-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME.Rmd
78 lines (61 loc) · 2.86 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
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# `fitlandr`: Fit Vector Fields and Potential Landscapes from Intensive Longitudinal Data <img src='man/figures/logo.png' align="right" height="138" />
<!-- badges: start -->
[](https://cran.r-project.org/package=fitlandr)
[](https://cran.r-project.org/package=fitlandr)
[](https://github.com/Sciurus365/fitlandr/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
A toolbox for estimating vector fields from intensive
longitudinal data, and construct potential landscapes thereafter. The
vector fields can be estimated with two nonparametric methods: the
Multivariate Vector Field Kernel Estimator (MVKE) by Bandi & Moloche
(2018) <https://doi.org/10.1017/S0266466617000305> and the Sparse Vector Field
Consensus (SparseVFC) algorithm by Ma et al. (2013)
<https://doi.org/10.1016/j.patcog.2013.05.017>. The potential landscapes can be
constructed with a simulation-based approach with the `simlandr`
package (Cui et al., 2021) <https://doi.org/10.31234/osf.io/pzva3>, or the Bhattacharya et al. (2011) method for path integration
<https://doi.org/10.1186/1752-0509-5-85>.
## Installation
You can install the development version of `fitlandr` from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("Sciurus365/fitlandr")
```
## Example
We use the following bistable dynamic system to illustrate the use of `fitlandr`. The test data set is created as follows.
```{r message=FALSE}
single_output_grad <- simlandr::sim_fun_grad(length = 200, seed = 1614)
library(tidyverse)
ggplot(data = single_output_grad %>% as_tibble()) +
geom_path(aes(x = 1:200, y = x), color = "blue") +
geom_path(aes(x = 1:200, y = y), color = "red") +
theme_bw()
```
Fit the vector field with MVKE (see `?MVKE` for the explanations of parameters):
```{r}
library(fitlandr)
v2 <- fit_2d_vf(single_output_grad, x = "x", y = "y", method = "MVKE")
plot(v2)
```
Fit the potential landscape:
```{r warning=FALSE}
future::plan("multisession")
set.seed(1614)
l2 <- fit_3d_vfld(v2, .sim_vf_options = sim_vf_options(chains = 16, stepsize = 1, forbid_overflow = TRUE), .simlandr_options = simlandr_options(adjust = 5, Umax = 4))
plot(l2, 2)
# equivalent:
# s2 <- sim_vf(v2, chains = 16, stepsize = 1, forbid_overflow = TRUE)
# l2 <- simlandr::make_3d_static(s2, x = "x", y = "y", lims = v2$lims, adjust = 5, Umax = 4)
```