qLRcontrast: Simulate quantiles of LR contrast tests in regression models with a lack of identifiability.

Description

The function simulates the desired quantiles of the distribution of LR contrast tests. The competing models can be specified by the argument 'models' (see drmodels). The argument 'dose' is the vector of the different unique dose-levels and 'weight' specifies the weight proportion of each dose group. By 'nsim' you can specify how many simulation 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

qLRcontrast(dose, probs, models, weight = rep(1/length(dose), length(dose)),  off = 0.01 * max(dose), scal = 1.2 * max(dose), nsim = 10000, info = TRUE)

Arguments

dose

A vector which contains the unique dose-levels.

probs

A vector of probabilities with values in $[0,1]$ specifying which quantiles should be calculated.

models

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

weight

A vector with values in $[0,1]$ with 'sum(weight) = 1'. The i-th entry specifies the weight proportion of the i-th dose group.

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 different quantiles for one model in each row. The last row contains the quantiles of the maximum statistic.

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

# 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)