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maxnetic

Maxnetic provide supplementary tools to augment the maxnet package.

Requirements

Installation

needed = c("AUC", "stars", "Rfast", "dplyr", "ggplot2")
installed = rownames(installed.packages())
for (need in needed) {
  if (!(need %in% installed)) install.packages(need)
}
remotes::install_github("BigelowLab/maxnetic")

Functionality

Presence only datasets

  • pAUC() for computing AUC for presence points only (aka forecasted AUC or fAUC)
  • plot, a method to plot pAUC objects

Presence-Absence datasets

  • TPR() true positive rate (“sensitivity”)
  • FPR() false positive rate (“specificity”)
  • ROC() receiver operator values
  • AUC() compute are under curve of ROC
  • plot_ROC() plot an ROC class object (base graphics or gpplot2)

General purpose utility

  • write_maxnet() and read_maxnet() for IO to R’s serialized file format
  • variable_importance() modeled after Peter D Wilson’s fitMaxnet R package varImportance() function.

Usage

suppressPackageStartupMessages({
  library(maxnet)
  library(dplyr)
  library(maxnetic)
  library(ggplot2)
})

Next we load data, make a model and then a data frame with the input labels and the output prediction.

obs <- dplyr::as_tibble(maxnet::bradypus)

model <- maxnet(obs$presence, dplyr::select(obs, -presence))
pred <- predict(model, newdata = bradypus, na.rm = TRUE, type = "cloglog")

x <- dplyr::tibble(label = obs$presence,
                   pred = pred[,1])
dplyr::glimpse(x)
## Rows: 1,116
## Columns: 2
## $ label <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ pred  <dbl> 0.1112753, 0.1128391, 0.7038097, 0.1928364, 0.2621609, 0.4292576…

Collect the responses.

r <- plot(model, type = "cloglog", plot = FALSE)

Plot the response curves

p = gather_plots(r)

p

Now compute ROC and show.

roc = ROC(x)
plot(roc, title = "Bradypus model")
## Warning in ggplot2::geom_segment(ggplot2::aes(x = 0, y = 0, xend = 1, yend = 1), : All aesthetics have length 1, but the data has 1115 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
##   a single row.

Variable importance via permutation

One way to ascertain “variable importance” is to repeatedly use the model and the input environmental data in a permutation test. A baseline prediction is made using th For each variable, the variable data is randomly shuffled and a temporary prediction is made. The Pearson’s correlation coefficient is then computed for the baseline and temporary models. A high correlation tells us that that variable doesn’t have much influence on the model output, while conversely a low correlation tells us that the variable has significant importance when computing the model. The actual metric is transformed to a more familiar “higher is more important” metric.

variable_importance(model, dplyr::select(obs, -presence), type = "cloglog",
                    arrange = "decreasing")
## # A tibble: 14 × 9
##    var         importance  mean         sd   min   q25   med   q75   max
##    <chr>            <dbl> <dbl>      <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 tmn6190_ann      24.8  0.328 0.0364     0.292 0.295 0.336 0.338 0.380
##  2 tmx6190_ann      14.1  0.617 0.0140     0.603 0.604 0.617 0.624 0.636
##  3 dtr6190_ann       9.3  0.748 0.0122     0.735 0.741 0.742 0.756 0.765
##  4 h_dem             8.88 0.759 0.0104     0.744 0.759 0.759 0.760 0.773
##  5 pre6190_l7        7.89 0.786 0.0178     0.766 0.771 0.784 0.802 0.806
##  6 pre6190_l10       7.76 0.790 0.0113     0.779 0.782 0.788 0.792 0.808
##  7 ecoreg            7.26 0.803 0.00655    0.798 0.798 0.802 0.803 0.814
##  8 frs6190_ann       5.24 0.858 0.0114     0.846 0.849 0.854 0.866 0.873
##  9 pre6190_l1        4.89 0.867 0.00534    0.863 0.865 0.867 0.867 0.876
## 10 vap6190_ann       4.39 0.881 0.00725    0.870 0.878 0.885 0.885 0.888
## 11 cld6190_ann       2.58 0.930 0.00430    0.926 0.926 0.931 0.932 0.935
## 12 pre6190_l4        2.19 0.941 0.00407    0.934 0.940 0.941 0.943 0.944
## 13 tmp6190_ann       0.73 0.980 0.00298    0.977 0.979 0.979 0.981 0.985
## 14 pre6190_ann       0    1.00  0.00000197 1.00  1.00  1.00  1.00  1.00

pAUC (presence AUC)

You can also compute a “presence AUC” which involves only the presence points. Unlike a ROC object, a pAUC object has it’s own plot method.

p = dplyr::filter(x, label == 1)
pauc = pAUC(x$pred, p$pred)
plot(pauc)
## Warning in ggplot2::geom_segment(ggplot2::aes(x = 0, y = 0, xend = 1, yend = 1), : All aesthetics have length 1, but the data has 1001 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
##   a single row.

You might be wondering why have pAUC if AUC already exists. AUC is used where you have both presence and absence data. For presence only data we need to use a modified computation.