Description Usage Arguments Details Value
Pairwise statistical tests! Wiser than Supermatrix. This function allows you to perform multiple pairwise association tests between two sets of data, containing numeric/categorical and categorical data, respectively, and to adjust reulting p-values by different methods. See details
for more information.
1 |
x |
A data frame containing your data. See |
v1 |
A range of columns containing phenotypic data in x. Data can be either quantitative (numeric) or qualitative (factor). Example: 1:20, for 20 columns of trait data, located in the first 20 columns. |
v2 |
A range of columns containing genotypic data in x. Example: 20:40, for 20 sites located in columns 20 to 40. |
fisher.method |
Indicates which method should be used for Fisher's tests. |
correction |
In case that p-value correction for multiple tests is required, use this parameter to specify which correction must be used. See ?p.adjust for details of available correction methods. It is possible to have more than one correction in one go, using a vector with correction names in the |
control |
Used as a internal control helper, to check that supermatrix performed the tests accordingly to variables' type.If TRUE, two columns indicating whether (1) the trait value was identified as numeric, and (2) the name of the test used for that column vs. genotypic data. |
Superwise function was designed to perform multiple pairwise association analyses between two sets of phenotypic vs genotypic data. Set1 must ideally contain phenotypic data (in columns), which can be quantitative (e.g. number of offspring, weight, etc) or categorical unordered (e.g. diurnal/nocturnal, herbivore/carnivore/omnivore) data. Set2 must ideally contain categorical unordered data, such as which amino-acid in a specific site in a proteic sequence Such data should be distributed in columns, using rows for each species to be included in the analyses. So far, superwise performs only Kruskal-Wallis test for numeric vs. categorical variables, and Fisher's Exact Test for categorical vs. categorical variables. The recommended format is a single data.frame containing the two sets of data to be compared pairwise. NOTE: Categorical data must be of factor class.
A data frame with the results of each test, including P-values and adjusted P-values (if requested).
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