View source: R/sens_spec_roc.R
| sens_spec_roc | R Documentation | 
This is a wrapper function that takes output from the 
sens_spec_calc() function and constructs values for the
Receiver Operating Characteristic (ROC) curve
sens_spec_roc(
  cutoff,
  mut_rate,
  mean_gens_pdf,
  max_link_gens = 1,
  max_gens = NULL,
  max_dist = NULL
)
| cutoff | the maximum genetic distance at which to consider cases linked | 
| mut_rate | mean number of mutations per generation, assumed to be Poisson distributed | 
| mean_gens_pdf | the density distribution of the mean number of generations between cases; the index of this vector is assumed to be the discrete distance between cases | 
| max_link_gens | the maximum generations of separation for linked pairs | 
| max_gens | the maximum number of generations to consider, if  | 
| max_dist | the maximum distance to calculate, if  | 
data frame with cutoff, sensitivity, and 1-specificity
Shirlee Wohl and Justin Lessler
Other mutrate_functions: 
gen_dists(),
get_optim_roc(),
sens_spec_calc()
# ebola-like pathogen
R <- 1.5
mut_rate <- 1
# use simulated generation distributions
data('genDistSim')
mean_gens_pdf <- as.numeric(genDistSim[genDistSim$R == R, -(1:2)])
# get theoretical genetic distance dist based on mutation rate and generation parameters
dists <- as.data.frame(gen_dists(mut_rate = mut_rate,
                                 mean_gens_pdf = mean_gens_pdf,
                                 max_link_gens = 1))
dists <- reshape2::melt(dists,
                        id.vars = 'dist',
                        variable.name = 'status',
                        value.name = 'prob')
# get sensitivity and specificity using the same paramters
roc_calc <- sens_spec_roc(cutoff = 1:(max(dists$dist)-1),
                          mut_rate = mut_rate,
                          mean_gens_pdf = mean_gens_pdf)
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