CM_quads: CM_quads

In mrecos/DistRegLMERR: Kernel Logistic Regression with Focal Mean Embeddings

CM_quads

Description

'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.

Usage

CM_quads(dat, threshold = 0.5)

Arguments

dat	- [data.frame] Table with two columns, "pred" and "presence". "pred" is the predicted probability. "presence" is the observed presence/absence as 1/0.
threshold	- [scalar or vector] a scalar or vector of one or more thresholds at which to evaluate the Confusion Matrix quadrants.

Details

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'.

Value

[data.frame] A data.frame of Confusion Matrix quatrants at one or more threshold values.

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_values <- CM_quads(data.frame(pred=test_log_pred, presence=test_presence))

## End(Not run)

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