pLRcontrast: Simulate p-values for LR contrast tests in regression models with a lack of identifiability.

Description

The function simulates p-values for LR contrast tests. The competing models can be specified by the argument 'models' (see drmodels) and by 'nsim' you can specify how many simulations should be done. It is recommended to do about 10000 but notice that the simulation can take some time. If the parameter 'info' is set 'TRUE', information of the progress will be shown.

Usage

pLRcontrast(dose, resp, models, off = 0.01 * max(dose), scal = 1.2 * max(dose),  nsim = 1000, info = TRUE)

Arguments

dose, resp

Vectors of equal length specifying dose and response values.

models

A subvector of c("linear", "emax", "exponential", "linlog", "sigEmax", "quadratic", "betaMod", "logistic") (see drmodels) which specifies the competing models.

off

Positive and fixed offset parameter in the 'linlog' model (see drmodels).

scal

Positive and fixed dose scaling parameter in the 'betaMod' with '$scal\ge$ max(dose)' (see drmodels).

nsim

A positive integer which specifies the number of simulations that should be done.

info

Logical value which indicates whether progress information should be shown.

Value

A matrix containing the unadjusted and adjusted p-value for one model in each row.

References

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

Examples

Run this code

# 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))

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