# calculate_residuals_drift: Calculate Residual Drift for old model and new vs. old data In drifter: Concept Drift and Concept Shift Detection for Predictive Models

## Description

Calculate Residual Drift for old model and new vs. old data

## Usage

 ```1 2``` ```calculate_residuals_drift(model_old, data_old, data_new, y_old, y_new, predict_function = predict, bins = 20) ```

## Arguments

 `model_old` model created on historical / 'old' data `data_old` data frame with historical / 'old' data `data_new` data frame with current / 'new' data `y_old` true values of target variable for historical / 'old' data `y_new` true values of target variable for current / 'new' data `predict_function` function that takes two arguments: model and new data and returns numeric vector with predictions, by default it's 'predict' `bins` continuous variables are discretized to 'bins' intervals of equal sizes

## Value

an object of a class 'covariate_drift' (data.frame) with Non-Intersection Distances calculated for residuals

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ``` library("DALEX") model_old <- lm(m2.price ~ ., data = apartments) model_new <- lm(m2.price ~ ., data = apartments_test[1:1000,]) calculate_model_drift(model_old, model_new, apartments_test[1:1000,], apartments_test[1:1000,]\$m2.price) library("ranger") predict_function <- function(m,x,...) predict(m, x, ...)\$predictions model_old <- ranger(m2.price ~ ., data = apartments) calculate_residuals_drift(model_old, apartments_test[1:4000,], apartments_test[4001:8000,], apartments_test\$m2.price[1:4000], apartments_test\$m2.price[4001:8000], predict_function = predict_function) calculate_residuals_drift(model_old, apartments, apartments_test, apartments\$m2.price, apartments_test\$m2.price, predict_function = predict_function) ```

drifter documentation built on May 31, 2019, 5:04 p.m.