selection.metrics {gesso}

Selection metrics

Description

Calculates principal selection metrics for the binary zero/non-zero classification problem (sensitivity, specificity, precision, auc).

Usage

selection.metrics(true_b_g, true_b_gxe, estimated_b_g, estimated_b_gxe)

Arguments

true_b_g

vector of true main effect coefficients

true_b_gxe

vector of true interaction coefficients

estimated_b_g

vector of estimated main effect coefficients

estimated_b_gxe

vector of estimated interaction coefficients

Value

A list of principal selection metrics

b_g_non_zero

number of non-zero main effects

b_gxe_non_zero

number of non-zero interactions

mse_b_g

mean squared error for estimation of main effects effect sizes

mse_b_gxe

mean squared error for estimation of interactions effect sizes

sensitivity_g

recall of the non-zero main effects

specificity_g

recall of the zero main effects

precision_g

precision with respect to non-zero main effects

sensitivity_gxe

recall of the non-zero interactions

specificity_gxe

recall of the zero interactions

precision_gxe

precision with respect to non-zero interactions

auc_g

area under the curve for zero/non-zero binary classification problem for main effects

auc_gxe

area under the curve for zero/non-zero binary classification problem for interactions

Examples

data = data.gen()
model = gesso.cv(data$G_train, data$E_train, data$Y_train)
gxe_coefficients = gesso.coef(model$fit, model$lambda_min)$beta_gxe                
g_coefficients = gesso.coef(model$fit, model$lambda_min)$beta_g  
selection.metrics(data$Beta_G, data$Beta_GxE, g_coefficients, gxe_coefficients)