Description Usage Arguments Value Examples
Calculates the power for model coefficients based on a supplied alpha error, beta error, covariance matrix, and desired resolution for each coefficient.
1 | vcovpower(vcovmat, detectdiff, test_alpha = 0.05, test_beta = 0.2)
|
vcovmat |
Covariance matrix to use for power calculations. Alternatively, this can be a vector taken from the diagonal of a covariance matrix. |
detectdiff |
Vector of detectable difference to use for power calculations. This should be equal to the absolute difference in each coefficient that the user would like to detect. |
test_alpha |
Vector of alpha errors to use for each power calculation. Defaults to 0.05. |
test_beta |
Vector of beta errors to use for each power calculation. Defaults to 0.20. |
Data frame with estimated minimum sample size required to estimate each parameter as well as the input values provided by the user for each calculation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (vcovmat, detectdiff, test_alpha = 0.05, test_beta = 0.2)
{
if (length(test_alpha) == 1) {
test_alpha <- rep(test_alpha, ncol(CurrentMatrix))
}
if (length(test_beta) == 1) {
test_beta <- rep(test_beta, ncol(CurrentMatrix))
}
z_one_minus_alpha <- qnorm(1 - test_alpha)
z_one_minus_beta <- qnorm(1 - test_beta)
minsamplesize <- ((z_one_minus_beta + z_one_minus_alpha) *
sqrt(diag(vcovmat))/abs(detectdiff))^2
output <- data.frame(rownames(vcovmat), detectdiff, minsamplesize,
test_alpha, test_beta)
colnames(output) <- c("Effect Name", "Difference to Detect",
"Minimum Sample Size", "Alpha", "Beta")
return(output)
}
|
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