Dose Response Signal Detection under Model Uncertainty

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Description

Provides functions for calculating test statistics, simulating quantiles and simulating p-values of likelihood ratio contrast tests in regression models with a lack of identifiability.

Details

Package: LRcontrast
Type: Package
Version: 1.0
Date: 2015-06-21
License: GPL-3

The main functions are:
qLRcontrast: Simulates quantiles of likelihood ratio contrast tests
sLRcontrast: Calculates test statistics of likelihood ratio contrast tests
pLRcontrast: Simulates p-values of likelihood ratio contrast tests

Author(s)

Kevin Kokot

Maintainer: Kevin Kokot <kevin.kokot@ruhr-uni-bochum.de>

References

Dette, H., Titoff, S., Volgushev, S. and Bretz, F. (2015), Dose response signal detection under model uncertainty. Biometrics. doi: 10.1111/biom.12357

See Also

DoseFinding

Examples

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# Simulate the 90%, 95% and 99% quantiles of the LR contrast tests where the specified 
# models are competing against each other.
# The size of each dose group is equal in this case.

qLRcontrast(dose = c(0, 0.05, 0.2, 0.6, 1), probs = c(0.9, 0.95, 0.99), weight 
            = c(0.2, 0.2, 0.2, 0.2, 0.2), models = c("linear", "emax", 
            "exponential", "linlog"), nsim = 10)

# Calculate the LR test statistics with the same underlying models.
# In this case the data is generated by the constant model, i.e. the 
# null hypothesis of no dose response is true.

resp <- rnorm(n = 50, mean = 0.2)
dose <- c(rep(0, 10), rep(0.05, 10), rep(0.2, 10), rep(0.6, 10), rep(1, 10))

sLRcontrast(dose = dose, resp = resp, models = c("linear", "emax", "exponential", "linlog"))
			 
# Calculate the p-values in this scenario
			 
pLRcontrast(dose = dose, resp = resp, models = c("linear", "emax", "exponential", "linlog"), 
            nsim = 10)