R Under development (unstable) (2024-08-21 r87038 ucrt) -- "Unsuffered Consequences"
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> ## require("DoseFinding")
> 
> ## S <- diag(rep(1,4))/c(5,6,7,8)
> ## contrastModels <- Mods(emax=c(0.25,0.01),exponential=c(1.5),
> ##                        doses=seq(0,1,length=5))
> ## contMat <- optContr(contrastModels,c(0.25,0.5,0.75,1),S=S,placAdj=TRUE)$contMat
> 
> ## ## power scenario 1
> ## models <- Mods(linear=NULL,emax=c(0.25,0.01),doses=seq(0,1,length=5),
> ##                placEff=c(0.5,0.6,0.7),maxEff=0.5)
> ## power1 <- powMCT(contMat, alpha = 0.025, altModels=models, S=S, placAdj = TRUE,
> ##                  alternative = c("one.sided"),df=Inf, critV = TRUE)
> 
> ## ## power scenario 2: placebo Effect smaller for linear model.
> ## models <- Mods(linear=NULL,emax=c(0.25,0.01),
> ##                doses=seq(0,1,length=5),placEff=c(0.1,0.6,0.7),maxEff=0.5)
> ## power2 <- powMCT(contMat, alpha = 0.025, altModels=models, S=S, placAdj = TRUE,
> ##                  alternative = c("one.sided"),df=Inf, critV = TRUE)
> 
> ## ## resulting values:
> ## any(abs(power1-power2) > 0.05)
> 
> ## ## everything commented out here, for time reasons
> 
> ## ## first define the target function
> ## ## first calculate the power to detect all of the models in the candidate set
> ## fmodels <- Mods(linear = NULL, emax = c(25),                            
> ##                 logistic = c(50, 10.88111), exponential=c(85),          
> ##                 betaMod=matrix(c(0.33,2.31,1.39,1.39), byrow=TRUE, nrow=2),
> ##                 doses = c(0,10,25,50,100,150), placEff=0, maxEff=0.4,
> ##                 addArgs = list(scal=200))
> ## ## contrast matrix to use
> ## contMat <- optContr(fmodels, w=1)
> ## ## this function calculates the power under each model and then returns
> ## ## the average power under all models
> ## tFunc <- function(n){
> ##   powVals <- powMCT(contMat, altModels=fmodels, n=n, sigma = 1,
> ##                     alpha=0.05)
> ##   mean(powVals)
> ## }
> 
> ## ## assume we want to achieve 80\% average power over the selected shapes
> ## ## and want to use a balanced allocations
> ## sSize <- sampSize(upperN = 80, targFunc = tFunc, target=0.8, 
> ##                   alRatio = rep(1,6), verbose = TRUE)
> 
> ## ## Now the same using the convenience sampSizeMCT function 
> ## sampSizeMCT(upperN=80, contMat = contMat, sigma = 1, altModels=fmodels,
> ##             power = 0.8, alRatio = rep(1, 6), alpha = 0.05)
> ## ## Alternatively one can also specify an S matrix
> ## ## covariance matrix in one observation (6 total observation result in a
> ## ## variance of 1 in each group)
> ## S <- 6*diag(6)
> ## ## this uses df = Inf, hence a slightly smaller sample size results
> ## sampSizeMCT(upperN=500, contMat = contMat, S=S, altModels=fmodels,
> ##             power = 0.8, alRatio = rep(1, 6), alpha = 0.05, Ntype = "total")
> 
> 
> ## ## targN examples
> ## ## first calculate the power to detect all of the models in the candidate set
> ## fmodels <- Mods(linear = NULL, emax = c(25),                            
> ##                 logistic = c(50, 10.88111), exponential=c(85),          
> ##                 betaMod=matrix(c(0.33,2.31,1.39,1.39), byrow=TRUE, nrow=2),
> ##                 doses = c(0,10,25,50,100,150), placEff=0, maxEff=0.4,
> ##                 addArgs = list(scal=200))
> ## ## corresponding contrast matrix
> ## contMat <- optContr(fmodels, w=1)
> ## ## define target function
> ## tFunc <- function(n){
> ##   powMCT(contMat, altModels=fmodels, n=n, sigma = 1, alpha=0.05)
> ## }
> ## powVsN <- targN(upperN = 100, lowerN = 10, step = 10, tFunc,
> ##                 alRatio = rep(1, 6))
> ## plot(powVsN)
> 
> ## ## the same can be achieved using the convenience powN function
> ## ## without the need to specify a target function
> ## res <- powN(upperN = 100, lowerN=10, step = 10, contMat = contMat,
> ##             sigma = 1, altModels = fmodels, alpha = 0.05, alRatio = rep(1, 6))
> 
> ## ## the same but with S (but using df=Inf)
> ## S <- 6*diag(6)
> ## res1 <- powN(upperN=80*6, lowerN=60, step=60, contMat = contMat,
> ##              S=S, altModels = fmodels, alRatio = rep(1, 6),
> ##              alpha = 0.05, sumFct = "mean", Ntype = "total")
> 
> ## ## different allocation ratio
> ## res2 <- powN(upperN=80, lowerN=10, step=10, contMat = contMat,
> ##             sigma = 1, altModels=fmodels, alRatio = c(1, rep(0.5,4), 1),
> ##             alpha = 0.05, sumFct = "mean")
> 
> ## ## powMCT(contMat, n = c(100,rep(50,4),100), sigma = 1, altModels = fmodels,
> ## ##        alpha = 0.05)
> 
> ## ## iterating the total sample size
> ## res3 <- powN(upperN=600, lowerN=100, step=25, contMat = contMat,
> ##              sigma = 1, altModels=fmodels, alRatio = rep(1, 6),
> ##              alpha = 0.05, sumFct = "mean", Ntype = "total")
> 
> ## ## powMCT(contMat, n = c(50,rep(50,4),50), sigma = 1, altModels = fmodels,
> ## ##        alpha = 0.05)
> 
> ## ## iterating the total sample size, with unbalanced allocations
> ## res4 <- powN(upperN=600, lowerN=100, step=25, contMat = contMat,
> ##              sigma = 1, altModels=fmodels, alRatio = c(1, rep(0.5,4), 1),
> ##              alpha = 0.05, sumFct = "mean", Ntype = "total")
> 
> ## ## powMCT(contMat, n = c(100,rep(50,4),100), sigma = 1, altModels = fmodels,
> ## ##        alpha = 0.05)
> 
> 
> proc.time()
   user  system elapsed 
   0.18    0.09    0.26