design the function function tn= true_negative(sens, spec, prior) that calculates the probability of a negative test to be correct in matlab

Here's the implementation of the function true_negative(sens, spec, prior):

main.m
function tn = true_negative(sens, spec, prior)
% Calculate the probability of a negative test to be correct

p_pos = 1 - prior;
p_neg = prior;

tn = (spec * p_neg) / ((sens * p_pos) + (spec * p_neg));
end
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This function takes three input arguments: sens, spec, and prior. sens is the sensitivity of the test, spec is the specificity of the test, and prior is the prior probability of the event being tested (e.g. the prevalence of a disease in the population).

The function then calculates the probability of a true negative test result, given the input sensitivity, specificity, and prior probability.

The calculation involves using Bayes' theorem, where the probability of a negative test result being true negative is the specificity of the test multiplied by the prior probability of a negative test result, divided by the total probability of a negative test result (which is the sum of the probability of a true negative and a false positive).

The output of the function is the probability of a negative test to be correct (tn).