README.md
In BigelowLab/calanusthreshold: calanusthreshold
calanusthreshold
Provides R tools for producing threshold prey models for Calanus.
Requirements
Installation
remotes::install_github("BigelowLab/calanusthreshold")
Convention
We prepend the package namespace to function not exposed by the
calanusthreshold package. Bare function names (no package prepended)
are used for base R functions and for calanusthreshold functions.
The input data - observations
We provide an anonymized example dataset.
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
library(calanusthreshold)
library(tidymodels)
})
x <- read_dataset()
dplyr::glimpse(x)
## Rows: 500
## Columns: 26
## $ year <dbl> 2004, 2002, 2012, 2008, 1999, 2014, 2002, 20…
## $ month <dbl> 6, 6, 11, 9, 12, 12, 10, 6, 4, 5, 11, 3, 6, …
## $ `Calanus finmarchicus IV` <dbl> 236.02645, 4510.13581, 1161.99465, 41.98692,…
## $ `Calanus finmarchicus V` <dbl> 670.943036, 1809.590256, 8955.440010, 1894.2…
## $ `Calanus finmarchicus VI` <dbl> 6034.0333961, 905.4738941, -1.5132048, 204.0…
## $ `Calanus hyperboreus IV` <dbl> 6483.5748842, 24903.2212951, 390.4185439, 16…
## $ `Calanus hyperboreus V` <dbl> 222.1248963, 12678.9134394, 2141.9136464, 42…
## $ `Calanus hyperboreus VI` <dbl> 0.69772204, 4073.72230427, 193.95381683, 1.5…
## $ `Calanus glacialis IV` <dbl> 1115.7922462, 1.1160846, 192.7468295, -1.411…
## $ `Calanus glacialis V` <dbl> 894.18232508, -0.11398095, 778.55885444, 0.1…
## $ `Calanus glacialis VI` <dbl> 0.08948687, -0.19533522, 0.08868125, -0.0724…
## $ bathymetry <dbl> -108.08097, -76.65710, -154.51820, -2671.281…
## $ slope <dbl> 0.60293689, 0.13743035, 1.72548948, 2.007829…
## $ aspect <dbl> 202.39786, 172.61117, 47.37903, 157.70372, 1…
## $ roughness <dbl> 16.281775, 3.607043, 39.982697, 46.942935, 1…
## $ proximity <dbl> 15487.692, 58168.940, 7770.374, 260893.082, …
## $ mlotst <dbl> 10.50661, 10.52630, 10.68431, 17.86722, 10.6…
## $ thetao <dbl> 10.4495645, 10.0880378, 4.2502529, 18.861244…
## $ usi <dbl> 1.809754e-05, -1.316802e-05, -5.998105e-07, …
## $ bottomT <dbl> 1.5842750, 0.4054613, 5.3369919, 2.9089500, …
## $ vsi <dbl> 2.581229e-06, -6.283402e-06, -1.378345e-05, …
## $ vo <dbl> 0.01168093, 0.02015713, -0.06217529, -0.0427…
## $ uo <dbl> 0.042651366, 0.034764852, 0.102006459, 0.063…
## $ so <dbl> 25.18974, 26.58304, 25.34187, 32.60157, 27.6…
## $ zos <dbl> -0.4254408, -0.4446317, -0.3484691, -0.49414…
## $ chlor_a <dbl> 6.440967e+00, 2.431810e+00, 2.728898e+00, -1…
If you have your own dataset just provide the filename and path, but be
sure to see ?read_dataset dcoumentation. Note that the variables with
species names are abundance measurements by life stage.
x <- read_dataset("/path/to/input/data.csv")
We are modeling patches of the species which we define by excess
abundance over user specified thresholds. For this example we define
just one threshold…
THRESHOLD <- 10000
For the purpose of illustration, we lump all stages of a given species
together.
