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"
)Data frame with a response variable and a set of predictors. Default: NULL
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
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
Number of columns of the plot. Argument ncol of wrap_plots.
Method for geom_smooth, one of: "lm", "glm", "gam", "loess", or a function, for example mgcv::gam Default: 'loess'
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")
Character string, color of the line produced by ggplot2::geom_smooth(). Default: "gray30"
A wrap_plots object.
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]
)
}