asypow.sig: Asymptotic Significance

In asypow: Calculate Power Utilizing Asymptotic Likelihood Ratio Methods

Description

Calculates the significance level of a test via likelihood ratio methods.

Usage

     asypow.sig(asypow.obj, sample.size, power)

Arguments

asypow.obj	The object returned from asypow.noncent.
sample.size	The sample size of the test.
power	The power of the test.

Value

Returns the significance level of the test.

References

Cox, D.R. and Hinkley, D.V. (1974). Theoretical Statistics Chapman and Hall, London.

See Also

asypow.noncent, asypow.n, asypow.power

Examples

# Single Group Binomial Example:
#    Consider testing the null hypothesis that the binomial
#    probability is p = .2 when the actual probability is .4.
#    What significance level corresponding to a sample
#    size of 47 and power of .8?
# Step 1: Find the information matrix
info.binom <- info.binomial.kgroup(.4)
# Step 2: Create the constraints matrix
constraints <- c(1, 1, .2)
# Step 3: Find the noncentrality parameter and
#         degrees of freedom for the test
binom.object <- asypow.noncent(.4, info.binom, constraints)
# Step 4: Compute the power of a test with
#         sample size of 47 and a significance level 0.05
sig.binom <- asypow.sig(binom.object, 47, 0.8)
print(sig.binom)

asypow documentation built on May 2, 2019, 2:37 a.m.