Species <- calanusthreshold::known_species()
abund <- lapply(Species,
function(s) {
dplyr::tibble(species = s,
abund = calanusthreshold::lump_vars(x, vars = s,
selector = dplyr::starts_with) |>
dplyr::pull("lumped"))
})|>
dplyr::bind_rows() |>
dplyr::mutate(patch = factor(findInterval(abund, THRESHOLD),
levels = seq(from = 0, to = length(THRESHOLD)),
labels = c("sparse", THRESHOLD))) |>
dplyr::group_by(species) |>
dplyr::glimpse()
## Rows: 1,500
## Columns: 3
## Groups: species [3]
## $ species <chr> "Calanus finmarchicus", "Calanus finmarchicus", "Calanus finma…
## $ abund <dbl> 6941.0029, 7225.2000, 10115.9215, 2140.3282, 23655.3459, 2179.…
## $ patch <fct> sparse, sparse, 10000, sparse, 10000, sparse, sparse, sparse, …
We can plot the raw abundance by species group, note that we drop
records where abundance is less than 0 for the sake of clarity. Note
that C glacials generally has low patchiness when the threshold is
held high.
ggplot2::ggplot(abund %>% dplyr::filter(abund > 0), ggplot2::aes(species, abund)) +
ggplot2::geom_violin() +
ggplot2::scale_y_log10() +
ggplot2::labs(title = "Filtered input raw abundances (>0) by species",
x = "Species", y = "Abundance (log10)") +
ggplot2::geom_hline(yintercept = THRESHOLD,
linetype = 'dashed',
color = 'grey')
The model - random forest
The tidymodels attempts to provide a uniform
programmatic interface for myriad model functions/packages in R,
including random forest
models. The idea is to provide consistent argument interfaces, “engine”
selection, and uniform model assessment metrics.
We are going to model just Calanus Finmarchicus. First we prep the
dataset - dropping non-essential variables (columns), log=scaling some
variables and by aggregating (lumping) the various life-stages. The
prep_dataset() function includes a ‘patch’ label indicating that the
abundance for that observation exceeded the threshold. We then split the
data into a user specified proportion of training and testing groups.
x_split <- prep_dataset(x,
lump_var = "Calanus finmarchicus",
threshold = THRESHOLD) |>
dplyr::relocate(patch, .before = 1) |>
rsample::initial_split(prop = 0.6)
x_split
## <Analysis/Assess/Total>
## <300/200/500>
Next we build up a series of ‘recipe’ steps that prepare the data. The
prep steps implements all of the other steps, including removal highly
correlated variable (like roughness).
x_recipe <- rsample::training(x_split) |>
recipes::recipe(patch ~.) |>
recipes::step_corr(recipes::all_predictors()) |>
recipes::step_center(recipes::all_predictors(), -recipes::all_outcomes()) |>
recipes::step_scale(recipes::all_predictors(), -recipes::all_outcomes()) |>
recipes::prep()
x_recipe
## Recipe
##
## Inputs:
##
## role #variables
## outcome 1
## predictor 16
##
## Training data contained 300 data points and no missing data.
##
## Operations:
##
## Correlation filter removed slope [trained]
## Centering for month, bathymetry, aspect, roughness, proximity... [trained]
## Scaling for month, bathymetry, aspect, roughness, proximity... [trained]
Now we can ‘bake’ the training and testing datasets.
x_testing <- x_recipe |>
recipes::bake(rsample::testing(x_split))
x_training <- x_recipe |>
recipes::bake(rsample::training(x_split))
Now we build and fit a model. Note the we are accepting the defaults
arguments for the
ranger::ranger()
function.
x_model <- parsnip::rand_forest(mode = "classification") |>
parsnip::set_engine("ranger") |>
parsnip::fit(patch ~ ., data = x_training)
x_model
## parsnip model object
##
## Fit time: 114ms
## Ranger result
##
## Call:
## ranger::ranger(x = maybe_data_frame(x), y = y, num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1), probability = TRUE)
##
## Type: Probability estimation
## Number of trees: 500
## Sample size: 300
## Number of independent variables: 15
## Mtry: 3
## Target node size: 10
## Variable importance mode: none
## Splitrule: gini
## OOB prediction error (Brier s.): 0.1891881
The prediction
We can now predict the outcome if we pass in another dataset, in the
