R: cov.sel.np

cov.sel.np {CovSel}

R Documentation

cov.sel.np

Description

Function called by cov.sel if type="np". Not meant to be used on its own.

Usage

cov.sel.np(T, Y, X, alg, scope, thru, thro, thrc, dat, data.0,
		data.1, covar, ...)

Arguments

`T`	A vector, containing `0` and `1`, indicating the binary treatment variable.
`Y`	A vector of observed outcomes.
`X`	A matrix or data frame containing columns of covariates. The covariates may be a mix of continuous, unordered discrete (to be specified in the data frame using `factor`), and ordered discrete (to be specified in the data frame using `ordered`).
`alg`	Specifying which algorithm to be use. `1` indicates Algorithm A, `2` indicates Algorithm B and `3` runs them both. See Details. `alg = 3` is default.
`scope`	A character string giving the name of one (or several) covariate(s) that must not be removed.
`thru`	Bandwidth threshold for unordered discrete covariates. Values in `[0,1]` are valid. `thru=0` removes all unordered discrete covariates and `thru=1` removes none of them. Default is `thru=0.5`.
`thro`	Bandwidth threshold for ordered discrete covariates. Values in `[0,1]` are valid. `thro=0` removes all unordered discrete covariates and `thro=1` removes none of them. Default is `thro=0.25`.
`thrc`	Bandwidth threshold for continuous covariates. Non-negative values are valid. Default is `thr=100`.
`dat`	Passed on from `cov.sel`
`data.0`	Passed on from `cov.sel`
`data.1`	Passed on from `cov.sel`
`covar`	Passed on from `cov.sel`
`...`	Additional arguments passed on to `npregbw`. `regtype` can be set to `"lc"` or `"ll"`, the first being default and `bwtype` can be set to `"fixed"`, `"generalized_nn"` or `"adaptive_nn"`, defaults to `"fixed"`.

Details

See cov.sel for details.

Value

Function returns subsets, methods and removed covariates. See cov.sel for details.

Note

cov.sel.np calls the function npregbw so the package np is required.

Author(s)

Jenny Häggström, <jenny.haggstrom@umu.se>

References

de Luna, X., I. Waernbaum, and T. S. Richardson (2011). Covariate selection for the nonparametric estimation of an average treatment effect. Biometrika 98. 861-875

Häggström, J., E. Persson, I. Waernbaum and X. de Luna (2015). An R Package for Covariate Selection When Estimating Average Causal Effects. Journal of Statistical Software 68. 1-20

Hall, P., Q. Li and J.S. Racine (2007). Nonparametric estimation of regression functions in the presence of irrelevant regressors. The Review of Economics and Statistics, 89. 784-789