Significant Taxa using Mirror Statistics — select

This selects taxa through a data splitting procedure. It retrains an mbtransfer model across random splits of the data. Partial dependence profiles for pairs of splits are compared with one another -- if they agree for a given feature, then that feature is considered more likely to have a true effect on the response. This function only supports inference of the effects of interventions on taxa responses, but the same principle could apply to estimate significant relationships between taxa.

Usage

select_taxa(ts, w0, w1, tr_fun, qvalue = 0.2, ...)

Arguments

ts: An object of class ts_inter containing the time-varying microbiome community, environmental interventions, and static host features data. The columns for each element of the values matrix are expected to be consecutive timepoints in an changing community. ts_from_dfs() is a helper to create these objects from data.frames with the relevant information.
w0: One of the counterfactuals with which to compute partial dependence profiles. See steps or pulses for helpers in generating these counterfactuals. The procedure concatenates these counterfactuals to the end of the series and computes the difference in the forecasts.
w1: One of the counterfactuals with which to compute partial dependence profiles. See steps or pulses for helpers in generating these counterfactuals. The procedure concatenates these counterfactuals to the end of the series and computes the difference in the forecasts.
tr_fun: A function that can be used to train over random splits. In the examples in this package, we use an anonymous function that fills our chosen hyperparameters in mbtransfer. For example \(x) mbtransfer(x, P = 1, Q = 1) will fit a lag-1 transfer function model on all the random splits.
qvalue: The target False Discovery Rate. Defaults to 0.2.

Examples

data(sim_ts)
w0 <- cbind(sim_ts[[1]]@interventions, matrix(0, nrow = 1, ncol = 3))
w1 <- cbind(sim_ts[[1]]@interventions, matrix(1, nrow = 1, ncol = 3))
S <- select_taxa(sim_ts, w0, w1, \(x) mbtransfer(x, 1, 1, nrounds = 10), n_splits = 1)
#> Training models for split 1/1
#> Warning: no non-missing arguments to max; returning -Inf
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