This vignette demonstrates how to use the *auditor* package for auditing residuals of models.
The auditor provides methods for model verification and validation by error analysis.

Many models, such as random forests and neutral networks are nowadays treated as black boxes. Therefore, there is a lack of theory that describes the behavior of errors in those models. Most methods provided in auditor package are model-agnostic, so can be used regardless of knowledge about errors.

Some of the graphical error analysis methods also have corresponding scores, which allow comparison of two models.

To illustrate applications of *auditor* to regression problems we will use an artificial dataset apartments available in the *DALEX* package. Our goal is to predict the price per square meter of an apartment based on selected features such as construction year, surface, floor, number of rooms, district. It should be noted that four of these variables are continuous while the fifth one is a categorical one. Prices are given in Euro.

```
library(DALEX)
data("apartments")
head(apartments)
```

```
## m2.price construction.year surface floor no.rooms district
## 1 5897 1953 25 3 1 Srodmiescie
## 2 1818 1992 143 9 5 Bielany
## 3 3643 1937 56 1 2 Praga
## 4 3517 1995 93 7 3 Ochota
## 5 3013 1992 144 6 5 Mokotow
## 6 5795 1926 61 6 2 Srodmiescie
```

```
lm_model <- lm(m2.price ~ construction.year + surface + floor + no.rooms + district, data = apartments)
```

```
library("randomForest")
set.seed(59)
rf_model <- randomForest(m2.price ~ construction.year + surface + floor + no.rooms + district, data = apartments)
```

The beginning of each analysis is creation of a `modelAudit`

object. Itâ€™s an object that can be used to audit a model.

```
library("auditor")
lm_audit <- audit(lm_model, label = "lm", data = apartmentsTest, y = apartmentsTest$m2.price)
rf_audit <- audit(rf_model, label = "rf", data = apartmentsTest, y = apartmentsTest$m2.price)
```

In this section we give short overview of a visual validation of model errors and show the propositions for the validation scores. Auditor helps to find answers for questions that may be crucial for further analyses.

Does the model fit data? Is it not missing the information?

Which model has better performance?

How similar models are?

In further sections we will overview auditor functions for analysis of model residuals. They are discussed in alphabetical order.

The auditor provides 2 pipelines of model audit.

**model %>% audit() %>% modelResiduals() %>% plot(type=…)**This pipeline is recommended. Function`modelResiduals()`

creates a`modelResiduals`

object. Such object may be passed to a`plot()`

function with defined type of plot. This approach requires one additional function within the pipeline. However, once created`modelResiduals`

contains all nessesary calculations that all plots require. Therefore, generating multiple plots is fast.

Alternative:**model %>% audit() %>% modelResiduals() %>% plotType()****model %>% audit() %>% plot(type=…)**This pipeline is shorter than previous one. Calculations are carried out every time a function is called. However, it is faster to use.

Alternative**model %>% audit() %>% plotType()**

Help of functions `plot[Type]()`

contains additional information about plots.

In this vignette we use first pipeline. However, alternative evaluations are showed as comments.
First, we need to create a `modelResiduals`

objects.

```
lm_mr <- modelResiduals(lm_audit)
rf_mr <- modelResiduals(rf_audit)
head(lm_mr)
```

```
## label res val variable y fitted.values std.res index
## 1 lm -176.0094 1 NA 4644 4820.009 -0.6218021 1001
## 2 lm -210.6776 2 NA 3082 3292.678 -0.7442769 1002
## 3 lm -219.9097 3 NA 2498 2717.910 -0.7768920 1003
## 4 lm -187.7511 4 NA 2735 2922.751 -0.6632827 1004
## 5 lm -193.0858 5 NA 2781 2974.086 -0.6821291 1005
## 6 lm 408.9568 6 NA 2936 2527.043 1.4447532 1006
```

Some plots may require specified variable or fitted values for `modelResidual`

object.

```
lm_mr_district <- modelResiduals(lm_audit, variable = "district")
rf_mr_district <- modelResiduals(rf_audit, variable = "district")
lm_mr_fitted <- modelResiduals(lm_audit, variable = "Fitted values")
rf_mr_fitted <- modelResiduals(rf_audit, variable = "Fitted values")
head(lm_mr_district)
```

```
## label res val variable y fitted.values std.res index
## 1 lm -193.0858 Bemowo district 2781 2974.086 -0.6821291 1005
## 2 lm 408.9568 Bemowo district 2936 2527.043 1.4447532 1006
## 3 lm -190.4866 Bemowo district 3010 3200.487 -0.6729468 1007
## 4 lm -187.2122 Bemowo district 2593 2780.212 -0.6613788 1009
## 5 lm -197.6170 Bemowo district 2792 2989.617 -0.6981369 1016
## 6 lm -205.4415 Bemowo district 2550 2755.441 -0.7257788 1024
```

Autocorrelation Function plot can be used to check randomness of errors. If random, autocorrelations should be near zero for lag separations. If non-random, then autocorrelations will be significantly non-zero.

Residuals may be ordered by values of any model variable or by fitted values. If variable is not specified, function takes order from the data set.

```
lm_mr_surface <- modelResiduals(lm_audit, variable = "surface")
plot(lm_mr_surface, type = "ACF")
```

```
# alternative:
# plotACF(lm_audit, variable = "surface")
```

On the Autocorrelation plot there are i-th vs i+1-th residuals. This plot may be useful for checking autocorrelation of residuals.