case the testing data we reserved as 40% of the original. We’ll bind the
prediction to the testing data and reorganize for clarity. In the output
patch is out original (aka ‘Truth’) while .pred_class is the
predicted class (aka ‘Predicted’)
x_pred <- predict(x_model, x_testing) |>
dplyr::bind_cols(x_testing) |>
dplyr::relocate(patch, .before = 1)
dplyr::glimpse(x_pred)
## Rows: 200
## Columns: 17
## $ patch <fct> 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0…
## $ .pred_class <fct> 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0…
## $ month <dbl> -0.5938016, 1.0931348, -0.5938016, -0.9311889, -0.5938016,…
## $ bathymetry <dbl> -0.52985149, -0.16543546, -1.01676056, -0.21770387, -1.136…
## $ aspect <dbl> 0.63420933, -2.95142930, -0.49349153, -0.07294458, 0.03341…
## $ roughness <dbl> -0.06580198, 0.85224565, -0.62738980, -0.13441518, -0.4973…
## $ proximity <dbl> -0.76641226, -0.88764152, -0.40780727, -0.35648322, -0.028…
## $ mlotst <dbl> -0.66731877, -0.63769021, -0.66127137, -0.66517243, -0.660…
## $ thetao <dbl> 0.48489466, -0.80637988, 0.50472807, -0.71136728, 0.480468…
## $ usi <dbl> -0.03978587, -0.04614104, -0.03994837, -0.04399429, -0.047…
## $ bottomT <dbl> -1.08215478, 0.49815904, -1.22295850, 1.36070073, -1.65113…
## $ vsi <dbl> -0.02808831, -0.03429746, -0.03444024, -0.03114629, -0.021…
## $ vo <dbl> 0.54237767, -0.50575367, 0.20304179, -0.66386630, 0.765944…
## $ uo <dbl> 0.28014988, 0.88331687, -0.12154074, -1.55425681, -0.14030…
## $ so <dbl> -0.67001595, -0.63660482, 0.03587577, 0.59319538, 0.096127…
## $ zos <dbl> 0.25691085, 1.51196302, -0.41492897, -0.88338888, -0.41423…
## $ chlor_a <dbl> 0.351305662, 0.002346937, -0.449618642, -0.952865393, -0.4…
We can compute and examine a confusion matrix.
x_cm <- yardstick::conf_mat(x_pred, patch, .pred_class)
x_cm
## Truth
## Prediction 0 1
## 0 115 34
## 1 11 40
Or present graphically…
heat_map(x_cm, title = "Calanus finmarchicus")
Similar the the ease of computing a predicted class value, we can also
produce predicted class probabilities. Here we
x_prob <- predict(x_model, x_testing, type = "prob") |>
dplyr::bind_cols(x_testing) |>
dplyr::relocate(patch, .before = 1) |>
dplyr::glimpse()
## Rows: 200
## Columns: 18
## $ patch <fct> 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0,…
## $ .pred_0 <dbl> 0.9034413, 0.4457603, 0.7941833, 0.3558579, 0.7606325, 0.38…
## $ .pred_1 <dbl> 0.09655873, 0.55423968, 0.20581667, 0.64414206, 0.23936746,…
## $ month <dbl> -0.5938016, 1.0931348, -0.5938016, -0.9311889, -0.5938016, …
## $ bathymetry <dbl> -0.52985149, -0.16543546, -1.01676056, -0.21770387, -1.1369…
## $ aspect <dbl> 0.63420933, -2.95142930, -0.49349153, -0.07294458, 0.033411…
## $ roughness <dbl> -0.06580198, 0.85224565, -0.62738980, -0.13441518, -0.49735…
## $ proximity <dbl> -0.76641226, -0.88764152, -0.40780727, -0.35648322, -0.0281…
## $ mlotst <dbl> -0.66731877, -0.63769021, -0.66127137, -0.66517243, -0.6609…
## $ thetao <dbl> 0.48489466, -0.80637988, 0.50472807, -0.71136728, 0.4804684…
## $ usi <dbl> -0.03978587, -0.04614104, -0.03994837, -0.04399429, -0.0475…
## $ bottomT <dbl> -1.08215478, 0.49815904, -1.22295850, 1.36070073, -1.651135…
## $ vsi <dbl> -0.02808831, -0.03429746, -0.03444024, -0.03114629, -0.0217…
## $ vo <dbl> 0.54237767, -0.50575367, 0.20304179, -0.66386630, 0.7659446…
## $ uo <dbl> 0.28014988, 0.88331687, -0.12154074, -1.55425681, -0.140303…
## $ so <dbl> -0.67001595, -0.63660482, 0.03587577, 0.59319538, 0.0961272…
## $ zos <dbl> 0.25691085, 1.51196302, -0.41492897, -0.88338888, -0.414235…
## $ chlor_a <dbl> 0.351305662, 0.002346937, -0.449618642, -0.952865393, -0.47…
From the probabilities for each class we can compute ACU on the ROC.
x_auc <- yardstick::roc_auc(x_prob, patch, .pred_1, event_level = 'second') |>
dplyr::glimpse()
## Rows: 1
## Columns: 3
## $ .metric <chr> "roc_auc"
## $ .estimator <chr> "binary"
## $ .estimate <dbl> 0.8117761
BigelowLab/calanusthreshold documentation built on May 12, 2022, 5:06 a.m.
