Scaled Logistic Regression.

sclr

The goal of sclr is to fit the scaled logit model from Dunning (2006) using the maximum likelihood method. The package website contains all documentation, vignettes and version history.

Installation

Install the CRAN version with

install.packages("sclr")

Or the development version from GitHub with:

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

Model

The model is logistic regression with an added parameter for the top asymptote. For model specification, log likelihood, scores and second derivatives see the math vignette. 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, one_titre_data) # included simulated data
summary(fit)
#> Call: status ~ logHI
#> 
#> Parameter estimates
#>       theta      beta_0  beta_logHI 
#> -0.03497876 -5.42535734  2.14877741 
#> 
#> 95% confidence intervals
#>                 2.5 %      97.5 %
#> theta      -0.1350572  0.06509969
#> beta_0     -6.4417802 -4.40893449
#> beta_logHI  1.8146909  2.48286390
#> 
#> Log likelihood: -2469.765

For more details see the usage vignette.

References

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