multivariance.test {multivariance} | R Documentation |
This performs the (specified by type
and p.value.type
) independence test for the columns of a sample matrix.
multivariance.test(x, vec = 1:ncol(x), type = "total", p.value.type = "distribution_free", verbose = TRUE, ...)
x |
matrix, each row is a sample |
vec |
vector which indicates which columns are treated as one sample |
type |
one of |
p.value.type |
one of |
verbose |
logical, if TRUE meaningful text output is generated. |
... |
these are passed to |
For the use of vec
see the examples below and the more detailed explanation of this argument for multivariance
.
The types "independence"
and "total"
are identical: an independence test is performed.
Also the types "pairwise independence"
and "m.multi.2"
are identical: a test of pairwise independence is performed.
The type "m.multi.3"
, performs a test for 3-independence, assuming pairwise independence. The type "multi"
performs a test for n-independence, assuming (n-1)-independence.
There are several ways (determined by p.value.type
) to estimate the p-value: The "pearson_approx"
and "resample"
are approximately sharp. The latter is based on a resampling approach and thus much slower. The "distribution_free"
test might be very conservative, its p-value estimates are only valid for p-values lower than 0.215 - values above should be interpreted as "values larger than 0.215".
All tests are performed using the standard euclidean distance. Other distances can be supplied via the ...
, see cdm
for the accepted arguments.
A list with class "htest
" containing the following components:
statistic
the value of the test statistic,
p.value
the p-value of the test statistic,
method
a character string indicating the type of test performed,
data.name
a character string giving the name(s) of the data.
For the theoretic background see the references given on the main help page of this package: multivariance-package.
# an independence test multivariance.test(dep_struct_several_26_100) # conservative multivariance.test(dep_struct_several_26_100,p.value.type = "resample") #sharp but slow multivariance.test(dep_struct_several_26_100,p.value.type = "pearson_approx") # # as an example, all tests for one data set: coins100 = coins(100) for (ty in c("total","m.multi.2","m.multi.3","multi")) for (pvt in c("distribution_free","resample","pearson_approx")) print(multivariance.test(coins100,type=ty,p.value.type = pvt)) # using the vec argument: x = matrix(rnorm(50*6),ncol = 10) # a 50x6 data matrix vec = c(1,2,3,4,5,6) # each column is treated as one variable multivariance.test(x,vec) # is the same as the default vec = c(1,2,2,1,3,1) # column 1,4,6 are treated as one variable # column 2,3 are treated as one variable # column 5 is treated as one variable multivariance.test(x,vec)