The goal of `randomForestVIP`

is to tune and select a good
Random Forest model based on the accuracy and variable importance
metrics associated with each model. To accomplish this, functions are
available to tabulate and plot results designed to help the user select
an optimal model.

This package contains functions for assessing variable relations and
associations prior to modeling with a Random Forest algorithm (although
these are relevant for any predictive model). Metrics such as partial
correlations and variance inflation factors are tabulated as well as
plotted for the user using the functions `partial_cor`

and
`robust_vifs`

.

The function `mtry_compare`

is available for tuning the
hyper-parameter mtry based on model performance and variable importance
metrics. This grid-search technique provides tables and plots showing
the effect of mtry on each of the assessment metrics. It also returns
each of the evaluated models to the user.

The package also provides superior ggplot2 variable importance plots
for individual models using the function `ggvip`

. This
function is a highly aesthetic and editable improvement upon the
function `randomForest::varImpPlot`

and other basic
importance graphics.

All of the plots generated by these functions are developed with ggplot2 techniques so that the user has the ability to edit and improve further upon the plots.

For methodology see “Contributions to Random Forest Variable Importance with Applications in R” https://digitalcommons.usu.edu/etd/8587/.

You can install the released version of `randomForestVIP`

from CRAN with:

`install.packages("randomForestVIP")`

You can install the development version of
`randomForestVIP`

from GitHub with:

```
# install.packages("devtools")
::install_github("KelvynBladen/randomForestVIP") devtools
```

You can view R package’s source code on GitHub: https://github.com/KelvynBladen/randomForestVIP

```
library(randomForestVIP)
library(MASS)
library(EZtune)
```

To introduce the functionality of `randomForestVIP`

, we
look at modeling the Boston housing data (found in the MASS package). We
want to build a Random Forest model with a view towards both accuracy
and interpretability. We begin by running some preliminary diagnostics
on our data.

```
set.seed(1234)
<- partial_cor(medv ~ ., data = Boston, model = lm)
pcs $plot_y_part_cors pcs
```

```
<- robust_vifs(medv ~ ., data = Boston, model = lm)
rv $plot_lin_vifs rv
```

These functions assess concerns with collinearity. Notice that the
VIFs from `robust_vifs`

are all less than 10. The partial
correlations with the response from `partial_cor`

are a type
of pseudo-importance assessing the importance each variable does not
share with the others. Now we tune our model by assessing four different
mtry values in the `mtry_compare`

function.

```
set.seed(1)
<- mtry_compare(medv ~ .,
m data = Boston, sqrt = TRUE,
mvec = c(1, 4, 9, 13), num_var = 7
)$gg_model_errors m
```

```
$model_errors
m#> mtry mse
#> 1 1 18.96051
#> 2 4 10.11247
#> 3 9 10.13187
#> 4 13 10.36482
```

According to the accuracy plot and table above, our best choice is when mtry is 4. However, the accuracy for the best model is notably only slightly better than the models with mtry set to 9 and 13. We now look at the variable importance metrics across the different models.

`$gg_var_imp_error m`

The top two variables are consistently identified as more important than the other variables and their order remains unchanged across mtry. However, the variables ‘nox’ and ‘dis’ switch order as mtry increases. Pollution (nox) has a strong negative correlation with distance to employment centers (dis). This makes sense if the employment centers are responsible for much of the pollution. If many home buyers consider distance to work more important than pollution when selecting a house, ‘dis’ is more likely to be a causal driver of price than ‘nox’. By this reasoning, the model where mtry is 9 appears to be superior to the model where mtry is 4, despite mtry of 4 yielding slightly more accurate results.

We now take our selected model and build individual importance plots
for it using `ggvip`

.

`<- ggvip(m$rf9, num_var = 7)$both_vips g `

The plot above resembles a standard variable importance plot, but possesses superior tick labels and editing capabilities for the analyst.

We have used the `randomForestVIP`

package to tune a
strong model for prediction and with reasonably useful importance
values. This was accomplished by assessing variable importance and
accuracy metrics across the hyper-parameter mtry.