`CM_quads()` produces the True Positive, False Positive, True Negative, and False Negative quantities of the Confusion Matrix at one or more threshholds for the binary classification of continuous prediction values.
CM_quads(dat, threshold = 0.5)
| dat | - [data.frame] Table with two columns, "pred" and "presence". "pred" is the predicted probability. "presence" is the observed presence/absence as 1/0. |
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| threshold | - [scalar or vector] a scalar or vector of one or more thresholds at which to evaluate the Confusion Matrix quadrants. |
[data.frame] A data.frame of Confusion Matrix quatrants at one or more threshold values.
This function takes two arguments: 1) a data.frame of two columns; `pred` is a column of continuous predicted values and `presence` is a binary observed class value (encoded as `1 == present` or `0 == absent`); and 2) a scalar or numeric vector of threshold values `threshold` at which to classify the continuous values of `pred` into binary 1/0 classes. The results is a data table where each row holds the TP, FP, TN, FN counts for each of the thresholds given in `threshold`.
# 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_values <- CM_quads(data.frame(pred=test_log_pred, presence=test_presence)) # }