Given the distribution under the alternative (assumed to be multivariate normal), this function calculates the power to reject at least one hypothesis, the local power for the hypotheses as well as the expected number of rejections.
1 2 3 4 
weights 
Initial weight levels for the test procedure (see graphTest
function). Alternatively a 
alpha 
Overall alpha level of the procedure, see graphTest function.
(For entangled graphs 
G 
Matrix determining the graph underlying the test procedure. Note
that the diagonal need to contain only 0s, while the rows need to sum to 1.
When multiple graphs should be used this needs to be a list containing the
different graphs as elements. Alternatively a 
mean 
Mean under the alternative 
corr.sim 
Covariance matrix under the alternative. 
corr.test 
Correlation matrix that should be used for the parametric test.
If 
n.sim 
Monte Carlo sample size. If type = "quasirandom" this number is rounded up to the next power of 2, e.g. 1000 is rounded up to 1024=2^10 and at least 1024. 
type 
What type of random numbers to use. 
f 
List of user defined power functions (or just a single power
function). If one is interested in the power to reject hypotheses 1 and 3
one could specify: 
upscale 
Logical. If 
graph 
A graph of class 
... 
For backwards compatibility. For example up to version 0.87
the parameters 
test 
In the parametric case there is more than one way to handle
subgraphs with less than the full alpha. If the parameter 
A list containg three elements
LocalPower
A numeric giving the local powers for the hypotheses
ExpRejections
The expected number of rejections
PowAtlst1
The power to reject at least one hypothesis
Bretz, F., Maurer, W., Brannath, W. and Posch, M. (2009) A graphical approach to sequentially rejective multiple test procedures. Statistics in Medicine, 28, 586–604
Bretz, F., Maurer, W. and Hommel, G. (2010) Test and power considerations for multiple endpoint analyses using sequentially rejective graphical procedures, to appear in Statistics in Medicine
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  ## reproduce example from Stat Med paper (Bretz et al. 2010, Table I)
## first only consider line 2 of Table I
## significance levels
graph < simpleSuccessiveII()
## alternative (mvn distribution)
corMat < rbind(c(1, 0.5, 0.5, 0.5/2),
c(0.5,1,0.5/2,0.5),
c(0.5,0.5/2,1,0.5),
c(0.5/2,0.5,0.5,1))
theta < c(3, 0, 0, 0)
calcPower(graph=graph, alpha=0.025, mean=theta, corr.sim=corMat, n.sim= 100000)
## now reproduce all 14 simulation scenarios
## different graphs
weights1 < c(rep(1/2, 12), 1, 1)
weights2 < c(rep(1/2, 12), 0, 0)
eps < 0.01
gam1 < c(rep(0.5, 10), 1eps, 0, 0, 0)
gam2 < gam1
## different multivariate normal alternatives
rho < c(rep(0.5, 8), 0, 0.99, rep(0.5,4))
th1 < c(0, 3, 3, 3, 2, 1, rep(3, 7), 0)
th2 < c(rep(0, 6), 3, 3, 3, 3, 0, 0, 0, 3)
th3 < c(0, 0, 3, 3, 3, 3, 0, 2, 2, 2, 3, 3, 3, 3)
th4 < c(0,0,0,3,3,3,0,2,2,2,0,0,0,0)
## function that calculates power values for one scenario
simfunc < function(nSim, a1, a2, g1, g2, rh, t1, t2, t3, t4, Gr){
al < c(a1, a2, 0, 0)
G < rbind(c(0, g1, 1g1, 0), c(g2, 0, 0, 1g2), c(0, 1, 0, 0), c(1, 0, 0, 0))
corMat < rbind(c(1, 0.5, rh, rh/2), c(0.5,1,rh/2,rh), c(rh,rh/2,1,0.5), c(rh/2,rh,0.5,1))
mean < c(t1, t2, t3, t4)
calcPower(weights=al, alpha=0.025, G=G, mean=mean, corr.sim=corMat, n.sim = nSim)
}
## calculate power for all 14 scenarios
outList < list()
for(i in 1:14){
outList[[i]] < simfunc(10000, weights1[i], weights2[i],
gam1[i], gam2[i], rho[i], th1[i], th2[i], th3[i], th4[i])
}
## summarize data as in Stat Med paper Table I
atlst1 < as.numeric(lapply(outList, function(x) x$PowAtlst1))
locpow < do.call("rbind", lapply(outList, function(x) x$LocalPower))
round(cbind(atlst1, locpow), 5)

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