View source: R/onesampletest.R
one_sample_test  R Documentation 
This function carries out an hypothesis test where the null hypothesis is that the sample is governed by a generative probability distribution which is centered and symmetric against the alternative hypothesis that they are governed by a probability distribution that is either not centered or not symmetric.
one_sample_test(
x,
stats = list(stat_max),
B = 1000L,
M = NULL,
alternative = "two_tail",
combine_with = "tippett",
type = "exact",
seed = NULL,
...
)
x 
A numeric vector or a numeric matrix or a list representing the sample from which the user wants to make inference. 
stats 
A list of functions produced by 
B 
The number of sampled permutations. Default is 
M 
The total number of possible permutations. Defaults to 
alternative 
A single string or a character vector specifying whether
the pvalue is righttailed, lefttailed or twotailed. Choices are

combine_with 
A string specifying the combining function to be used to
compute the single test statistic value from the set of pvalue estimates
obtained during the nonparametric combination testing procedure. For now,
choices are either 
type 
A string specifying which formula should be used to compute the
pvalue. Choices are 
seed 
An integer specifying the seed of the random generator useful for
result reproducibility or method comparisons. Default is 
... 
Extra parameters specific to some statistics. 
A list
with three components: the value of the
statistic for the original two samples, the pvalue of the resulting
permutation test and a numeric vector storing the values of the permuted
statistics.
A userspecified function should have at least two arguments:
the first argument is data
which should be a list of the n
observations from the sample;
the second argument is flips
which should be an integer vector giving
the signs by which each observation in data
should be multiplied.
It is possible to use the use_stat
function with nsamples = 1
to have flipr automatically generate a template file for writing down
your own test statistics in a way that makes it compatible with the flipr
framework.
See the stat_max
function for an example.
n < 10L
mu < 3
sigma < 1
# Sample under the null distribution
x1 < rnorm(n = n, mean = 0, sd = sigma)
t1 < one_sample_test(x1, B = 100L)
t1$pvalue
# Sample under some alternative distribution
x2 < rnorm(n = n, mean = mu, sd = sigma)
t2 < one_sample_test(x2, B = 100L)
t2$pvalue
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