Computes Friedman's H-statistic to assess the strength of variable interactions.

`interact.gbm(x, data, i.var = 1, n.trees = x$n.trees)`

x

A `gbm.object`

fitted using a call to `gbm`

.

data

The dataset used to construct `x`

. If the original dataset
is large, a random subsample may be used to accelerate the computation in
`interact.gbm`

.

i.var

A vector of indices or the names of the variables for compute
the interaction effect. If using indices, the variables are indexed in the
same order that they appear in the initial `gbm`

formula.

n.trees

The number of trees used to generate the plot. Only the first
`n.trees`

trees will be used.

Returns the value of \(H\).

`interact.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.

J.H. Friedman and B.E. Popescu (2005). “Predictive Learning via Rule Ensembles.” Section 8.1