KLR | R Documentation |
'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 |
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.
list: 'pred' - predicted probabiity of positive class, 'alphas' - estimated coefficients
## 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) ## End(Not run)
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