poolpower_t: Visualize T-test Power for Pooling Design
In pooling: Fit Poolwise Regression Models

Description Usage Arguments Value Examples

Useful for assessing efficiency gains that might be achieved with a pooling design.

poolpower_t(g = c(1, 3, 10), d = NULL, mu1 = NULL, mu2 = NULL,
  sigsq = NULL, sigsq1 = sigsq, sigsq2 = sigsq, sigsq_p = 0,
  sigsq_m = 0, multiplicative = FALSE, alpha = 0.05, beta = 0.2,
  assay_cost = 100, other_costs = 0, labels = TRUE)

`g`	Numeric vector of pool sizes to include.
`d`	Numeric value specifying true difference in group means.
`mu1, mu2`	Numeric value specifying group means. Required if `multiplicative = TRUE`.
`sigsq`	Numeric value specifying the variance of observations.
`sigsq1, sigsq2`	Numeric value specifying the variance of observations for each group.
`sigsq_p`	Numeric value specifying the variance of processing errors.
`sigsq_m`	Numeric value specifying the variance of measurement errors.
`multiplicative`	Logical value for whether to assume multiplicative rather than additive errors.
`alpha`	Numeric value specifying type-1 error rate.
`beta`	Numeric value specifying type-2 error rate.
`assay_cost`	Numeric value specifying cost of each assay.
`other_costs`	Numeric value specifying other per-subject costs.
`labels`	Logical value.

Plot of power vs. total costs generated by ggplot.

# Plot power vs. total study costs for d = 0.25, sigsq = 1, and costs of $100
# per assay and $0 in other per-subject costs.
poolpower_t(d = 0.5, sigsq = 1, assay_cost = 100, other_costs = 0)

# Repeat but with $10 in per-subject costs.
poolpower_t(d = 0.5, sigsq = 1, assay_cost = 100, other_costs = 10)

# Back to no per-subject costs, but with processing and measurement error
poolpower_t(d = 0.5, sigsq = 1, sigsq_p = 0.2, sigsq_m = 0.1,
            assay_cost = 100, other_costs = 0)