R: Estimate the strength of interaction effects

interact.gbm {gbm} | R Documentation

## Estimate the strength of interaction effects

### Description

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

### Usage

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

### Arguments

`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.

### Details

`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=β_0+β_1x_1+β_2x_2+β_3x_3

then

H=\frac{β_3}{√{β_1^2+β_2^2+β_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.

### Value

Returns the value of H.

### Author(s)

Greg Ridgeway gregridgeway@gmail.com

### References

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

### See Also

`gbm`, `gbm.object`

[Package gbm version 2.1.8 Index]