This is a high-level function which accepts a data set, stop criteria, and split functions for continuous variables and then applies a chi-square test for independence to bins generated by recursively binning the ranks of continuous variables or implied by the combinations of levels of categorical variables.
inDep(
data,
stopCriteria,
catCon = uniRIntSplit,
conCon = rIntSplit,
ptype = c("simple", "conservative", "gamma", "best"),
dropPoints = FALSE
)An `inDep` object, with slots `data`, `types`, `pairs`, `binnings`, `residuals`, `statistics`, `K`, `logps`, and `pvalues` that stores the results of using recursive binning with the specified splitting logic to test independence on a data set. `data` gives the name of the data object in the global environment which was split, `types` is a character vector giving the data types of each pair, `pairs` is a character vector of the variable names of each pair, `binnings` is a list of lists where each list is the binning fir to the corresponding pair by the recursive binning algorithm, `residuals` is list of numeric vectors giving the residual for each bin of each pairwise binning, `statistics` is a numeric vector giving the chi-squared statistic for each binning, `K` is a numeric vector giving the number of bins in each binning, `logps` gives the natural logarithm of the statistic's p-value, and finally `pvalues` is a numeric vector of p-values for `statistics` based on the specified p-value computation, which defaults to 'simple'. Internally, the p-values are computed on the log scale to better distinguish between strongly dependent pairs and the `pvalues` returned are computed by calling `exp(logps)`. The order of all returned values is by increasing `logps`.
`data.frame` or object coercible to a `data.frame`
output of `makeCriteria` providing criteria used to stop binning to be passed to binning functions
splitting function to apply to pairs of one cateogorical and one continuous variable
splitting function to apply to pairs of continuous variables
one of 'simple', 'conservative', 'gamma', or 'fitted'; the type of p-values to compute for continuous pairs and pairs of mixed type. 'Conservative' assumes a chi-square distribution to the statistic with highly conservative degrees of freedom that are based on continuous uniform margins and so do not account for the constraints introduced by the ranks. 'Simple' assumes a chi-square distribution but uses contingency-table inspired degrees of freedom which can be slightly anti-conservative in the case of continuous pairs but work well for continuous/categorical comparisons. 'Gamma' assumes a gamma distribution on the resulting statistics with parameters fit from the same empirical investigation. 'Fitted' mixes the gamma approach and the chi-squared approach these by applying 'gamma' to continuous-categorical comparisons and a least squares fitted version of the simple approximation to continuous-continuous comparisons. For all categorical-categorical comparisons the contingency table degrees of freedom are use in a chi-squared distribution. More details can be found in the associated paper.
logical; should returned bins contain points?
Chris Salahub
The output of `inDep` is a list, the first element of which is a list of lists, each of which records the details of the binning of a particular pair of variables