sclr

The goal of sclr is to fit the scaled logit model from Dunning (2006) using the maximum likelihood method.

Installation

The package is not yet on CRAN. You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("khvorov45/sclr")

Model

For log likelihood, scores and second derivatives see vignette("Math", "sclr"). Documentation of the main fitting function ?sclr has details on how the model is fit.

Example

Usage is similar to other model fitting functions like lm.

library(sclr)
fit <- sclr(status ~ logHI, sclr_one_titre_data)
summary(fit)
#> Call: status ~ logHI
#> 
#> Parameter estimates
#>     lambda     beta_0 beta_logHI 
#>   0.243828  -7.763952   2.088048 
#> 
#> 95% confidence intervals
#>                 2.5 %     97.5 %
#> lambda      0.2255282  0.2621277
#> beta_0     -9.6362901 -5.8916139
#> beta_logHI  1.6458693  2.5302271

For more details see vignette("Usage", "sclr").

References

Dunning AJ (2006). “A model for immunological correlates of protection.” Statistics in Medicine, 25(9), 1485-1497. doi: 10.1002/sim.2282.