# cont_conv: Continuous convolution In tnagler/cctools: Tools for the Continuous Convolution Trick in Nonparametric Estimation

## Description

Applies the continuous convolution trick, i.e. adding continuous noise to all discrete variables. If a variable should be treated as discrete, declare it as `ordered()` (passed to `expand_as_numeric()`).

## Usage

 `1` ```cont_conv(x, theta = 0, nu = 5, quasi = TRUE) ```

## Arguments

 `x` data; numeric matrix or data frame. `theta` scale parameter of the USB distribution (see, `dusb()`). `nu` smoothness parameter of the USB distribution (see, `dusb()`). The estimator uses the Epanechnikov kernel for smoothing and the USB for continuous convolution (default parameters correspond to the U[-0.5, 0.5] distribution). `quasi` logical indicating whether quasi random numbers sholuld be used (`qrng::ghalton()`); only works for `theta = 0`.

## Details

The UPSB distribution (`dusb()`) is used as the noise distribution. Discrete variables are assumed to be integer-valued.

## Value

A data frame with noise added to each discrete variable (ordered columns).

## References

Nagler, T. (2017). A generic approach to nonparametric function estimation with mixed data. arXiv:1704.07457

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# dummy data with discrete variables dat <- data.frame( F1 = factor(rbinom(10, 4, 0.1), 0:4), Z1 = ordered(rbinom(10, 5, 0.5), 0:5), Z2 = ordered(rpois(10, 1), 0:10), X1 = rnorm(10), X2 = rexp(10) ) pairs(dat) pairs(expand_as_numeric(dat)) # expanded variables without noise pairs(cont_conv(dat)) # continuously convoluted data ```

tnagler/cctools documentation built on Nov. 28, 2017, 10:16 a.m.