multcor.test.Rd
It performs multiple correlation t-tests from a correlation matrix based on the statistic: $$t = r * \sqrt(df / (1 - r^2))$$ where, in general, \(df = n - 2\).
multcor.test(x, n = NULL, Df = NULL, alternative = c("two.sided", "less", "greater"), adjust = "none")
x | a correlation matrix. |
---|---|
n | the number of observations; if |
Df | the number of degrees of freedom of the t statistic; if |
alternative | the alternative hypothesis. It must be one of "two.sided", "greater" or "less". You can specify just the initial letter. "greater" corresponds to positive association,"less" to negative association. The default is "two.sided". |
adjust | The adjustment method for multiple tests. It must be one of
"holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
"none" (default). For more information, see |
A list with class "multcor.test" containing the following components:
the t-value calculated for each correlation.
the p.value for each t-test, adjusted for multiple tests.
a matrix containing the p.values
for each t-test (lower triangular)
and a symbol indicating the significance level at which one can to reject the null hypothesis
(upper triangular).
a character indicating the p-value adjustment method.
the degrees of freedom of the tests.
a character indicating the type of alternative hypothesis.
a character string giving the name of the data.
Anderson Rodrigo da Silva <anderson.agro@hotmail.com>
#> #> Pairwise correlation t-test #> #> data: peppercorr #> degrees of freedom: 18 #> alternative hypothesis: the true correlation is not equal to 0 #> p-values (with none adjustment for multiple tests): #> PL FL LFD PT AFW DMC #> PL <NA> ** * #> FL 0.4676 <NA> * #> LFD 0.0035 0.9957 <NA> *** *** #> PT 0.1313 0.732 0.001 <NA> #> AFW 4e-04 0.5363 0 1e-04 <NA> #> DMC 0.0432 0.032 2e-04 5e-04 3e-04 <NA> #> --- #> Signif. codes: '***'0.001 '**'0.01 '*'0.05 '.'0.1 ' '1 #># End (not run)