| Title: | Generate Causally-Simulated Data |
| Version: | 0.1.1 |
| 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 athttps://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html. |
| Depends: | R (≥ 4.2) |
| Imports: | dplyr, magrittr, purrr, tidyr, igraph |
| Suggests: | broom, ggnetwork, ggpubr, kableExtra, knitr, rmarkdown,testthat (≥ 3.0.0), tidyverse |
| VignetteBuilder: | knitr |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.1 |
| LazyData: | true |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2024-06-22 02:42:13 UTC; herdiantrisufriyana |
| Author: | Herdiantri Sufriyana [aut, cre], Emily Chia-Yu Su [aut] |
| Maintainer: | Herdiantri Sufriyana <herdi@tmu.edu.tw> |
| Repository: | CRAN |
| Date/Publication: | 2024-06-24 15:30:09 UTC |
Generate causally-simulated data
Description
Generate causally-simulated data
Usage
data_from_function(func, n)Arguments
func | Functions, an object class generated by |
n | Number of observations, a numeric of length 1, non-negative, andnon-decimal. |
Value
A data frame which include the simulated data for each vertex as acolumn.
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 |
which | Which, a character of length 1 indicating a vertex name forwhich function is defined. The vertex name must be defined in 'Functions'. |
what | What, a function to be defined. It must use all and onlythe specified arguments for the vertex in 'Functions', if not previouslydefined. |
Value
A list of either functions or character vectors of arguments forfunction. It can be continuously defined or redefined by a user usingdefine function. If all elements of the list are functions, then itcan 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 |
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 thisorder. A vertex name 'n' does not exist.
Usage
edgesFormat
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 bedefined by a user usingdefine 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. Thelist must be non-empty. All elements of the list must be named. All elementsof 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 simulateddata, or redefined by a user usingdefine 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 byfunction_from_edgeorfunction_from_user functions. The causal structure is a directedacyclic graph (DAG), which means no loops are allowed. A function in the listinclude 'n' as the only argument. All arguments within any function aredefined by their respective functions, except the argument 'n'. The outputlengths of vertex functions match the specified length 'n'.
Usage
functionsFormat
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 |
... | Additional arguments are ignored in this method, but are includedto maintain consistency with the generic print method. |
Value
A summary of vertices that has functions. If there are verticeswithout 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 |
data | Data, a data frame generated by |
T_max | Maximum time for every instance, a numeric vector of lengthequal to the number of rows in 'data' and must be non-negative andnon-decimal. |
Value
A data frame which include the simulated data for each vertex as acolumn 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)