View source: R/estimate_performance_function.R
estimate_performance | R Documentation |
Estimate psychometric parameters of an identification system given the observed scores of the identified students using bootstrapping to provide uncertainty estimates.
estimate_performance( x, id.rate, nom.rate, reps, relyt = 1, pop.mean = 0, pop.sd = 1, adjust = 1, CI = 0.95 )
x |
Numeric vector of observed scores. |
id.rate |
The proportion of students who have been identified. Range (0, 1). Must
be less than or equal to |
nom.rate |
The proportion of students who have been nominated. Range (0, 1). Used to calculate the nomination cutoff. |
reps |
The number of bootstrap samples. |
relyt |
Confirmatory test reliability coefficient. Range (0,1]. Must not be exactly 0. Used in the calculation of sensitivity. Defaults to 1, in which case the reported sensitivity is relative to a universal screening system and does not include sensitivity loss from imperfect test reliability. If <1, the test reliability is included in the sensitivity calculation. |
pop.mean |
The known general population mean of the x. Defaults to 0. |
pop.sd |
The known general population standard deviation of the x. Defaults to 1. |
adjust |
Number that controls the amount of smoothing in the density estimation. Defaults to 1.0, which has been found to work well in simulation. |
CI |
The confidence limit. Must be between 0 and 1. Defaults to .95. |
This function stimates the psychometric parameters of an identification system
given the observed scores of the identified students. This function calculates statistics
using the estimate_valid
function with bootstrapping. The resulting
nomination validity (valid
) estimates are passed to the
marginal_psychometrics
function in order to calculate the implied
performance statistics.
At least 500 bootstrapped samples are suggested. This can take a while.
# generate some data set.seed(123) x <- r_identified( n = 300, test.cutoff = .9, valid = .5, nom.cutoff = .9 ) # calculate the identification rate implied by the system parameters id.rate <- marginal_psychometrics( test.cutoff = .9, valid = .5, nom.cutoff = .9 )$identification.rate # calculate the nomination rate implied by the system parameters nom.rate <- marginal_psychometrics( test.cutoff = .9, valid = .5, nom.cutoff = .9 )$nom.rate # estimate the system parameters with 200 bootstrapped samples # this uses one CPU a <- estimate_performance( x = x, id.rate = id.rate, nom.rate = nom.rate, reps = 200 ) a # plot the results for each parameter plot(a) # plot with histograms plot(a, type = "hist") # the width argument controls the x-limits plot(a, type = "hist", width = .0) # for type="density", the bandwidth is adjustable # (default is 1.2, see ?density for details) plot(a, type = "density", width = .05, adjust = .5) plot(a, type = "density", width = .05, adjust = 1.5)
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