make_quads: make_quads

View source: R/metrics.R

make_quadsR Documentation

make_quads

Description

'make_quads()' Produces True Positive, False Positive, True Negative, and False Negative quantities from two binary input vectors of predicted and observed classes.

Usage

make_quads(pred, obs)

Arguments

pred

- [vector] Predicted probabilities.

obs

- [vector] Observed presence/absence as 1/0

Details

This function takes two binary vectors coded as 1 == presence/positive and 0 == absent/negative. The 'pred' vector is the predicted binary class and 'obs' vector is the true observed class. The output quantites can be used for additional metric evaluation.

Value

[vector] TP, FP, TN, FN names vector

Examples

## Not run: 
sim_data <- get_sim_data(site_samples = 800, N_site_bags = 75,
sites_var1_mean = 80, sites_var1_sd   = 10,
sites_var2_mean = 5,  sites_var2_sd   = 2,
backg_var1_mean = 100,backg_var1_sd   = 20,
backg_var2_mean = 6,  backg_var2_sd   = 3)
formatted_data <- format_site_data(sim_data, N_sites=10, train_test_split=0.8,
                                   sample_fraction = 0.9, background_site_balance=1)
train_data <- formatted_data[["train_data"]]
train_presence <- formatted_data[["train_presence"]]
test_presence <- formatted_data[["test_presence"]]

##### Logistic Mean Embedding KLR Model
#### Build Kernel Matrix
K <- build_K(train_data, sigma = sigma, dist_metric = dist_metric)
#### Train
train_log_pred <- KLR(K, train_presence, lambda, 100, 0.001, verbose = 2)
#### Predict
test_log_pred <- KLR_predict(test_data, train_data, dist_metric = dist_metric,
                            train_log_pred[["alphas"]], sigma)
                            
cm <- make_quads(ifelse(test_log_pred >= 0.5, 1, 0), test_presence)

## End(Not run)


mrecos/DistRegLMERR documentation built on April 9, 2022, 5:10 p.m.