Permutation importance

For this example we train a Maxnet model:

maxnet_model <- train("Maxnet", data = train)

Now we can calculate the variable importance with the function varImp() using 5 permutations:

vi_maxnet <- varImp(maxnet_model, permut = 5)
vi_maxnet

And plot it with:

plotVarImp(vi_maxnet)

Next we compute the permutation importance for the Maxent model using 10 permutations and compare the results with the Maxent output:

# Compute the permutation importance
vi_maxent <- varImp(model, permut = 10)
# Print it
vi_maxent
# Compare with Maxent output
maxentVarImp(model)

The difference is probably due to a different shuffling of the presence and background locations during the permutation process and because in this example we performed 10 permutations and averaged the values.

Jackknife test for variable importance

Another method to estimate the variable importance is the leave one out Jackknife test. The test removes one variable at time and records the change in the chosen metric. We use the function doJk, the AUC as evaluation metric and the maxnet_model:

jk <- doJk(maxnet_model, metric = "auc", test = test)
jk

We can also plot the output using the function plotJk. In the following example we plot the previous result and we add a line representing the AUC of the full model trained using all the variables. First we plot the Jackknife test for the training AUC:

plotJk(jk, type = "train", ref = auc(maxnet_model))

and the Jackknife test for the testing AUC:

plotJk(jk, type = "test", ref = auc(maxnet_model, test = test))

Response curves

With the function plotResponse is possible to plot the marginal and the univariate response curve. Let’s plot the cloglog univariate response curve of bio1:

plotResponse(maxnet_model, var = "bio1", type = "cloglog", only_presence = TRUE,
             marginal = FALSE, rug = TRUE)

On top is displayed the rug of the presence locations and on bottom the rug of the background locations. As another example we can plot the logistic marginal response curve of biome that is a categorical variable, keeping the other variables at the mean value:

plotResponse(maxnet_model, var = "biome", type = "logistic",
             only_presence = TRUE, marginal = TRUE, fun = mean, color = "blue")

In the case of an SDMmodelCV the response curve shows the averaged value of the prediction together with one Standard Deviation error interval:

plotResponse(cv_model, var = "bio1", type = "cloglog", only_presence = TRUE,
             marginal = TRUE, fun = mean, rug = TRUE)

Model report

All what you have learned till now con be saved and summarized calling the function modelReport. The function will:

• save the training, background and testing locations in separated csv files;
• train and evaluate the model;
• create a report in a html format with the ROC curve, threshold values, response curves, predicted map and Jackknife test;
• save the predicted distribution map;
• save all the curves in the plot folder;
• save the model with .Rds extension that can be loaded in R using the readRDS function.

The function is totally inspired by the default output of the MaxEnt Java software (Phillips, Anderson, and Schapire 2006) and extends it to other methods. You can decide what to include in the report using dedicated function arguments, like response_curves, jk and env but the function cannot be used with SDMmodelCV objects. Run the following code to create a report of the Maxnet model we trained before:

modelReport(maxnet_model, type = "cloglog", folder = "virtual-sp", test = test,
            response_curves = TRUE, only_presence = TRUE, jk = TRUE,
            env = predictors)

The output is displayed in the browser and all the files are saved in the virtual-sp folder.

Remove highly correlated variables

SDMtune implements an algorithm that removes highly correlated variables repeating the following steps:

1. ranks the variables according to the permutation importance or the percent contribution (the second method is available only for Maxent models).
2. checks if the variable ranked as most important is highly correlated with other variables, according to the given method and correlation threshold. If the algorithm finds correlated variables it moves to the next step, otherwise checks the other variables in the rank;
3. performs a leave one out Jackknife test among the correlated variables;
4. remove the variable that decreases the less the model performance when removed, according to the given metric on the training dataset.

The process is repeated until the remaining variables have a correlation coefficient lower than the provided correlation threshold. In the next example we remove the variables that have a Spearman correlation coefficient \(|r_s|\leq0.7\) and checking the AUC on the training dataset (we use only one permutation to save time but you are free to increase this value). If you use RStudio, the function creates an interactive real-time chart in the viewer pane. Run the following code and hover over the chart during the execution of the function to get extra information:

selected_variables_model <- varSel(maxnet_model, metric = "auc", test = test,
                                   bg4cor = bg, method = "spearman",
                                   cor_th = 0.7, permut = 1)

As you can see some variables have been removed. The output of the function is the model trained with the selected variables:

selected_variables_model

In the next example we remove the highly correlated variables using the Maxent model, ranking the variables with the percent contribution and using the AICc as evaluation metric:

selected_variables_model <- varSel(model, metric = "aicc", bg4cor = bg,
                                   method = "spearman", cor_th = 0.7,
                                   env = predictors, use_pc = TRUE)

Remove variables with low importance

There are cases in which a model has some environmental variables ranked with very low contribution and you may want to remove some of them to reduce the model complexity. SDMtune offers two different strategies implemented in the function reduceVar. We will use the Maxent model trained with all the variables. Let’s first check the permutation importance (we use only one permutation to save time):

varImp(model, permut = 1)

In the first example we want to remove all the environmental variables that have a permutation importance lower than 6%, no matter if the model performance decreases. The function removes the last ranked environmental variable, trains a new model and computes a new rank. The process is repeated until all the remaining environmental variables have an importance greater than 6%:

cat("Testing AUC before: ", auc(maxnet_model, test = test))
reduced_variables_model <- reduceVar(maxnet_model, th = 6, metric = "auc",
                                     test = test, permut = 1)
cat("Testing AUC after: ", auc(reduced_variables_model, test = test))

In the second example we want to remove the environmental variables that have a permutation importance lower than 15% only if removing the variables the model performance does not decrease, according to the given metric. In this case the function performs a leave one out Jackknife test and remove the environmental variables in a step-wise fashion as described in the previous example, but only if the model performance doesn’t drop:

cat("Testing AUC before: ", auc(maxnet_model, test = test))
reduced_variables_model <- reduceVar(maxnet_model, th = 15, metric = "auc",
                                     test = test, permut = 1, use_jk = TRUE)
cat("Testing AUC after: ", auc(reduced_variables_model, test = test))

As you can see in this case several variables have been removed and the AUC in the testing dataset didn’t decrease.