plot_training_df: Scatterplots of a training data frame

In spatialRF: Easy Spatial Modeling with Random Forest

Scatterplots of a training data frame

Description

Plots the dependent variable against each predictor.

Usage

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.

See Also

Other visualization: plot_evaluation(), plot_importance(), plot_moran(), plot_optimization(), plot_residuals_diagnostics(), plot_response_curves(), plot_response_surface(), plot_training_df_moran(), plot_tuning()

Examples

data(
  plants_df,
  plants_response,
  plants_predictors
)

plot_training_df(
  data = plants_df,
  dependent.variable.name = plants_response,
  predictor.variable.names = plants_predictors[1:4]
)

spatialRF documentation built on Dec. 20, 2025, 1:07 a.m.