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calanusthreshold
Provides R tools for producing threshold prey models for Calanus.
Requirements
Installation
remotes::install_github("BigelowLab/calanusthreshold")
Convention
We prepend the package namespace to function not exposed by the
calanusthresholdpackage. Bare function names (no package prepended) are used for base R functions and forcalanusthresholdfunctions.
The input data - observations
We provide an anonymized example dataset.
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
library(calanusthreshold)
library(tidymodels)
})
x <- read_dataset()
dplyr::glimpse(x)
## Rows: 500
## Columns: 26
## $ year <dbl> 2004, 2002, 2012, 2008, 1999, 2014, 2002, 20…
## $ month <dbl> 6, 6, 11, 9, 12, 12, 10, 6, 4, 5, 11, 3, 6, …
## $ `Calanus finmarchicus IV` <dbl> 236.02645, 4510.13581, 1161.99465, 41.98692,…
## $ `Calanus finmarchicus V` <dbl> 670.943036, 1809.590256, 8955.440010, 1894.2…
## $ `Calanus finmarchicus VI` <dbl> 6034.0333961, 905.4738941, -1.5132048, 204.0…
## $ `Calanus hyperboreus IV` <dbl> 6483.5748842, 24903.2212951, 390.4185439, 16…
## $ `Calanus hyperboreus V` <dbl> 222.1248963, 12678.9134394, 2141.9136464, 42…
## $ `Calanus hyperboreus VI` <dbl> 0.69772204, 4073.72230427, 193.95381683, 1.5…
## $ `Calanus glacialis IV` <dbl> 1115.7922462, 1.1160846, 192.7468295, -1.411…
## $ `Calanus glacialis V` <dbl> 894.18232508, -0.11398095, 778.55885444, 0.1…
## $ `Calanus glacialis VI` <dbl> 0.08948687, -0.19533522, 0.08868125, -0.0724…
## $ bathymetry <dbl> -108.08097, -76.65710, -154.51820, -2671.281…
## $ slope <dbl> 0.60293689, 0.13743035, 1.72548948, 2.007829…
## $ aspect <dbl> 202.39786, 172.61117, 47.37903, 157.70372, 1…
## $ roughness <dbl> 16.281775, 3.607043, 39.982697, 46.942935, 1…
## $ proximity <dbl> 15487.692, 58168.940, 7770.374, 260893.082, …
## $ mlotst <dbl> 10.50661, 10.52630, 10.68431, 17.86722, 10.6…
## $ thetao <dbl> 10.4495645, 10.0880378, 4.2502529, 18.861244…
## $ usi <dbl> 1.809754e-05, -1.316802e-05, -5.998105e-07, …
## $ bottomT <dbl> 1.5842750, 0.4054613, 5.3369919, 2.9089500, …
## $ vsi <dbl> 2.581229e-06, -6.283402e-06, -1.378345e-05, …
## $ vo <dbl> 0.01168093, 0.02015713, -0.06217529, -0.0427…
## $ uo <dbl> 0.042651366, 0.034764852, 0.102006459, 0.063…
## $ so <dbl> 25.18974, 26.58304, 25.34187, 32.60157, 27.6…
## $ zos <dbl> -0.4254408, -0.4446317, -0.3484691, -0.49414…
## $ chlor_a <dbl> 6.440967e+00, 2.431810e+00, 2.728898e+00, -1…
If you have your own dataset just provide the filename and path, but be
sure to see ?read_dataset dcoumentation. Note that the variables with
species names are abundance measurements by life stage.
x <- read_dataset("/path/to/input/data.csv")
We are modeling patches of the species which we define by excess abundance over user specified thresholds. For this example we define just one threshold…
THRESHOLD <- 10000
For the purpose of illustration, we lump all stages of a given species together.
