| goreTest {PMCMRplus} | R Documentation |
Gore Test
Description
Performs Gore's test. The null hypothesis
H_0: \theta_i = \theta_j~~(i \ne j) is tested against the
alternative H_{\mathrm{A}}: \theta_i \ne \theta_j, with at least
one inequality beeing strict.
Usage
goreTest(y, groups, blocks)
Arguments
y |
a numeric vector of data values. |
groups |
a vector or factor object giving the group for the
corresponding elements of |
blocks |
a vector or factor object giving the group for the
corresponding elements of |
Details
The function has implemented Gore's test for testing main effects in unbalanced CRB designs, i.e. there are one ore more observations per cell. The statistic is assymptotically chi-squared distributed.
Value
A list with class "htest" containing the following components:
- method
a character string indicating what type of test was performed.
- data.name
a character string giving the name(s) of the data.
- statistic
the estimated quantile of the test statistic.
- p.value
the p-value for the test.
- parameter
the parameters of the test statistic, if any.
- alternative
a character string describing the alternative hypothesis.
- estimates
the estimates, if any.
- null.value
the estimate under the null hypothesis, if any.
References
Gore, A. P. (1975) Some nonparametric tests and selection procedures for main effects in two-way layouts. Ann. Inst. Stat. Math. 27, 487–500.
See Also
friedmanTest, skillingsMackTest,
durbinTest
Examples
## Crop Yield of 3 varieties on two
## soil classes
X <-c("130,A,Light
115,A,Light
123,A,Light
142,A,Light
117,A,Heavy
125,A,Heavy
139,A,Heavy
108,B,Light
114,B,Light
124,B,Light
106,B,Light
91,B,Heavy
111,B,Heavy
110,B,Heavy
155,C,Light
146,C,Light
151,C,Light
165,C,Light
97,C,Heavy
108,C,Heavy")
con <- textConnection(X)
x <- read.table(con, header=FALSE, sep=",")
close(con)
colnames(x) <- c("Yield", "Variety", "SoilType")
goreTest(y = x$Yield, groups = x$Variety, blocks = x$SoilType)