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.
Install the CRAN version with
install.packages("sclr")
Or the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("khvorov45/sclr")
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.
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.
Dunning AJ (2006). “A model for immunological correlates of protection.” Statistics in Medicine, 25(9), 1485-1497. doi: 10.1002/sim.2282.
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