R Under development (unstable) (2024-03-03 r86036 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(TCPMOR)
> 
> # Generate simulated data dataC
> dataC <- createData()
> 
> # Fit the semi-parametric model
> spm.fit <- fitSemiParamModel(dataC)
iteration 1
iteration 2
iteration 3
iteration 4
> plot(spm.fit,ylab = "logit π",xlab = "age",shade = FALSE)
> summary(spm.fit)


Summary for non-linear components:

               df  spar knots
f(dataC$age) 5.25 55.83    34

Note this includes 1 df for the intercept.


> 
> # Find two cut-off points
> dataC <- findCutoffs(spm.fit, dataC)
> 
> # Calculate the two cut-off points after limiting sensitivity se and specificity sp
> result <- calculateCutoffs(dataC)
> cutoffs <- result$cutoffs
> dataC2 <- result$filteredData
> print(cutoffs)
[1] 32.26711 65.35335
> 
> # Discretize age variable based on the two cutoffs
> dataC2 <- discretizeAge(dataC2, cutoffs)
> 
> # Fitting logistic regression models and obtaining OR values and 95% confidence intervals
> OR_Results <- fitLogisticRegression(dataC2)
> print(round(OR_Results, 3))
               OR 2.5 % 97.5 %
(Intercept) 0.188 0.055  0.643
age_p       5.610 1.633 19.266
> 
> proc.time()
   user  system elapsed 
 228.39    3.84  232.25