.
size	number of features to plot.
ri_permutations	if type = "ri" and ri_permutations = "max", then it additionally shows horizontal lines that correspond to max RI values obtained from each single permutation experiment.
diff_bars	if type = "ri" or type = "id" and diff_bars = T, then it shows difference values for RI or ID values.
features_margin	if type = "features", then it determines the size of the left margin of the plot.
cv_measure	if type = "cv", then it determines the type of accuracy shown in the plot: weighted or unweighted accuracy ("wacc" or "acc"). If target attribute is numeric it is possible to review one of the following prediction quality measures: ("pearson", "MAE", "RMSE", "SMAPE")
heatmap_norm	if type = "heatmap", then it defines type of input data normalization 'none' - without any normalization, 'norm' - normalization within range [-1,1], 'scale' - standardization/centering by mean and stdev.
heatmap_fun	if type = "heatmap", then it determines calculation 'mean' or 'median' within the class to be shown as heatmap color intensity.
color	it defines main color of the following type of plots: 'ri', 'id', 'heatmap', 'features' and 'cmatrix'.
gg	if gg = TRUE use ggplot2.
cex	size of fonts.
...	additional plotting parameters.

Examples

  ## Not run: ###dontrunbegin

  # Create input data.
  adata <- artificial.data(rnd_features = 10)
  showme(adata)
  
  # Parametrize and run MCFS-ID procedure.
  result <- mcfs(class~., adata, cutoffPermutations = 0, featureFreq = 10,
                  finalCV = FALSE, finalRuleset = TRUE, threadsNumber = 2)

  # Plot & print out distances between subsequent projections. 
  # These are convergence MCFS-ID statistics.
  plot(result, type = "distances")
  print(result$distances)
  
  # Plot & print out 50 most important features and show max RI values from 
  # permutation experiment.
  plot(result, type = "ri", size = 50)
  print(head(result$RI, 50))
  
  # Plot & print out 50 strongest feature interdependencies.
  plot(result, type = "id", size = 50)
  print(head(result$ID, 50))
  
  # Plot features ordered by RI. Parameter 'size' is the number of 
  # top features in the chart. By default it is set on cutoff_value + 10
  plot(result, type = "features", cex = 1)

  # Here we set 'size' at fixed value 10.
  plot(result, type = "features", size = 10)
  
  # Plot cv classification result obtained on top features.
  # In the middle of x axis red label denotes cutoff_value.
  # plot(result, type = "cv", measure = "wacc", cex = 0.8)
  
  # Plot & print out confusion matrix. This matrix is the result of 
  # all classifications performed by all decision trees on all s*t datasets.
  plot(result, type = "cmatrix")
  
  
## End(Not run)###dontrunend

rmcfs documentation built on Sept. 11, 2024, 8:41 p.m.