Description Usage Arguments Note References Examples
Compute the power of the two-sided one-sample multivariate equivalence test for population means of multivariate normal summary values with unknown population variance.
1 2 |
rho |
Vector of quantiles |
cl |
Lower boundary point of the critical region |
cu |
Upper boundary point of the critical region |
n.of.y |
Number of replicate simulations |
p |
Number of variables |
support |
Support of the truncated power function (vector of dimension 2). |
log |
If |
The power function can be truncated to support
.
http://arxiv.org/abs/1305.4283
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # power function of the two-sided F-test, to test equality of means for multivariate
# normal samples with unknown covariance matrix
n <- 10
p <- 3
cu <- 8
rho <- seq(0, 2, length = 1024)
tmp <- lapply(c(0.01, 0.5, 1, 2, 3, 4, 5, 6, 7), function(cl)
{
data.table(cl = as.factor(cl), cu=cu, rho = rho, power = tsftest.pow(rho, cl, cu, n, p))
})
tmp <- do.call('rbind', tmp)
pp <- ggplot(tmp, aes(x = rho, y = power, colour = cl, group = cl)) + geom_line() + labs(y = 'Power\n(ABC acceptance probability)')
print(pp)
|
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