Description Usage Arguments Value References See Also Examples
Calculates the sample size required to obtain the desired power for a test via likelihood ratio methods.
1 |
asypow.obj |
The object returned from asypow.noncent. |
power |
The desired power of the test. |
significance |
The desired significance level of the test. |
Returns the sample size needed to achieve specified power at the specified significance level.
Cox, D.R. and Hinkley, D.V. (1974). Theoretical Statistics Chapman and Hall, London.
asypow.noncent
,
asypow.sig
,
asypow.power
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # Three Sample Poisson Example :
# Three independent Poisson processes produce events at
# mean rates of 1, 2 and 3 per day. For how many days
# must the processes be observed to have an 80% chance
# of detecting that the means are different at an
# overall significance level of 0.05?
# Step 1 : Find the information matrix
pois.mean <- c(1,2,3)
info.pois <- info.poisson.kgroup(pois.mean, group.size=3)
# Step 2: Create the constraints matrix
constraints <- matrix(c(2,1,2,2,2,3),ncol=3,byrow=TRUE)
# Step 3: Find the noncentrality parameter and
# degrees of freedom for the test
poisson.object <- asypow.noncent(pois.mean, info.pois, constraints)
# Step 4: Compute sample size needed for
# 0.8 power with significance level 0.05
n.pois <- asypow.n(poisson.object, 0.8, 0.05)
# Step 5: Divide the sample size by 3 (the number of processes)
# to get the number of days required.
n.days <- n.pois/3
print(n.days)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.