-
Notifications
You must be signed in to change notification settings - Fork 13
/
Copy pathREADME.Rmd
228 lines (173 loc) · 8.67 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
---
output: github_document
bibliography: inst/REFERENCES.bib
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
library(goldfish)
```
# goldfish <img src="man/figures/logo.png" align="right" width="150"/>
<!-- badges: start -->
[](https://github.com/stocnet/goldfish/actions/workflows/R-CMD-check.yml)
[](https://app.codecov.io/gh/stocnet/goldfish?branch=main)
[](https://www.bestpractices.dev/projects/4563)
<!-- badges: end -->
<!-- [](https://www.codefactor.io/repository/github/stocnet/goldfish) -->
## Description
The `{goldfish}` package offers a collection of tools designed for applying
statistical models to dynamic network data.
It primarily focus on models for relational event data, namely,
sequences of interactions between actors or entities within a network,
enriched by fine-grained time-stamps information.
Relational event data emerge in various domains,
such as automatically collected data about interactions in
communication and social media research,
social science studies using social sensors,
and archival network studies that provide in-depth details regarding
the timing or sequence of relational actions between nodes.
Currently, the package includes the following models:
- **Dynamic Network Actor Models (DyNAM)**: Investigate relational event models
as an actor-oriented decision process [@stad2017interaction].
- *rate*: Actors compete for creating the next relational event.
- *choice*: The active actor choose the receiver of the event.
- *choice_coordination*: The creation of coordination ties as a two-sided
process [@Stadtfeld2017Coordination]
- **Dynamic Network Actor Models for interactions (DyNAMi)**:
Investigate dynamics of conversation groups and interpersonal interaction in
different social contexts from an actor-oriented perspective
[@Hoffman2020].
- *rate*: Actors compete for joining or leaving groups.
- *choice*: The active actor choose the group to join.
- **Relational Event Models (REM)**: Investigate relational event models
as a tie-oriented process [@butts2008relational].
### Vignettes
For detailed documentation on each model, including usage examples, users are
encouraged to consult the package's vignettes and help files:
- [Getting Started with goldfish (DyNAM and
REM)](https://stocnet.github.io/goldfish/articles/teaching1.html)
- [Coordination ties (DyNAM-choice
coordination)](https://stocnet.github.io/goldfish/articles/teaching2.html)
- [Face to face interactions
(DyNAMi)](https://stocnet.github.io/goldfish/articles/dynami-example.html)
- [Catalog of available
effects](https://stocnet.github.io/goldfish/articles/goldfishEffects.html)
## Table of Contents
- [Installation](#installation)
+ [Installing OpenMP on Mac OSX](#installing-openmp-on-mac-osx)
- [Usage](#usage)
+ [Define data objects and link events](#define-data-objects-and-link-events)
+ [Define dependent events](#define-dependent-events)
+ [Model specification and estimation](#model-specification-and-estimation)
- [About](#about)
- [References](#references)
## Installation
You can install `{goldfish}` directly from
[CRAN](https://cran.r-project.org/package=goldfish):
``` r
install.packages("goldfish")
```
To install the development version from GitHub, use the
[remotes](https://cran.r-project.org/package=remotes) package:
- For latest stable version:
`remotes::install_github("stocnet/goldfish", build_vignettes = TRUE)`
- For latest development version:
`remotes::install_github("stocnet/goldfish@develop", build_vignettes = TRUE)`
Or by downloading and install the latest binary releases for all major OSes
-- Windows, Mac, and Linux -- can be found
[here](https://github.com/stocnet/goldfish/releases).
### Installing OpenMP on Mac OSX
In some cases, you may get an error that does not allow installation of
`{goldfish}` from source on Mac OSX versions, including under R 4.0.0.
The error may relate to compiling the parts of `{goldfish}` that are written
in C++, or whether OpenMP (for parallelisation) can be found.
Many installation woes can be solved by directing R to use [Homebrew](https://brew.sh) installed `gcc`.
An updated setting up instructions thanks to \@timonelmer are available [here](https://github.com/stocnet/goldfish/blob/main/.github/OpenMP_mac.md).
More details can be found [here](https://medium.com/biosyntax/following-up-library-dependency-when-compiling-r-packages-89f191b9f227) (Thank you \@Knieps for identifying this.).
Other links that may be helpful include:
- https://asieira.github.io/using-openmp-with-r-packages-in-os-x.html
- https://thecoatlessprofessor.com/programming/cpp/r-compiler-tools-for-rcpp-on-macos/
- https://ryanhomer.github.io/posts/build-openmp-macos-catalina-complete
- https://pat-s.me/transitioning-from-x86-to-arm64-on-macos-experiences-of-an-r-user/
Please share feedback on which of these work and we will update the installation guide accordingly.
## Usage
Below is a quick-start guide to using the `{goldfish}` package.
The dataset used in this example is an abbreviated version of the
MIT Social Evolution data (`?Social_Evolution`).
### Define data objects and link events
The main data objects required for the analysis are the node set(s)
`defineNodes()` and network(s) `defineNetwork()`.
The node set object contains labels and attributes of the actors in the network.
In contrast, a network object contains the information of past relational events
between actors.
By default, `defineNetwork()` constructs an empty matrix, its dimensions
defined by the length of the nodeset(s).
Data frames containing event data that modify these data objects can be
linked to them using the `linkEvents()` method.
```{r}
library(goldfish)
data("Social_Evolution")
callNetwork <- defineNetwork(nodes = actors, directed = TRUE) |> # 1
linkEvents(changeEvent = calls, nodes = actors) # 2
```
The events data frame, which indicates the time-varying attributes in
the node set, contains the following columns:
- `time`: The time when the attribute changes,
either a `numeric` or `POSIXct` value.
- `node`: The node for which the attribute changes, a `character` value
that matches the `label` variable in the node set.
- `replace`: The new value of the attribute, a `numeric` value.
The events data frame that details the relational events between actors
contains the following columns:
- `time`: The time when the event occurred,
either a `numeric` or `POSIXct` value.
- `sender`: The actor initiating the event, a `character` value
that matches the `label` variable in the node set.
- `receiver`: The actor receiving the event, a `character` value
that matches the `label` variable in the node set.
- `increment` or `replace`: A `numeric` value indicating either the increment
that the relational event represents or the new value.
### Define dependent events
The final step in defining the data objects is to identify the dependent events.
Here we would like to model as the dependent variable the calls
between individuals.
We specify the event data frame and the node set.
```{r}
callsDependent <- defineDependentEvents(
events = calls, nodes = actors,
defaultNetwork = callNetwork
)
```
### Model specification and estimation
We specify our model using the standard R formula format like:
`goldfish_dependent ~ effects(process_state_element)`
We can see which effects are currently available and how to specify them here:
```r
vignette("goldfishEffects")
```
Now to estimate this model, we use the `?estimate` function.
```{r}
mod00Rate <- estimate(
callsDependent ~ indeg + outdeg,
model = "DyNAM", subModel = "rate"
)
summary(mod00Rate)
mod00Choice <- estimate(
callsDependent ~ inertia + recip + trans,
model = "DyNAM", subModel = "choice"
)
summary(mod00Choice)
```
## About
This project is a joint collaboration between the **Social Networks Lab at ETH Zürich** and the **Graduate Institute Geneva**,
and incorporates and supports several sub-projects.
## References