QQassociation finds Association measure between all the variables in
data with only categorical columns.
QQassociation(factb, use = "everything", methods_used)a data frame with all the categorical columns. This should have at least two columns
an optional character string giving a method for computing association in the presence of missing values. This must be (complete or an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs". If use is "everything", NAs will propagate conceptually, i.e., a resulting value will be NA whenever one of its contributing observations is NA. If use is "all.obs", then the presence of missing observations will produce an error. If use is "complete.obs" then missing values are handled by case wise deletion (and if there are no complete cases, that gives an error). "na.or.complete" is the same unless there are no complete cases, that gives NA
a square data.frame which will store the type of association used between the variables. Dimension will be number of variables * number of variables.
a list of two tables with number of rows and column equal to number
of columns in factb:
Table containing p-values of chi-square test
Table containing Cramer's V
This function measures the association between categorical variables using Chi Square test. This also returns Cramers V value which is a measure of association between two nominal variables, giving a value between 0 and +1 (inclusive). Higher number indicates higher association. Note that, unlike Pearson correlation this doesn't give negative value.
The relation between Cramer's V and Chi Sq test is
$$\sqrt{\frac{\chi ^2}{n*min(k-1,r-1))}}$$
where:
is derived from Pearson's chi-squared test
is the grand total of observations
being the number of columns
being the number of rows
The p-value for the significance of Cramer's V is the same one that is calculated using the Pearson's chi-squared test.
association for association between any type of variables,
CCassociation for Association between Continuous (numeric)
variables,
CQassociation for Association between Continuous-Categorical
variables