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Fixed Monte Carlo P-value calculation in
TreeMineR(). Simulated maximum log-likelihood ratios (LLRs)
equal to the observed LLR are now counted as at least as extreme as the
observed value (>= instead of >), as
required for a valid Monte Carlo P-value. Previously, ties were
excluded, which inflated the family-wise type-I error rate, particularly
for sparse count data with many possible ties (#9).
Fixed calc_llr() so that nodes in which every
individual is exposed (or every individual is unexposed) no longer get
an undefined (NaN) log-likelihood ratio. Such nodes were
previously dropped from both the simulated reference distribution and
the reported results, which could bias P-values (#9).
Added a check to TreeMineR() which now throws an
error if p is not greater than 0 and smaller than 1.
p = 0 or p = 1 made the log-likelihood ratio
degenerate (Inf) for all nodes containing exposed,
respectively unexposed, individuals.
Monte Carlo simulations in TreeMineR() now preserve
the correlation between related cuts. Exposure is simulated once per
individual and re-aggregated through the tree, rather than resampling
every cut independently, so a parent cut’s simulated count is now
mechanically consistent with its children’s, as it is in the observed
data (#10).
Added a check which now throws an error if data does
not contain any rows, and if tree contains leafs that are
duplicated across different branches. Both previously caused an
uninformative error from internal data.table
operations.
Fixed issue in the example dataset: The example dataset included individuals with non-constant exposure status, i.e., individuals who were at the same time exposed and unexposed. This should not be allowed. The example dataset has been updated accordingly.
Added a check to the TreeMineR function which now throws an error if exposure status is not constant within ids.
TreeMineR() for returning the test
distribution of LLRs used for calculating the P-values.A02AH, which only has a depth of 4 levels.data.table from depend field in
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