get_optim_roc: Find optimal ROC threshold

In HopkinsIDD/phylosamp: Sample Size Calculations for Molecular and Phylogenetic Studies

Find optimal ROC threshold

Description

This function takes the dataframe output of the sens_spec_roc() function and finds the optimal threshold of sensitivity and specificity by minimizing the distance to the top left corner of the Receiver Operating Characteristic (ROC) curve

Usage

get_optim_roc(roc)

Arguments

roc	a dataframe produced by the sens_spec_roc() function containing the Receiver Operating Characteristic (ROC) curve

Value

vector containing optimal thresholds of sensitivity and specificity

Author(s)

Shirlee Wohl, John Giles, and Justin Lessler

See Also

Other mutrate_functions: gen_dists(), sens_spec_calc(), sens_spec_roc()

Examples

# ebola-like pathogen
R <- 1.5
mut_rate <- 1

# use simulated generation distributions
data(genDistSim)
mean_gens_pdf <- as.numeric(genDistSim[genDistSim$R == R, -(1:2)])

# get theoretical genetic distance dist based on mutation rate and generation parameters
dists <- as.data.frame(gen_dists(mut_rate = mut_rate,
                                 mean_gens_pdf = mean_gens_pdf,
                                 max_link_gens = 1))

# reshape dataframe for plotting
dists <- reshape2::melt(dists,
                        id.vars = 'dist',
                        variable.name = 'status',
                        value.name = 'prob')

# get sensitivity and specificity using the same paramters
roc_calc <- sens_spec_roc(cutoff = 1:(max(dists$dist)-1),
                          mut_rate = mut_rate,
                          mean_gens_pdf = mean_gens_pdf)

# get the optimal value for the ROC plot
optim_point <- get_optim_roc(roc_calc)

HopkinsIDD/phylosamp documentation built on May 28, 2023, 3:21 a.m.