association: Find association between variables
In analyzer: Data Analysis and Automated R Notebook Generation

Description Usage Arguments Details Value See Also Examples

association finds association among all the variables in the data.

association(
  tb,
  categorical = NULL,
  method1 = c("auto", "pearson", "kendall", "spearman"),
  method3 = c("auto", "parametric", "non-parametric"),
  methodMats = NULL,
  use = "everything",
  normality_test_method = c("ks", "anderson", "shapiro"),
  normality_test_pval = 0.05,
  ...
)

`tb`	tabular data
`categorical`	a vector specifying the names of categorical (character, factor) columns
`method1`	method for association between continuous-continuous variables. values can be `"auto", "pearson", "kendall", "spearman"`. See details for more information.
`method3`	method for association between continuous-categorical variables. Values can be `"auto", "parametric", "non-parametric"`. See details of `CQassociation` for more information. Parametric does t-test while non-parametric does 'Mann-Whitney’ test.
`methodMats`	This parameter can be used to define the methods for calculating correlation and association at variables pair level. The input is a square data.frame of dimension - number of columns in `tb`. The row names and column names of `methodMats` are the column names of `tb`. The values in the data.frame can be: between continuous-continuous variables from parameter `method1` - "auto", "pearson", "kendall", "spearman" between continuous-categorical variables from parameter `method3` - "auto", "parametric", "non-parametric" between categorical-categorical variables can be anything Default is NULL. In that case the method used for calculating correlation and association will be the inputs from parameters. This parameter can also tale some other values. See example for more details. But its advisable to use like mentioned above.
`use`	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
`normality_test_method`	method for normality test for a variable. Values can be `shapiro` for Shapiro-Wilk test or `'anderson'` for 'Anderson-Darling' test of normality or `ks` for 'Kolmogorov-Smirnov'
`normality_test_pval`	significance level for normality tests. Default is 0.05
`...`	other parameters passed to `cor`, `CCassociation`, `CQassociation` and `QQassociation`

This function calculates association value in three categories -

between continuous variables (using CCassociation function)
between categorical variables (using QQassociation function)
between continuous and categorical variables (using CQassociation function)

For more details, look at the individual documentation of CCassociation, QQassociation, CQassociation

A list of three tables:

continuous_corr: correlation among all the continuous variables
continuous_pvalue: Table containing p-value for the correlation test
categorical_cramers: Cramer's V value among all the categorical variables
categorical_pvalue: Chi Sq test p-value
continuous_categorical: association value among continuous and categorical variables
method_used: A data.frome showing the method used for all pairs of variables

CCassociation for Correlation between Continuous variables, QQassociation for Association between Categorical variables, CQassociation for Association between Continuous-Categorical variables

tb <- mtcars
tb$cyl <- as.factor(tb$cyl)
tb$vs  <- as.factor(tb$vs)
out <- association(tb, categorical = c("cyl", "vs"))

# To use the methodMats parameter, create a matrix like this
methodMats <- out$method_used

# the values can be changed as per requirement
# NOTE: in addition to the values from parameters method1 and method3,
#       the values in methodMats can also be the values returned by
#       association function. But its advisable to use the options from
#       method1 and method3 arguements
methodMats["mpg", "disp"] <- methodMats["disp", "mpg"] <- "spearman"
out <- association(tb, categorical = c("cyl", "vs"), methodMats = methodMats)
rm(tb)