#Model Fit audit

The half-normal plot presented in this chapter is one of the tools designed to evaluate the goodness of fit of a statistical model. It is a graphical method for comparing two probability distributions by plotting their quantiles against each other.

Points on the plot correspond to ordered absolute values of model diagnostic (i.e. standardized residuals) plotted against theoretical order statistics from a half-normal distribution.

There are various implementations of half-normal plots in R. Functions for generating such plotes are available in packages **auditor**, **faraway**, **hnp**, but also in others.
Some functions can only draw a simple half-normal plot, while some have additional functionalities like a simulated envelope and score of goodness-of-fit.

```
library(auditor)
set.seed(123)
```

Function `plotHalfNormal()`

offers a plotting interface for half-normal plots generated by hnp package in a unified style using `ggplot2`

.
Additional functionalities not included in the hnp are scores and the possibility to draw half-normal plot on a quantile scale.

Below we present example of the use of half-normal plots for a generalized linear models.
Plots are generated by `plotHalfNormal()`

function from package **auditor**.

By default, deviance residuals were used as diagnostic values.

We will use dataset corn from the `hnp`

package. For more details on the data set and models see Moral, R., Hinde, J., & Demétrio, C. (2017). *Half-Normal Plots and Overdispersed Models in R: The hnp Package*.

```
data("corn", package = "hnp")
head(corn)
```

If diagnostic values are from the normal distribution, they are close to a straight line. However, if they don't come from a normal distribution, they still show a certain trend. Simulated envelopes can be used to help verify the correctness of this trend. For a well-fitted model, diagnostic values should lay within the envelope.

First step of auditing is fitting a model and creating an `explainer`

object with DALEX package which wraps up a model with meta-data.

```
model_bin <- glm(cbind(y, m - y) ~ extract, family = binomial, data = corn)
bin_exp <- DALEX::explain(model_bin, data = corn, y = corn$y)
```

Second step is creating `auditor_model_halfnormal`

object that can be further used for validating a model.

```
bin_hnp <- model_halfnormal(bin_exp)
```

```
## Binomial model
```

`auditor_model_halfnormal`

object may be plotted.

```
plot(bin_hnp, sim=500)
```

```
# or
# plot_halfnormal(bin_hnp, sim = 500)
```

It doesn't fit because of the overdispersion in data.

In function `plotHalfNormal`

there is also a posibility to draw half-normal plot on a quantile scale.

```
plot(bin_hnp, quantiles = TRUE)
```

HalfNormal plots works also for classification random Forest.

In this case we consider the differences between observed class and predicted probabilities to be residuals.

```
library(randomForest)
iris_rf <- randomForest(Species ~ ., data=iris)
iris_rf_exp <- DALEX::explain(iris_rf, data = iris, y = as.numeric(iris$Species)-1)
```

```
## Preparation of a new explainer is initiated
## -> model label : randomForest ([33mdefault[39m)
## -> data : 150 rows 5 cols
## -> target variable : 150 values
## -> predict function : yhat.randomForest will be used ([33mdefault[39m)
## -> predicted values : numerical, min = 0 , mean = 0.3333333 , max = 1
## -> residual function : difference between y and yhat ([33mdefault[39m)
## -> residuals : numerical, min = -1 , mean = 0.6666667 , max = 2
## [32mA new explainer has been created![39m
```

```
iris_rf_hnp <- model_halfnormal(iris_rf_exp)
plot_halfnormal(iris_rf_hnp)
```

Other methods and plots are described in vignettes:

Moral, R., Hinde, J., & Demétrio, C. (2017). *Half-Normal Plots and Overdispersed Models in R: The hnp Package*. Journal of Statistical Software, 81(10), 1 - 23. doi:http://dx.doi.org/10.18637/jss.v081.i10