Contents
What is the criterion table in ctree partykit?
(3) The $criterion table erroneously shown currently in the type = “simple” plot contains the test statistics and corresponding log 1-p values from the conditional inference conducted in each node.
How to get the list of splitting conditions?
If you use the new recommended partykit implementation of ctree () rather than the old party package, then you can use the function .list.rules.party (). This is not yet officially exported, yet, but can be leveraged to extract the desired information.
Why is my ctree plot not working properly?
In general: Also the explanatory variables need to have appropriate classes (numeric, factor, ordered factor) to be processed correctly when growing the tree. (2) The plot (…, type = “simple”) currently does not work as desired – in other words this is a bug.
How to cross validate ctree output in partykit?
Note how (a) the values of the trees change from an average value of 0s and 1s (regression tree) to the most likely class 0/1 (classification tree) and (b) the err values from a number (error) become a percentage (missclassification). Thanks for contributing an answer to Cross Validated!
When to use quadratic test statistic in ctree?
This means that one should use a quadratic test statistic when factors are in play (because the evaluation of the corresponding multivariate normal distribution is time-consuming). By default, the scores for each ordinal factor x are 1:length (x), this may be changed using scores = list (x = c (1,5,6)), for example.
How is the univariate p-value computed in ctree?
The univariate p-values based on the limiting distribution (chi-square or normal) are computed for each of the random permutations of the data. This means that one should use a quadratic test statistic when factors are in play (because the evaluation of the corresponding multivariate normal distribution is time-consuming).