https://github.com/ikosmidis/enrichwith
Methods to Enrich R Objects with Extra Components
https://github.com/ikosmidis/enrichwith
infrastructure r
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Methods to Enrich R Objects with Extra Components
- Host: GitHub
- URL: https://github.com/ikosmidis/enrichwith
- Owner: ikosmidis
- Created: 2016-07-04T00:56:46.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2020-01-09T21:01:09.000Z (over 5 years ago)
- Last Synced: 2024-11-27T22:16:19.141Z (5 months ago)
- Topics: infrastructure, r
- Language: R
- Homepage:
- Size: 808 KB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
[](https://cran.r-project.org/package=enrichwith)
[](https://travis-ci.org/ikosmidis/enrichwith)
[](https://codecov.io/github/ikosmidis/enrichwith?branch=master)
# enrichwith
Methods to enrich list-like R objects with extra components
## Rationale
When I develop a piece of statistical methodology it is not uncommon
that it depends on functionality or quantities that a list-like core R
object `object_x` of a certain class could have but doesn't. An
example is objects of class `link-glm` which only provide up to the
the first derivatives of the link function. It is also not uncommon
that the functionality or quantities that I need are hard-coded in
another package in the R ecosystem. Prominent examples include
implementations of gradients of the log-likelihood and information
matrices for specific model classes.
In such cases, I either contact the developers and ask them to provide
a generic which I can then use, or copy some of the code and adopt it
to what I'm doing. Both options can be rather time-consuming, and
particularly the latter is rarely bug-free.
I believe that
* users and developers should have direct access to useful
functionality and quantities in the R ecosystem, epsecially if these include implementations of complex statistical quantities
* quantities and functionality that are specific to a list-like `object_x` should be components of `object_x`
The above motivated me to develop the **enrichwith** R package that
allows `object_x` to be enriched with components corresponding to the
option `enrichment_option` through the following simple call
``` r
enrich(object_x, with = enrichment_option)
```
The call is inspired by [Donald
Knuth's](https://en.wikipedia.org/wiki/Donald_Knuth) [literare
programing](https://en.wikipedia.org/wiki/Literate_programming)
paradigm.
## Purpose and objective
The main objective of **enrichwith** is to allow users and developers
to directly use the enrichment options that other developers have
provided, through a *clean interface*, minimising the need to adopt
source code of others.
The purpose of **enrichwith** is to provide:
* *useful enrichment options* for core R objects, including objects of
class `lm`, `glm`, `link-glm` and `family` (see, for example,
`?enrich.glm`)
* methods for producing *customisable source code templates* for the
structured implementation of methods to compute new components (see
`?enrichwith` and `?create_enrichwith_skeleton`)
* generic methods for the *easy enrichment* of the object with those
components (see, for example, `?enrich` and
`?get_enrichment_options`)
## Vignettes
The vignettes illustrate the **enrichwith** functionality through
comprehensive, step-by-step case studies. These also include
illustrations from recent research of mine on methods for statistical
learning and inference.
## Installation
Get the development version from github with
``` r
# install.packages("devtools")
devtools::install_github("ikosmidis/enrichwith")
```
## Examples
### `link-glm` objects
Objects of class `link-glm` have as components functions to compute
the link function (`linkfun`), the inverse link function (`linkinv`),
and the 1st derivative of the inverse link function (`mu.eta`).
**enrichwith** comes with a built-in template with the methods for the
enrichment of `link-glm` objects with the 2nd and 3rd derivatives of
the inverse link function.
The `get_enrichment_options` method can be used to check what
enrichment options are available for objects of class `link-glm`.
``` r
library("enrichwith")
standard_link <- make.link("probit")
class(standard_link)
# [1] "link-glm"
get_enrichment_options(standard_link)
# -------
# Option: d2mu.deta
# Description: 2nd derivative of the inverse link function
# component compute_function
# 1 d2mu.deta compute_d2mu.deta
# -------
# Option: d3mu.deta
# Description: 3rd derivative of the inverse link function
# component compute_function
# 1 d3mu.deta compute_d3mu.deta
# -------
# Option: inverse link derivatives
# Description: 2nd and 3rd derivative of the inverse link function
# component compute_function
# 1 d2mu.deta compute_d2mu.deta
# 2 d3mu.deta compute_d3mu.deta
# -------
# Option: all
# Description: all available options
# component compute_function
# 1 d2mu.deta compute_d2mu.deta
# 2 d3mu.deta compute_d3mu.deta
```
According to the result of `get_enrichment_options`, the object
`standard_link` can be enriched with the 2nd and 3rd derivative of the
inverse link function through the option "inverse link derivatives".
``` r
enriched_link <- enrich(standard_link, with = "inverse link derivatives")
class(enriched_link)
# [1] "enriched_link-glm" "link-glm"
cat(format(enriched_link$d2mu.deta), sep = "\n")
# function (eta)
# {
# -eta * pmax(dnorm(eta), .Machine$double.eps)
# }
cat(format(enriched_link$d3mu.deta), sep = "\n")
# function (eta)
# {
# (eta^2 - 1) * pmax(dnorm(eta), .Machine$double.eps)
# }
```
`enriched_link` is now an "enriched" `link-glm` object, which, as per
the enrichment options above, has the extra components `d2mu.deta` and
`d3mu.deta`, for the calculation of 2nd and 3rd derivatives of the
inverse link function with respect to `eta`, respectively.
### `family` objects
