Calculates the Negative likelihood ratio

This function calculate the Negative Likelihood Ratio estimator, their standard error estimated and a confidence interval in a traverse or Cross-sectional study.

Usage

ebdt_nlr(s1, r1, s0, r0, conflev = 0.95, digits = 3)

Arguments

s1

Non-negative numeric. TP - True positive (cases correctly classified as +).

r1

Non-negative numeric. FP - False positives (controls classified as +).

s0

Non-negative numeric. FN - False negatives (cases classified as -).

r0

Non-negative numeric. TN - True negatives (controls classified as -).

conflev

Confidence level (0,1). Default 0.95.

digits

Integer. Number of decimal places. Default 3.

Value

list with: - LinfGNLRn: lower limit of the IC GN for LR− - LsupGNLRn: upper limit of the IC GN for LR−

Details

Evaluating of Binary Diagnostic Test (EBDT)

This function calculates the Negative likelihood ratio (LR-) with Simel and Gart - Nam ICs

Requires a `gn_nlr()` function in the environment and `rootall()` function in the environment (the robust version reviewed above is suitable).

References

Agresti, A., (2002). Categorical Data Analysis. John Wiley and Sons, New York.

Gart, J.J., Nam J., (1988). Aproximate interval estimation of the ratio of binomial parameters: a review and corrections for skewness. Biometrics, 44: 323 – 338.

Montero-Alonso, M.Á.(2010). Intervalos de confianza y contrastes de hipótesis para parámetros de tests diagnósticos binarios, http://hdl.handle.net/10481/4879

Pepe, M. S. (2003). The statistical evaluation of medical tests for classification and prediction. Oxford University Press.

Zhou, X.-H., Obuchowski, N. A., y McClish, D. K. (2011). Statistical Methods in Diagnostic Medicine (2.ª ed.). John Wiley & Sons.

Examples

gn_nlr(40, 5, 10, 45)
#> $LinfGNLRn
#> [1] 0.1242585
#> 
#> $LsupGNLRn
#> [1] 0.3715251
#>