Sometimes it is difficult to compare two models basing only on visualizations. Therefore, we have proposed some scores, which may be useful for choosing a better model.

```
plot(rf_mr_fitted, type = "Autocorrelation")
```

```
# alternative:
# plotAutocorrelation(rf_audit, variable = "Fitted values")
```

DW score and Runs score are based on Durbin-Watson and Runs test statistics.

Scores can be calculated with the `scoreDW()`

and `scoreRuns()`

functions or the `score()`

function with argument `score`

equals to “DW” or “Runs”.

```
rf_score_DW <- scoreDW(rf_audit, variable = "Fitted values")
rf_score_Runs <- scoreRuns(rf_audit, variable = "Fitted values")
rf_score_DW$score
```

```
## [1] 0.7958427
```

```
rf_score_Runs$score
```

```
## [1] -40.71628
```

A grid of plots presents correlation of dependennt variable and fitted model values.

```
plot(rf_mr, lm_mr, type = "ModelCorrelation")
```

```
# alternative:
# plotModelCorrelation(rf_audit, lm_audit)
```

Principal Component Analysis of models residuals. PCA can be used to assess the similarity of the models.

```
plot(rf_mr, lm_mr, type = "ModelPCA")
```

```
# alternative:
# plotModelPCA(rf_audit, lm_audit)
```

Basic plot of residuals vs observed, fitted or variable values. If variable is not specified, function takes order from the data set.

Black line corresponds to the y=x function.

```
lm_mr_m2 <- modelResiduals(lm_audit, variable = "m2.price")
rf_mr_m2 <- modelResiduals(rf_audit, variable = "m2.price")
plot(rf_mr_m2, lm_mr_m2, type = "Prediction")
```

```
# alternative:
# plotPrediction(rf_audit, lm_audit, variable = "m2.price")
```

Residuals may be ordered by values any model variable or by fitted values. And both models may be plotted together.

```
plot(rf_mr_fitted, lm_mr_fitted, type = "Residual")
```

```
# alternative:
# plotResidual(rf_audit, lm_audit, variable = "Fitted values")
```

Error Characteristic curves are a generalization of ROC curves. On the x axis of the plot there is an error tolerance and on the y axis there is a percentage of observations predicted within the given tolerance. REC curve estimates the Cumulative Distribution Function (CDF) of the error. Area Over the REC Curve (REC) is a biased estimate of the expected error.

```
plot(rf_mr, lm_mr, type = "REC")
```

```
# alternative:
# plotREC(rf_audit, lm_audit)
```

Basic plot of residuals vs observed, fitted or variable values. It provides information about the structure of the model.

```
plot(rf_mr, type = "Residual")
```

```
# alternative:
# plotResidual(rf_audit)
```

Residuals may be ordered by values any model variable or by fitted values. And both models may be plotted together. If variable is not specified, function takes order from the data set.

```
plot(rf_mr_fitted, lm_mr_fitted, type = "Residual")
```

```
# alternative:
# plotResidual(rf_audit, lm_audit, variable = "Fitted values")
```

Comparison of the absolute valued of residuals. The red dot stands for the root mean square.

```
plot(lm_mr, rf_mr, type = "ResidualBoxplot")
```

```
# alternative
# plotResidualBoxplot(lm_audit, rf_audit)
```

Density of residuals may be plotted in different ways. Residuals of models may be simply compared.

```
plot(rf_mr, lm_mr, type = "ResidualDensity")
```

```
# alternative
# plotResidualDensity(rf_audit, lm_audit)
```

Resuduals may be also divided by median of the numeric variable and splitted by a factor variable

```
plotResidualDensity(lm_mr_m2, rf_mr_m2)
```

```
# alternative
# plotResidualDensity(rf_audit, lm_audit, variable = "m2.price")
```

```
plotResidualDensity(lm_mr_district, rf_mr_district)
```

```
# alternative
# plotResidualDensity(rf_audit, lm_audit, variable = "district")
```

The basic idea of the ROC curves for regression is to show model asymmetry. The RROC is a plot where on the x-axis we depict total over-estimation and on the y-axis total under-estimation.

For RROC curves we use a shift, which is an equvalent to the threshold for ROC curves. For each observation we calculate new prediction: \eqn{\hat{y}'=\hat{y}+s} where s is the shift. Therefore, there are different error values for each shift: \eqn{e_i = \hat{y_i}' - y_i}

Over-estimation is caluclates as: \eqn{OVER= \sum(e_i|e_i>0)}. Under-estimation is calculated as: \eqn{UNDER = \sum(e_i|e_i<0)}. The shift equals 0 is represented by a dot.

The Area Over the RROC Curve (AOC) equals to the variance of the errors multiplied by \eqn{frac{n^{2}{2}}.}

```
plot(rf_mr, lm_mr, type = "RROC")
```

```
# alternative:
# plotRROC(rf_audit, lm_audit)
```

This plot shows if residuals are spread equally along the ranges of predictors.

```
plot(rf_mr_fitted, lm_mr_fitted, type = "ScaleLocation")
```

```
# alternative:
# plotScaleLocation(rf_audit, lm_audit, variable = "Fitted values")
```

For comparing 2 models we can use GQ score, which is based on Goldfeld-Quandt test statistic.
And may be computed also in `score()`

function with argument `score`

equals “GQ”.

Cumulative Distribution Function for positive and negative residuals.

```
plot(rf_mr, lm_mr, type = "TwoSidedECDF")
```

```
# alternative
# TwoSidedECDF(rf_audit, lm_audit)
```

Other methods and plots are described in vignettes: