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PLindleyROC

The goal of PLindleyROC is to evaluate the Receiver Operating Characteristic (ROC) for Power Lindley Distribution. Additionally, The performace asssesments can be performed associated with the Bi-Power Lindley ROC model.

Installation

You can install the development version of PLindleyROC via the following code:

# install.packages("devtools")
devtools::install_github("ErtanSU/PLindleyROC")

Example

This is a basic example which shows you how to solve a common problem:

library(PLindleyROC)
dPLD(c(1,2,3,4,5,200),alpha=3,beta=2)
#> [1]  1.082682e+00  1.620507e-05  3.560890e-21  1.070039e-52 3.363180e-105
#> [6]  0.000000e+00

library(PLindleyROC)
pPLD(c(.5,1,2,3,4),alpha=3,beta=2)
#> [1] 0.1562992 0.7744412 0.9999993 1.0000000 1.0000000

library(PLindleyROC)
qPLD(c(.9971,0.5,0.3),alpha=3,beta=2)
#> [1] 1.5220612 0.7868721 0.6362570

library(PLindleyROC)
rPLD(10,alpha=3,beta=2)
#>  [1] 0.6572033 0.7754573 0.6550335 0.9569136 1.1122406 0.6148588 0.8642285
#>  [8] 0.4055046 0.6852735 0.9968817

library(PLindleyROC)
r.pl_auc(x=c(1,2,2,3,1),y=c(1,3,2,4,2,3),true_param=c(alpha1=1,beta1=1,alpha2=1,beta2=1),method=c("TRUE"))
#> [1] 0.5

library(PLindleyROC)
r.pl_index(x=c(1,2,2,3,1),y=c(1,3,2,4,2,3),init_param=c(1,1,1,1),init_index=1,method=c("MLE"))
#>    Cut-off Point Sensitivity Specificity 1-Specificity
#> J       2.257651   0.5843951   0.7345488     0.2654512
#> ER      2.128638   0.6365278   0.6790223     0.3209777
#> CZ      2.155423   0.6258267   0.6909883     0.3090117
#> EC      2.049502   0.6676484   0.6424604     0.3575396

library(PLindleyROC)
x=c(1,2,2,3,1)
y=c(1,3,2,4,2,3)
r.pl_graph(x,y,init_param=c(1,1,1,1),empirical=TRUE,method=c("MLE"))

Corresponding Author

Department of Statistics, Faculty of Science, Selcuk University, 42250, Konya, Turkey

Email:https://www.researchgate.net/profile/Ertan-Akgenc

References

Akgenç, E., and Kuş, C., 2023, ROC Curve Analysis for the Measurements Distributed Power-Lindley Distribution, 2nd International E-Conference On Mathematical And Statistical Sciences: A Selçuk Meeting (ICOMSS-2023), Konya, 25.

Attwood, K., Hou, S., and Hutson, A., 2022, Application of the skew exponential power distribution to ROC curves, Journal of Applied Statistics, 1-16.

Ghitany M., Al-Mutairi D. K., Balakrishnan N., and Al-Enezi L., 2013, Power lindley distribution and associated inference, Computational Statistics & Data Analysis, 64,20–33.

Liu, X., 2012, Classification accuracy and cut point selection, Statistics in medicine, 31(23), 2676-2686.

Nahm, F. S., 2022, Receiver operating characteristic curve: overview and practical use for clinicians, Korean journal of anesthesiology, 75(1), 25-36.

Perkins, N. J., and Schisterman, E. F., 2006, The inconsistency of “optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve, American journal of epidemiology, 163(7), 670-675.

Pundir, S. and Amala, R., 2014, Evaluation of area under the constant shape bi-weibull roc curve, Journal of Modern Applied Statistical Methods, 13(1),1-20.

Youden, W. J., 1950, Index for rating diagnostic tests, Cancer, 3(1), 32-35.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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