Description Usage Arguments Value Examples
View source: R/poolcushion_t.R
Useful for choosing a sample size such that power will be adequate even if the processing errors are larger than anticipated.
1 2 3 4 |
g |
Numeric value specifying the pool size. |
n |
Numeric value specifying the number of assays per group. If
unspecified, function figures out |
d |
Numeric value specifying true difference in group means. |
mu1, mu2 |
Numeric value specifying group means. Required if
|
sigsq |
Numeric value specifying the variance of observations. |
sigsq1, sigsq2 |
Numeric value specifying the variance of observations for each group. |
sigsq_p_predicted |
Numeric value specifying predicted processing error
variance. Used to calculate |
sigsq_p_range |
Numeric vector specifying range of processing error variances to consider. |
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. Only used if
|
labels |
Logical value. |
Plot generated by ggplot
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # Determine optimal pool size and number of assays to detect a difference in
# group means of 0.5, with a common variance of 1, processing errors with
# variance of 0.1, and measurement errors with variance of 0.2. Assume costs
# of $100 per assay and $10 per subject.
poolcost_t(
g = 1: 10,
d = 0.5,
sigsq = 1,
sigsq_p = 0.1,
sigsq_m = 0.2,
assay_cost = 100,
other_costs = 10
)
# Visualize how power of the study will be affected if the true processing
# error variance is not exactly 0.1.
poolcushion_t(
g = 7,
n = 29,
d = 0.5,
sigsq = 1,
sigsq_p_predicted = 0.1,
sigsq_m = 0.2
)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.