interact.gbm(x,
data,
i.var = 1,
n.trees = x$n.trees)gbm.object fitted using a call to gbmx. If the original dataset is
large, a random subsample may be used to accelerate the computation in
interact.gbmgbm formula.n.trees trees will be usedinteract.gbm computes Friedman's H-statistic to assess the relative
strength of interaction effects in non-linear models. H is on the scale of
[0-1] with higher values indicating larger interaction effects. To connect to
a more familiar measure, if $x_1$ and $x_2$ are uncorrelated covariates
with mean 0 and variance 1 and the model is of the form
$$y=\beta_0+\beta_1x_1+\beta_2x_2+\beta_3x_3$$
then
$$H=\frac{\beta_3}{\sqrt{\beta_1^2+\beta_2^2+\beta_3^2}}$$Note that if the main effects are weak, the estimated H will be unstable. For example, if (in the case of a two-way interaction) neither main effect is in the selected model (relative influence is zero), the result will be 0/0. Also, with weak main effects, rounding errors can result in values of H > 1 which are not possible.
gbm, gbm.object