In clsong/topodetective: Infer topology of species interactions from time series of species abundance

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topodetective

The goal of topodetective is to infer topology of species interactions from time series of species abundance. We use a regression-based method, which can efficiently enumerate all possible topologies.

Installation

You can install the development version of InferInteractions from GitHub with:

# install.packages("devtools")
devtools::install_github("clsong/InferInteractions")

Example

This is a basic example which shows you how to solve a common problem:

library(tidyverse)
library(topodetective)

get_classic_dynamics("4_species_chaos") # choose a population dynamic
time_range <- seq(0, 50, by = .1)

set.seed(12345)
ts <- generate_time_series_LV(
  topology = topology_ground,
  state_initial = state_initial,
  time_range = time_range,
  noise = T,
  noise_level = .05
) # simulate a dynamic

plot_time_series(ts)

We then infer system parameters from time series alone. We choose all possible topologies.

reg_model <- choose_regression_model("linear") # 'lasso' and 'ridge' are also available

fitted_models <- ts %>%
  differentiate_ts() %>%
  group_split(species) %>%
  map(fit_interaction_parameters) %>%
  bind_rows(.id = 'species') %>%
  mutate(species = paste0("x", species))

We then simulate the dynamics with the inferred parameters. We compare the fit.

set.seed(123)
topology_fitted <- fitted_models %>%
  group_by(species) %>%
  top_n(3, R2) %>% 
  sample_n(1) %>%
  ungroup()

ts_simu <- simualte_fitted_dynamics(topology_fitted)
evaluate_fit(ts, ts_simu)

plot_true_vs_simu(ts, ts_simu)

The fitted topology is different from the true topology.

 library(ggraph)

bind_rows(
  topology_ground %>% 
    mutate(facet = 'Groundtruth'),
  topology_fitted %>% 
    mutate(facet = 'Fitted'),
) %>% 
  plot_interaction_topology(facet = T) 

clsong/topodetective documentation built on Feb. 18, 2022, 2:35 a.m.