KLR: KLR

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

KLR

Description

'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.

Usage

KLR(K, presence, lambda, maxiter = 100, tol = 0.01, verbose = 1)

Arguments

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

Details

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.

Value

list: 'pred' - predicted probabiity of positive class, 'alphas' - estimated coefficients

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)

## End(Not run)

mrecos/klrfome documentation built on April 6, 2022, 8:02 p.m.