Make a t-SNE plot — plot

Makes a t-SNE plot with color coding for classification correctness and shading for predicted outcome

Usage

plot_tsne(
  df,
  Y,
  A,
  caseColors,
  boxColor,
  pointSize = 1,
  textSize = 1,
  symb = 19,
  bottom_buffer = 1.25,
  all_buffer = 1,
  legend_under_plot = T,
  legend_bg_col = "transparent",
  legend_offset = c(0, 0),
  legend_position = "bottomright",
  shade_name = "Positive classification",
  jitter_factor = 1,
  jitter_amount = NULL,
  max_iter = 1000
)

Arguments

df: dataframe of binary features
Y: vector of binary outcome
A: rule set to use for determining which variables will be used for training t-SNE (will use outcome plus all variables that appear in A)
caseColors: vector colors to use for classification correctness. The first element is for correct, second is for incorrect
boxColor: color for shading
pointSize: cex paramater for plot for size of points
textSize: cex paramater for legend for size of text
symb: pch paramater for plot for symbol of points
bottom_buffer: amount by which to shift bottom of graph up for legend
all_buffer: buffer on all sides of plot
legend_under_plot: whether legend is under plot or inside the plot. Overrides legend_coordinates
legend_bg_col: background color of legend
legend_offset: distance to move legend (as a percentage). Negative values move legend to the left and down, positive values move legend to the right and up. If legend_under_plot=F, then this argument is analagous to the inset arguement for plot
legend_position: position of legend
shade_name: label in legend for shaded points
jitter_factor: factor by which to jitter points
jitter_amount: amount by which to jitter points
max_iter: maximum number of iterations to run t-SNE

Value

t-SNE plot