plotD3_prediction {auditor} R Documentation

## Plot Prediction vs Target, Observed or Variable Values in D3 with r2d3 package.

### Description

Function plotD3_prediction plots predicted values observed or variable values in the model.

### Usage

plotD3_prediction(
object,
...,
variable = "_y_",
points = TRUE,
smooth = FALSE,
abline = FALSE,
point_count = NULL,
single_plot = TRUE,
scale_plot = FALSE,
background = FALSE
)

plotD3Prediction(
object,
...,
variable = NULL,
points = TRUE,
smooth = FALSE,
abline = FALSE,
point_count = NULL,
single_plot = TRUE,
scale_plot = FALSE,
background = FALSE
)


### Arguments

 object An object of class 'auditor_model_residual. ... Other modelAudit or modelResiduals objects to be plotted together. variable Name of variable to order residuals on a plot. If variable="_y_", the data is ordered by a vector of actual response (y parameter passed to the explain function). If variable = "_y_hat_" the data on the plot will be ordered by predicted response. If variable = NULL, unordered observations are presented. points Logical, indicates whenever observations should be added as points. By default it's TRUE. smooth Logical, indicates whenever smoothed lines should be added. By default it's FALSE. abline Logical, indicates whenever function y = x should be added. Works only with variable = NULL which is a default option. point_count Number of points to be plotted per model. Points will be chosen randomly. By default plot all of them. single_plot Logical, indicates whenever single or facets should be plotted. By default it's TRUE. scale_plot Logical, indicates whenever the plot should scale with height. By default it's FALSE. background Logical, available only if single_plot = FALSE. Indicates whenever background plots should be plotted. By default it's FALSE.

### Value

a r2d3 object

plot_prediction

### Examples


dragons <- DALEX::dragons[1:100, ]

# fit a model
model_lm <- lm(life_length ~ ., data = dragons)

lm_audit <- audit(model_lm, data = dragons, y = dragons$life_length) # validate a model with auditor mr_lm <- model_residual(lm_audit) # plot results plotD3_prediction(mr_lm, abline = TRUE) plotD3_prediction(mr_lm, variable = "height", smooth = TRUE) library(randomForest) model_rf <- randomForest(life_length~., data = dragons) rf_audit <- audit(model_rf, data = dragons, y = dragons$life_length)
mr_rf <- model_residual(rf_audit)
plotD3_prediction(mr_lm, mr_rf, variable = "weight", smooth = TRUE)



[Package auditor version 1.3.3 Index]