asbio (version 1.5-5)

MC.test: Monte Carlo hypothesis testing for two samples.

Description

MC.test calculates a permutation of test statistics from an pooled variance t-test. It compares this distribution to an initial test statistic calculated using non-permuted data to derive a P-value.

Usage

MC.test(Y,X, perm = 1000, alternative = "not.equal", var.equal = TRUE, paired = FALSE)

Arguments

Y

Response data.

X

Categorical explanatory variable.

perm

Number of iterations.

var.equal

Logical: Should equal variances be assumed?

paired

Logical: Are sample paired?

alternative

Alternative hypothesis. One of three options: "less","greater", or "not.equal". These provide lower-tail, upper-tail, and two-tailed tests.

Value

Returns a list with the following items:

observed.test.statistic

t-statistic calculated from non-permuted (original)data.

no_of_permutations_exceeding_observed_value

The number of times a Monte Carlo derived test statistic was more extreme than the initial observed test statistic.

p.value

Empirical P-value

alternative

The alternative hypothesis

Details

The method follows the description of Manly (1998) for a two-sample test. Upper and lower tailed tests are performed by finding the portion of the distribution greater than or equal to the observed t test statistic (upper-tailed) or less than or equal to the observed test statistic (lower-tailed). A two tailed test is performed by multiplying the portion of the null distribution above the absolute value of the observed test statistic by two. Results from the test will be similar to oneway_test from the library coin since it is based on an equivalent test statistic. The function oneway_test allows additional options including blocking.

References

Manly, B. F. J. (1997) Randomization and Monte Carlo Methods in Biology, 2nd edition. Chapman and Hall, London.

See Also

t.test

Examples

# NOT RUN {
Y<-c(runif(100,1,3),runif(100,1.2,3.2))
X<-factor(c(rep(1,100),rep(2,100)))
MC.test(Y,X,alternative="less")
# }