functionalANOVA: generalized functional analysis of variance
In zmjones/fanova: Functional ANOVA Decomposition of Prediction Functions

Description Usage Arguments Value References

computes a generalized (weighted) functional ANOVA decomposition of a prediction function, giving the best additive decomposition of the prediction function in terms of squared error.

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functionalANOVA(data, vars, n = c(10, 2), model,
  predict.fun = function(object, newdata) predict(object, newdata = newdata),
  weight.fun = NULL)

`data`	a `data.frame` or `data.table` which contains the features/covariates on which `predict.fun` was learned/estimated.
`vars`	a `character` vector which corresponds to a subset of the columns of `data`.
`n`	a `numeric` vector of length 2, where the first element corresponds to the dimension of the grid constructed for each of the elements of `vars` and the second element to the number of rows to sample from `data`.
`model`	the first argument to `predict.fun`, presumably a model object which can make predictions.
`predict.fun`	a `function` whose first two arguments are "object" and "newdata" which returns a numeric vector the same length as the number of rows in newdata. the default value is to call the `predict` method on the `model`.
`weight.fun`	a `function` with two arguments, `design` and `data`, both of which are `data.tables` which returns a `numeric` of the same length as the number of rows in `design`. this is intended for use to use the `data` to estimate the distribution of the input features, and then to use that estimate to the probability of points in the `design` grid.

a data.table with columns for a grid of points of the vars, a (set of) column(s) that correspond to the estimated effect of those features/covariates on the prediction function, and a column effect which indicates which subset of the covariates/features each estimate belongs to.

Giles Hooker. Generalized Functional ANOVA Diagnostics for High Dimensional Functions of Dependent Variables, Journal of Computational and Graphical Statistics, Vol. 16, No. 3 (2007), pp. 709-732.

zmjones/fanova documentation built on May 4, 2019, 11:24 p.m.