dscr-template

a template repository for a dynamic statistical comparison

An example

See https://github.com/stephens999/dscr-example for an example of a repo set up using this template

How to use this to set up a new DSC

1. Copy this repo. For example, suppose you want to call your repo dscr-example and host it on github. You could do the following:
   • create a new repo named dscr-example on your github account.
   • clone this repo to your locaal computer, under the directory dscr-example say, using git clone https://github.com/stephens999/dscr-template.git dscr-example
   • set up your local repo to push to your github repo: cd dscr-example git remote rm origin git remote add origin https://github.com/yourgithubid/dscr-example.git
   • Push to your github repo using git push -u origin master
2. Put at least one datamaker function in a .R file in the datamakers directory (all .R files in this directory will be sourced before scenarios.R). See the file datamakers/eg_datamaker.R for example.
3. Put at least one method function in a .R file in the methods directory (all .R files in this directory will be sourced before methods.R). See the file methods/eg_method.R
4. Edit the file scenarios.R to define your scenarios
5. Edit the file methods.R to define your methods
6. Edit the file score.R to define your scoring function
7. Replace the text in this README.md file with a description of the DSC. Include background, and definitions of the structure of the objects meta, input, and output that is used by your DSC. A template for these instructions is included below.
8. Run your DSC by running source("run_dsc.R") in R. [Make sure you have installed the dscr package first from https://github.com/stephens999/dscr]

Instructions Template

Edit this template to provide specific details on your own DSC. I have included some instructions that might be generically applicable and useful for all DSCs (eg how to add a method, how to add a scenario).

Background

For a general introduction to DSCs, see here.

Here provide general background on the problem that methods in this DSC are attempting to do. Provide enough detail so that someone could work out whether their method might be appropriate to add to the DSC.

Input, meta and output formats

This DSC uses the following formats:

input: list(name [type]) # Add more explanation of each element here

meta: list(name [type]) # Add more explanation of each element here

output: list(name [type]) # Add more explanation of each element here

Scores

Provide a summary of how methods are scored.

See score.R.

To add a method

To add a method there are two steps.

• add a .R file containing an R function implenting that method to the methods/ subdirectory
• add the method to the list of methods in the methods.R file.

Each method function must take arguments (input,args) where input is a list with the correct format (defined above), and args is a list containing any additional arguments the method requires.

Each method function must return output, where output is a list with the correct format (defined above).

To add a scenario

To add a scenario there are two steps, the first of which can be skipped if you are using an existing datamaker function

• add a .R file containing an R function implenting a datamaker to the datamakers/ subdirectory
• add the scenario to the list of scenarios in the scenarios.R file.

Each datamaker function must return a list(meta,input) where meta and input are each lists with the correct format (defined above).