Species <- calanusthreshold::known_species()
abund <- lapply(Species,
function(s) {
dplyr::tibble(species = s,
abund = calanusthreshold::lump_vars(x, vars = s,
selector = dplyr::starts_with) |>
dplyr::pull("lumped"))
})|>
dplyr::bind_rows() |>
dplyr::mutate(patch = factor(findInterval(abund, THRESHOLD),
levels = seq(from = 0, to = length(THRESHOLD)),
labels = c("sparse", THRESHOLD))) |>
dplyr::group_by(species) |>
dplyr::glimpse()
## Rows: 1,500
## Columns: 3
## Groups: species [3]
## $ species <chr> "Calanus finmarchicus", "Calanus finmarchicus", "Calanus finma…
## $ abund <dbl> 6941.0029, 7225.2000, 10115.9215, 2140.3282, 23655.3459, 2179.…
## $ patch <fct> sparse, sparse, 10000, sparse, 10000, sparse, sparse, sparse, …
We can plot the raw abundance by species group, note that we drop records where abundance is less than 0 for the sake of clarity. Note that C glacials generally has low patchiness when the threshold is held high.
ggplot2::ggplot(abund %>% dplyr::filter(abund > 0), ggplot2::aes(species, abund)) +
ggplot2::geom_violin() +
ggplot2::scale_y_log10() +
ggplot2::labs(title = "Filtered input raw abundances (>0) by species",
x = "Species", y = "Abundance (log10)") +
ggplot2::geom_hline(yintercept = THRESHOLD,
linetype = 'dashed',
color = 'grey')
The model - random forest
The tidymodels attempts to provide a uniform programmatic interface for myriad model functions/packages in R, including random forest models. The idea is to provide consistent argument interfaces, “engine” selection, and uniform model assessment metrics.
We are going to model just Calanus Finmarchicus. First we prep the
dataset - dropping non-essential variables (columns), log=scaling some
variables and by aggregating (lumping) the various life-stages. The
prep_dataset() function includes a ‘patch’ label indicating that the
abundance for that observation exceeded the threshold. We then split the
data into a user specified proportion of training and testing groups.
x_split <- prep_dataset(x,
lump_var = "Calanus finmarchicus",
threshold = THRESHOLD) |>
dplyr::relocate(patch, .before = 1) |>
rsample::initial_split(prop = 0.6)
x_split
## <Analysis/Assess/Total>
## <300/200/500>
Next we build up a series of ‘recipe’ steps that prepare the data. The
prep steps implements all of the other steps, including removal highly
correlated variable (like roughness).
x_recipe <- rsample::training(x_split) |>
recipes::recipe(patch ~.) |>
recipes::step_corr(recipes::all_predictors()) |>
recipes::step_center(recipes::all_predictors(), -recipes::all_outcomes()) |>
recipes::step_scale(recipes::all_predictors(), -recipes::all_outcomes()) |>
recipes::prep()
x_recipe
## Recipe
##
## Inputs:
##
## role #variables
## outcome 1
## predictor 16
##
## Training data contained 300 data points and no missing data.
##
## Operations:
##
## Correlation filter removed slope [trained]
## Centering for month, bathymetry, aspect, roughness, proximity... [trained]
## Scaling for month, bathymetry, aspect, roughness, proximity... [trained]
Now we can ‘bake’ the training and testing datasets.
x_testing <- x_recipe |>
recipes::bake(rsample::testing(x_split))
x_training <- x_recipe |>
recipes::bake(rsample::training(x_split))
Now we build and fit a model. Note the we are accepting the defaults arguments for the ranger::ranger() function.
x_model <- parsnip::rand_forest(mode = "classification") |>
parsnip::set_engine("ranger") |>
parsnip::fit(patch ~ ., data = x_training)
x_model
## parsnip model object
##
## Fit time: 114ms
## Ranger result
##
## Call:
## ranger::ranger(x = maybe_data_frame(x), y = y, num.threads = 1, verbose = FALSE, seed = sample.int(10^5, 1), probability = TRUE)
##
## Type: Probability estimation
## Number of trees: 500
## Sample size: 300
## Number of independent variables: 15
## Mtry: 3
## Target node size: 10
## Variable importance mode: none
## Splitrule: gini
## OOB prediction error (Brier s.): 0.1891881
The prediction
We can now predict the outcome if we pass in another dataset, in the
