Class of the Permutation Tests (in C).

Plot, print and extract permutation tests. Objects of class 'krandtest' are lists.

Usage

as.krandtest(sim, obs, alter = "greater", call = match.call(),
  names = colnames(sim), p.adjust.method = "none", output = c("light", "full"))

# S3 method for krandtest
plot(x, mfrow = NULL, nclass = 10, main.title = x$names, ...)
# S3 method for krandtest
print(x, ...)
# S3 method for krandtest
[(x, i)
# S3 method for krandtest
[[(x, i)

Arguments

sim: a matrix or data.frame of simulated values (repetitions as rows, number of tests as columns
obs: a numeric vector of observed values for each test
alter: a vector of character specifying the alternative hypothesis for each test. Each element must be one of "greater" (default), "less" or "two-sided". The length must be equal to the length of the vector obs, values are recycled if shorter.
call: a call order
names: a vector of names for tests
p.adjust.method: a string indicating a method for multiple adjustment, see p.adjust.methods for possible choices.
output: a character string specifying if all simulations should be stored ("full"). This was the default until ade4 1.7-5. Now, by default ("light"), only the distribution of simulated values is stored in element plot as produced by the hist function.
x: an object of class 'krandtest'
mfrow: a vector of the form 'c(nr,nc)', otherwise computed by as special own function n2mfrow
nclass: a number of intervals for the histogram. Ignored if object output is "light"
main.title: a string of character for the main title
...: further arguments passed to or from other methods
i: numeric indices specifying elements to extract

Value

plot.krandtest draws the p simulated values histograms and the position of the observed value. [.krandtest returns a krandtest object and [[.krandtest returns a randtest object.

Author

Daniel Chessel and Stéphane Dray stephane.dray@univ-lyon1.fr

Examples

wkrandtest <- as.krandtest(obs = c(0, 1.2, 2.4, 3.4, 5.4, 20.4), 
  sim = matrix(rnorm(6*200), 200, 6))
wkrandtest
#> class: krandtest lightkrandtest 
#> Monte-Carlo tests
#> Call: as.krandtest(sim = matrix(rnorm(6 * 200), 200, 6), obs = c(0, 
#>     1.2, 2.4, 3.4, 5.4, 20.4))
#> 
#> Number of tests:   6 
#> 
#> Adjustment method for multiple comparisons:   none 
#> Permutation number:   200 
#>    Test  Obs     Std.Obs   Alter      Pvalue
#> 1 test1  0.0 -0.07947962 greater 0.552238806
#> 2 test2  1.2  1.08053644 greater 0.134328358
#> 3 test3  2.4  2.38425405 greater 0.024875622
#> 4 test4  3.4  3.61262092 greater 0.004975124
#> 5 test5  5.4  5.10175860 greater 0.004975124
#> 6 test6 20.4 24.20076645 greater 0.004975124
#> 
plot(wkrandtest)

wkrandtest[c(1, 4, 6)]
#> class: krandtest lightkrandtest 
#> Monte-Carlo tests
#> Call: `[.krandtest`(x = wkrandtest, i = c(1, 4, 6))
#> 
#> Number of tests:   3 
#> 
#> Adjustment method for multiple comparisons:   none 
#> Permutation number:   200 
#>    Test  Obs     Std.Obs   Alter      Pvalue
#> 1 test1  0.0 -0.07947962 greater 0.552238806
#> 2 test4  3.4  3.61262092 greater 0.004975124
#> 3 test6 20.4 24.20076645 greater 0.004975124
#> 
wkrandtest[[1]]
#> Monte-Carlo test
#> Call: `[[.krandtest`(x = wkrandtest, i = 1)
#> 
#> Observation: 0 
#> 
#> Based on 200 replicates
#> Simulated p-value: 0.5522388 
#> Alternative hypothesis: greater 
#> 
#>       Std.Obs Expectation  Variance
#> 1 -0.07947962  0.07384821 0.9291465