Calculate multiplicity adjusted p-values for a maximum contrast test corresponding to a set of contrasts and given a
set of observed test statistics. This function is exported as it may be a useful building block and used in more
complex testing situations that are not covered by MCTtest(). Most users probably don't need to use this
function.
Usage
MCTpval(
contMat,
corMat,
df,
tStat,
alternative = c("one.sided", "two.sided"),
control = mvtnorm.control()
)Arguments
- contMat
Contrast matrix to use. The individual contrasts should be saved in the columns of the matrix
- corMat
Correlation matrix of contrasts
- df
Degrees of freedom to use for calculation.
- tStat
Vector of contrast test statistics
- alternative
Character determining the alternative for the multiple contrast trend test.
- control
A list specifying additional control parameters for the qmvt and pmvt calls in the code, see also
mvtnorm.control()for details.
References
Pinheiro J, Bornkamp B, Bretz F (2006). “Design and Analysis of Dose Finding Studies Combining Multiple Comparisons and Modeling Procedures.” Journal of Biopharmaceutical Statistics, 16, 639-656. doi:10.1080/10543400600860428 .
Examples
data(biom)
## define shapes for which to calculate optimal contrasts
modlist <- Mods(emax = 0.05, linear = NULL, logistic = c(0.5, 0.1),
linInt = c(0, 1, 1, 1), doses = c(0, 0.05, 0.2, 0.6, 1))
contMat <- optContr(modlist, w=1)$contMat
## calculate inputs needed for MCTpval
fit <- lm(resp~factor(dose)-1, data=biom)
est <- coef(fit)
vc <- vcov(fit)
ct <- as.vector(est %*% contMat)
covMat <- t(contMat) %*% vc %*% contMat
den <- sqrt(diag(covMat))
tStat <- ct/den
corMat <- cov2cor(t(contMat) %*% vc %*% contMat)
MCTpval(contMat, corMat, df=100-5, tStat)
#> [1] 0.001566885 0.004235790 0.007480728 0.001206962
## compare to
test <- MCTtest(dose, resp, biom, models=modlist)
attr(test$tStat, "pVal")
#> [1] 0.001572649 0.004888976 0.007579961 0.001275986