sLRcontrast: Calculate LR test statistics

In LRcontrast: Dose Response Signal Detection under Model Uncertainty

Description

The function calculates LR test statistics for the competing models which are defined by the argument 'models' (see drmodels).

Usage

sLRcontrast(dose, resp, models, off = 0.01 * max(dose), scal = 1.2 * max(dose))

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≥ max(dose)' (see drmodels).

Value

A matrix containing the LR test statistic for one model in each row. The last row contains the value of the maximum statistic.

Author(s)

Kevin Kokot

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

## Simulate the power of the LR contrast test

# five dose levels will be used
doselvl <- c(0, 0.05, 0.2, 0.6, 1)
dose <- c(rep(0, 10), rep(0.05, 10), rep(0.2, 10), rep(0.6, 10), rep(1, 10))

# each row of 'resp' will contain one sample of size 50
resp <- matrix(nrow=100, ncol=50)

# the linear model will serve as the data generating model
linear <- function(dose, e0, delta){e0 + delta * dose} 

# now 'resp' is generated:
for (i in 1:100)
     {for(j in 1:5)
        {resp[i,(j*10-9):(10*j)]<-rnorm(10, mean=linear(doselvl[j], 0.2, 0.6), sd=1.478)}}  

# the simulated 95% quantile in this case:
quantile <- 4.349362

# now the power is simulated
count <- 0
for (i in 1:100)
     {if(sLRcontrast(dose = dose, resp = resp[i,], models = c("linear", "emax", 
                     "exponential", "linlog"))[5] > quantile)
         {count <- count + 1}}

#power:
count/100

LRcontrast documentation built on May 2, 2019, 5:10 a.m.