powCalc {MCPMod} | R Documentation |
Calculate the power for the multiple contrast test
Description
Given the optimal contrasts, the sample size and a certain ‘alternative’ (i.e. a mean vector and sigma), the
function calculates the power to detect this alternative. See Pinheiro et al. (2006) for details.
The function is the building block for the functions powerMM
, sampSize
and LP
.
Numerical integration routines from the mvtnorm
package are used to calculate the underlying
multivariate integrals.
Usage
powCalc(cMat, n, alpha = 0.025, delta = NULL, mu = NULL,
sigma = NULL, cVal = NULL, corMat = NULL,
twoSide = FALSE, control = mvtnorm.control())
Arguments
cMat |
Matrix with the contrasts in the columns |
n |
Numeric vector of sample sizes per group. In case just one number is specified, it is assumed that all group sample sizes are equal to this number |
alpha |
Level of significance (defaults to 0.025) |
delta |
Non-centrality vector of the distribution of the test statistic under the alternative. |
mu |
Mean vector under the alternative. The function then calculates the
non-centrality vector itself. Ignored if |
sigma |
Expected standard deviation of the response. Only necessary if
the non-centrality vector is to be calculated by the function (i.e.
if |
cVal |
Optional numeric vector giving the critical value, if specified
the argument |
corMat |
An optional matrix giving the correlations of the contrasts
specified in |
twoSide |
Logical indicating whether a two sided or a one sided test should be performed (defaults to one-sided) |
control |
A list of options for the |
Value
The function returns the power value.
References
Pinheiro, J. C., Bornkamp, B. and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple comparisons and modeling procedures, Journal of Biopharmaceutical Statistics, 16, 639–656
See Also
Examples
doses <- c(0,10,25,50,100,150)
models <- list(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))
# calculate optimal contrasts and critical value
plMM <- planMM(models, doses, 50, scal = 200, alpha = 0.05)
# calculate mean vectors
compMod <- fullMod(models, doses, base = 0, maxEff = 0.4, scal = 200)
muMat <- modelMeans(compMod, doses, FALSE, scal = 200)
# calculate power to detect mean vectors
# Power for linear model
powCalc(plMM$contMat, 50, mu = muMat[,1], sigma = 1, cVal = plMM$critVal)
# Power for emax model
powCalc(plMM$contMat, 50, mu = muMat[,2], sigma = 1, cVal = plMM$critVal)
# Power for logistic model
powCalc(plMM$contMat, 50, mu = muMat[,3], sigma = 1, cVal = plMM$critVal)
# compare with JBS 16, p. 650