Description Usage Arguments Value References Examples
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 |
weights |
Initial weight levels for the test procedure, see graphTest function. |
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. |
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 |
type |
What type of random numbers to use. |
nSim |
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. |
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: |
test |
In the parametric case there is more than one way to handle
subgraphs with less than the full alpha. If the parameter |
... |
For backwards compatibility. For example up to version 0.8-7
the parameters |
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 49 50 51 52 53 54 55 | ## reproduce example from Stat Med paper (Bretz et al. 2010, Table I)
## first only consider line 2 of Table I
## significance levels
weights <- c(1/2, 1/2, 0, 0)
## graph
G <- rbind(c(0, 0.5, 0.5, 0),
c(0.5, 0, 0, 0.5),
c(0, 1, 0, 0),
c(1, 0, 0, 0))
## or equivalent:
G <- simpleSuccessiveII()@m
## 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(weights, alpha=0.025, G, theta, corMat, nSim = 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), 1-eps, 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, 1-g1, 0), c(g2, 0, 0, 1-g2), 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(al, alpha=0.025, G, mean, corMat, nSim = 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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