Plots the dependent variable against each predictor.

plot_training_df(
  data = NULL,
  dependent.variable.name = NULL,
  predictor.variable.names = NULL,
  ncol = 4,
  method = "loess",
  point.color = viridis::viridis(100, option = "F"),
  line.color = "gray30"
)

Arguments

data

Data frame with a response variable and a set of predictors. Default: NULL

dependent.variable.name

Character string with the name of the response variable. Must be in the column names of data. If the dependent variable is binary with values 1 and 0, the argument case.weights of ranger is populated by the function case_weights(). Default: NULL

predictor.variable.names

Character vector with the names of the predictive variables. Every element of this vector must be in the column names of data. Optionally, the result of auto_cor() or auto_vif() Default: NULL

ncol

Number of columns of the plot. Argument ncol of wrap_plots.

method

Method for geom_smooth, one of: "lm", "glm", "gam", "loess", or a function, for example mgcv::gam Default: 'loess'

point.color

Colors of the plotted points. Can be a single color name (e.g. "red4"), a character vector with hexadecimal codes (e.g. "#440154FF" "#21908CFF" "#FDE725FF"), or function generating a palette (e.g. viridis::viridis(100)). Default: viridis::viridis(100, option = "F")

line.color

Character string, color of the line produced by ggplot2::geom_smooth(). Default: "gray30"

Value

A wrap_plots object.

Examples

if(interactive()){ #load example data data(plant_richness_df) #scatterplot of the training data plot_training_data( data = plant_richness_df, dependent.variable.name = "richness_species_vascular", predictor.variable.names = colnames(plant_richness_df)[5:21] ) }