case the testing data we reserved as 40% of the original. We’ll bind the
prediction to the testing data and reorganize for clarity. In the output
patch is out original (aka ‘Truth’) while .pred_class is the
predicted class (aka ‘Predicted’)
x_pred <- predict(x_model, x_testing) |>
dplyr::bind_cols(x_testing) |>
dplyr::relocate(patch, .before = 1)
dplyr::glimpse(x_pred)
## Rows: 200
## Columns: 17
## $ patch <fct> 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0…
## $ .pred_class <fct> 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0…
## $ month <dbl> -0.5938016, 1.0931348, -0.5938016, -0.9311889, -0.5938016,…
## $ bathymetry <dbl> -0.52985149, -0.16543546, -1.01676056, -0.21770387, -1.136…
## $ aspect <dbl> 0.63420933, -2.95142930, -0.49349153, -0.07294458, 0.03341…
## $ roughness <dbl> -0.06580198, 0.85224565, -0.62738980, -0.13441518, -0.4973…
## $ proximity <dbl> -0.76641226, -0.88764152, -0.40780727, -0.35648322, -0.028…
## $ mlotst <dbl> -0.66731877, -0.63769021, -0.66127137, -0.66517243, -0.660…
## $ thetao <dbl> 0.48489466, -0.80637988, 0.50472807, -0.71136728, 0.480468…
## $ usi <dbl> -0.03978587, -0.04614104, -0.03994837, -0.04399429, -0.047…
## $ bottomT <dbl> -1.08215478, 0.49815904, -1.22295850, 1.36070073, -1.65113…
## $ vsi <dbl> -0.02808831, -0.03429746, -0.03444024, -0.03114629, -0.021…
## $ vo <dbl> 0.54237767, -0.50575367, 0.20304179, -0.66386630, 0.765944…
## $ uo <dbl> 0.28014988, 0.88331687, -0.12154074, -1.55425681, -0.14030…
## $ so <dbl> -0.67001595, -0.63660482, 0.03587577, 0.59319538, 0.096127…
## $ zos <dbl> 0.25691085, 1.51196302, -0.41492897, -0.88338888, -0.41423…
## $ chlor_a <dbl> 0.351305662, 0.002346937, -0.449618642, -0.952865393, -0.4…
We can compute and examine a confusion matrix.
x_cm <- yardstick::conf_mat(x_pred, patch, .pred_class)
x_cm
## Truth
## Prediction 0 1
## 0 115 34
## 1 11 40
Or present graphically…
heat_map(x_cm, title = "Calanus finmarchicus")
Similar the the ease of computing a predicted class value, we can also produce predicted class probabilities. Here we
x_prob <- predict(x_model, x_testing, type = "prob") |>
dplyr::bind_cols(x_testing) |>
dplyr::relocate(patch, .before = 1) |>
dplyr::glimpse()
## Rows: 200
## Columns: 18
## $ patch <fct> 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0,…
## $ .pred_0 <dbl> 0.9034413, 0.4457603, 0.7941833, 0.3558579, 0.7606325, 0.38…
## $ .pred_1 <dbl> 0.09655873, 0.55423968, 0.20581667, 0.64414206, 0.23936746,…
## $ month <dbl> -0.5938016, 1.0931348, -0.5938016, -0.9311889, -0.5938016, …
## $ bathymetry <dbl> -0.52985149, -0.16543546, -1.01676056, -0.21770387, -1.1369…
## $ aspect <dbl> 0.63420933, -2.95142930, -0.49349153, -0.07294458, 0.033411…
## $ roughness <dbl> -0.06580198, 0.85224565, -0.62738980, -0.13441518, -0.49735…
## $ proximity <dbl> -0.76641226, -0.88764152, -0.40780727, -0.35648322, -0.0281…
## $ mlotst <dbl> -0.66731877, -0.63769021, -0.66127137, -0.66517243, -0.6609…
## $ thetao <dbl> 0.48489466, -0.80637988, 0.50472807, -0.71136728, 0.4804684…
## $ usi <dbl> -0.03978587, -0.04614104, -0.03994837, -0.04399429, -0.0475…
## $ bottomT <dbl> -1.08215478, 0.49815904, -1.22295850, 1.36070073, -1.651135…
## $ vsi <dbl> -0.02808831, -0.03429746, -0.03444024, -0.03114629, -0.0217…
## $ vo <dbl> 0.54237767, -0.50575367, 0.20304179, -0.66386630, 0.7659446…
## $ uo <dbl> 0.28014988, 0.88331687, -0.12154074, -1.55425681, -0.140303…
## $ so <dbl> -0.67001595, -0.63660482, 0.03587577, 0.59319538, 0.0961272…
## $ zos <dbl> 0.25691085, 1.51196302, -0.41492897, -0.88338888, -0.414235…
## $ chlor_a <dbl> 0.351305662, 0.002346937, -0.449618642, -0.952865393, -0.47…
From the probabilities for each class we can compute ACU on the ROC.
x_auc <- yardstick::roc_auc(x_prob, patch, .pred_1, event_level = 'second') |>
dplyr::glimpse()
## Rows: 1
## Columns: 3
## $ .metric <chr> "roc_auc"
## $ .estimator <chr> "binary"
## $ .estimate <dbl> 0.8117761
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