Package 'rcausim'

Title: Generate Causally-Simulated Data
Description: Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html>.
Authors: Herdiantri Sufriyana [aut, cre] , Emily Chia-Yu Su [aut]
Maintainer: Herdiantri Sufriyana <[email protected]>
License: GPL-3
Version: 0.1.1
Built: 2024-09-23 06:20:02 UTC
Source: CRAN

Help Index

Generate causally-simulated data

Description

Generate causally-simulated data

Usage

data_from_function(func, n)

Arguments

func

Functions, an object class generated by function_from_edge or function_from_user functions. All vertices must be defined for their functions. The causal structure needs to be a directed acyclic graph (DAG), which means no loops are allowed. Use edge_from_function to identify edges given a list of functions, then draw a causal diagram using the edges data frame (see vignettes). At least a function in the list must include 'n' as the only argument. All arguments within any function must be defined by their respective functions, except the argument 'n'. The output lengths of vertex functions must match the specified length 'n'.
n

Number of observations, a numeric of length 1, non-negative, and non-decimal.

Value

A data frame which include the simulated data for each vertex as a column.

Examples

data(functions)
data_from_function(functions, n = 100)

Define a function in the list of functions

Description

Define a function in the list of functions

Usage

define(func, which, what)

Arguments

func

Functions, an object class generated by function_from_edge or function_from_user functions.
which

Which, a character of length 1 indicating a vertex name for which function is defined. The vertex name must be defined in 'Functions'.
what

What, a function to be defined. It must use all and only the specified arguments for the vertex in 'Functions', if not previously defined.

Value

A list of either functions or character vectors of arguments for function. It can be continuously defined or redefined by a user using define function. If all elements of the list are functions, then it can be an input for generating the simulated data.

Examples

data(edges)
functions <- function_from_edge(edges)
function_B <- function(n){ rnorm(n, 90, 5) }
functions <- define(functions, 'B', function_B)

Identify edges given functions

Description

Identify edges given functions

Usage

edge_from_function(func)

Arguments

func

Functions, an object class generated by function_from_edge or function_from_user functions.

Value

A data frame which include the columns 'from' and 'to in this order.

Examples

data(functions)
edge_from_function(functions)

Edges

Description

An example of a data frame which include the columns 'from' and 'to' in this order. A vertex name 'n' does not exist.

Usage

edges

Format

A data frame with 7 rows and 2 columns:

from

A vertex name from which a directed edge comes.

to

A vertex name to which a directed edge comes.

Source

Generated for examples in this package.

List functions given edges

Description

List functions given edges

Usage

function_from_edge(e)

Arguments

e

Edge, a data frame that must only include the columns 'from' and 'to in this order. A vertex name 'n' is not allowed.

Value

A list of character vectors of arguments for function which will be defined by a user using define function.

Examples

data(edges)
function_from_edge(edges)

List functions from user

Description

List functions from user

Usage

function_from_user(func)

Arguments

func

Functions, a list of functions which are defined by a user. The list must be non-empty. All elements of the list must be named. All elements of the list must be functions. The list must construct 1 edge or more.

Value

A list of functions. It can be an input for generating the simulated data, or redefined by a user using define function.

Examples

function_B <- function(n){ rnorm(n, mean = 90, sd = 5) }
function_A <- function(B){ ifelse(B>=95, 1, 0) }
functions <- list(A = function_A, B = function_B)
functions <- function_from_user(functions)

Functions

Description

An example of an object class generated by function_from_edge or function_from_user functions. The causal structure is a directed acyclic graph (DAG), which means no loops are allowed. A function in the list include 'n' as the only argument. All arguments within any function are defined by their respective functions, except the argument 'n'. The output lengths of vertex functions match the specified length 'n'.

Usage

functions

Format

A list with 5 elements:

B

A function with an argument 'n'.

A

A function with an argument 'B'.

D

A function with an argument 'A'.

C

A function with arguments 'A', 'B', and 'D'.

E

A function with arguments 'A' and 'C'.

Source

Generated for examples in this package.

Print method for Functions

Description

Print method for Functions

Usage

## S3 method for class 'Functions'
print(x, ...)

Arguments

x

Functions, an object class generated by function_from_edge or function_from_user functions.
...

Additional arguments are ignored in this method, but are included to maintain consistency with the generic print method.

Value

A summary of vertices that has functions. If there are vertices without functions, an instruction is shown.

Examples

data(edges)
functions <- function_from_edge(edges)
print(functions)

Generate time-varying data

Description

Generate time-varying data

Usage

time_varying(func, data, T_max)

Arguments

func

Functions, an object class generated by function_from_edge or function_from_user functions. All vertices must be defined for their functions. The causal structure needs to be a directed cyclic graph (DCG), which means loops are allowed. Use edge_from_function to identify edges given a list of functions, then draw a causal diagram using the edges data frame (see vignettes). All arguments within any function must be defined by their respective functions, except the argument 'n'. The output lengths of vertex functions must match the input length.
data

Data, a data frame generated by data_from_function which contains causally-simulated data at t=0. Column names of 'i', 't', and 't_max' are not allowed, which respectively refer to instance, time, and maximum time.
T_max

Maximum time for every instance, a numeric vector of length equal to the number of rows in 'data' and must be non-negative and non-decimal.

Value

A data frame which include the simulated data for each vertex as a column for each time up to maximum time for every instance.

Examples

data(functions)
simulated_data <- data_from_function(functions, n = 100)

function_B <- function(B){
  B + 1
}

functions <- define(functions, which = "B", what = function_B)
T_max <- rpois(nrow(simulated_data), lambda = 25)

time_varying(functions, data = simulated_data, T_max = T_max)