testGroupIndependence {psichomics} | R Documentation |
Test multiple contingency tables comprised by two groups (one reference group and another containing remaining elements) and provided groups.
testGroupIndependence(ref, groups, elements, pvalueAdjust = "BH")
ref |
List of character: list of groups where each element contains the identifiers of respective elements |
groups |
List of characters: list of groups where each element contains the identifiers of respective elements |
elements |
Character: all available elements (if a data frame is given, its rownames will be used) |
pvalueAdjust |
Character: method used to adjust p-values (see Details) |
The following methods for p-value adjustment are supported by using
the respective string in the pvalueAdjust
argument:
none
: Do not adjust p-values
BH
: Benjamini-Hochberg's method (false discovery rate)
BY
: Benjamini-Yekutieli's method (false discovery rate)
bonferroni
: Bonferroni correction (family-wise error rate)
holm
: Holm's method (family-wise error rate)
hochberg
: Hochberg's method (family-wise error rate)
hommel
: Hommel's method (family-wise error rate)
multiGroupIndependenceTest
object, a data frame containing:
attribute |
Name of the original groups compared against the reference groups |
table |
Contingency table used for testing |
pvalue |
Fisher's exact test's p-value |
parseCategoricalGroups()
and
plotGroupIndependence()
Other functions for data grouping:
createGroupByAttribute()
,
getGeneList()
,
getSampleFromSubject()
,
getSubjectFromSample()
,
groupPerElem()
,
plotGroupIndependence()
elements <- paste("subjects", 1:10) ref <- elements[5:10] groups <- list(race=list(asian=elements[1:3], white=elements[4:7], black=elements[8:10]), region=list(european=elements[c(4, 5, 9)], african=elements[c(6:8, 10)])) groupTesting <- testGroupIndependence(ref, groups, elements) # View(groupTesting)