anova_test | R Documentation |
This function carries out an hypothesis test in which the null hypothesis is that K samples are governed by the same underlying generative probability distribution against the alternative hypothesis that they are governed by different generative probability distributions.
anova_test(
data,
memberships,
stats = list(stat_anova_f_ip),
B = 1000L,
M = NULL,
alternative = "right_tail",
combine_with = "tippett",
type = "exact",
seed = NULL,
...
)
data |
A numeric vector or a numeric matrix or a list specifying the
pooled data points. Alternatively, it can be a distance matrix stored as an
object of class |
memberships |
An integer vector specifying the original membership of each data point. |
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 p-value is right-tailed, left-tailed or two-tailed. Choices are
|
combine_with |
A string specifying the combining function to be used to
compute the single test statistic value from the set of p-value estimates
obtained during the non-parametric combination testing procedure. For now,
choices are either |
type |
A string specifying which formula should be used to compute the
p-value. 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 base::list
with 4 components:
observed
: the value of the (possible combined) test statistic(s) using
the original memberhips of data points;
pvalue
: the permutation p-value;
null_distribution
: the values of the (possible combined) test statistic(s)
using the permuted memberhips of data points;
permutations
: the permutations that were effectively sampled to produce
the null distribution.
A user-specified function should have at least two arguments:
the first argument should be either a list of the n
pooled data points or
a dissimilarity matrix stored as a stats::dist
object.
the second argument shoud be an integer vector specifying the (possibly permuted) membership of each data point.
See the stat_anova_f()
function for an example.
out1 <- anova_test(
data = dist(chickwts$weight),
memberships = chickwts$feed,
stats = list(stat_anova_f_ip)
)
out1$pvalue
out2 <- anova_test(
data = chickwts$weight,
memberships = chickwts$feed,
stats = list(stat_anova_f)
)
out2$pvalue
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