`KLR()` Is the primary model fitting function in the `klrfome` package. This function fits a Kernel Logistic Regression (KLR) model to a similarity or distance matrix.
KLR(K, presence, lambda, maxiter = 100, tol = 0.01, verbose = 1)
| K | - [NxN] Mean embedding kernel matrix |
|---|---|
| presence | - [vector] or presence = 1 or absence = 0 |
| lambda | - [scaler] Ridge regularization parameter |
| maxiter | - [integer] Maximum iterations for IRLS algorithm |
| tol | - [double] The convergence tolerance |
| verbose | - [scaler] 0 = No notice; 1 = Reports the number of steps until convergence; 2 = Reports each iteration |
list: `pred` - predicted probabiity of positive class, `alphas` - estimated coefficients
the `KLR` function takes the similarity kernel matrix `K`, a vector `presnece` of presence/absence coded as 1 or 0, and a scalar value for the `lambda` regularizing hyperparameter; optionally values for maximum iterations and threshold. This function performs Kernel Logistic Regression (KLR) via Iterative Re-weighted Least Squares (IRLS). The objective is to approximate a set of parameters that minimize the negative likelihood of the parameters given the data and response. This function returns a list of `pred`, the estimated response (probability of site-presence) for the training data, and `alphas`, the approximated parameters from the IRLS algorithm.
# 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) # }