🧪 first pass at some quantitative stuff

_freeze/notebooks/model_quantitative/execute-results/html.json

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+    "markdown": "---\ntitle: Quantitative approach to topology generators\nauthor: Tanya Strydom\nbibliography: ../references.bib\n---\n\n\nI think in the long run I will probably turn the other repo ([BecksLab/topology_generators](https://github.com/BecksLab/topology_generators)) into a project that we can then access and I will port all of the working code here... I think it will just come down to what the 'cost' is of the computational side of things and how convoluted it might end up looking.\n\nAnyway so for now I am just pulling in the INTERIM data from some of the models. This uses (for now) 20 mangal [@poisotMangalMakingEcological2016] datasets and just generates some networks for using the niche and cascade models. The 'results' for now is just counting the number of links each model generates for each network because that's what my brain decided on doing...\n\n\n::: {.cell .hidden}\n\n```{.r .cell-code .hidden}\n#| include: false\n#| echo: false\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr .hidden}\n\n```\n── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──\n✔ dplyr     1.1.4     ✔ readr     2.1.5\n✔ forcats   1.0.0     ✔ stringr   1.5.1\n✔ ggplot2   3.4.4     ✔ tibble    3.2.1\n✔ lubridate 1.9.3     ✔ tidyr     1.3.1\n✔ purrr     1.0.2     \n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag()    masks stats::lag()\nℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n```\n\n\n:::\n\n```{.r .cell-code .hidden}\n#| include: false\n#| echo: false\n# download file from other repo\ndownload.file(\"https://raw.githubusercontent.com/BecksLab/topology_generators/main/data/topology_summary.csv\", \"notebooks/data/topology_models.csv\")\n```\n\n::: {.cell-output .cell-output-stderr .hidden}\n\n```\nWarning in\ndownload.file(\"https://raw.githubusercontent.com/BecksLab/topology_generators/main/data/topology_summary.csv\",\n: URL\nhttps://raw.githubusercontent.com/BecksLab/topology_generators/main/data/topology_summary.csv:\ncannot open destfile 'notebooks/data/topology_models.csv', reason 'No such file\nor directory'\n```\n\n\n:::\n\n::: {.cell-output .cell-output-stderr .hidden}\n\n```\nWarning in\ndownload.file(\"https://raw.githubusercontent.com/BecksLab/topology_generators/main/data/topology_summary.csv\",\n: download had nonzero exit status\n```\n\n\n:::\n\n```{.r .cell-code .hidden}\n#| include: false\n#| echo: false\ndf = read.csv(\"data/topology_models.csv\")\n```\n:::\n\n\nLets just do a simple little plot where we plot real vs model observed\n\n\n::: {#cell-fig-topology .cell}\n\n```{.r .cell-code .hidden}\n#| echo: false\n#| label: fig-topology\n#| fig-cap: \"Real vs observed values for network summary statistics\"\n#| fig-alt: \"TODO\"\nggplot() +\n    geom_abline(slope = 1,\n                intercept = 0) +\n    geom_point(data = df,\n                aes(x = links,\n                    y = niche_links),\n                fill = \"blue\",\n                shape = 21,\n                alpha = 0.7,\n                size = 5) +\n    geom_point(data = df,\n                aes(x = links,\n                    y = cascade_links),\n                fill = \"green\",\n                shape = 23,\n                alpha = 0.7,\n                size  = 5) +\n    theme_bw()\n```\n\n::: {.cell-output-display}\n![Real vs observed values for network summary statistics](model_quantitative_files/figure-html/fig-topology-1.png){#fig-topology fig-alt='TODO' width=672}\n:::\n:::\n\n\n## References {.unnumbered}\n\n::: {#refs}\n:::",
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_freeze/notebooks/model_quantitative/execute-results/xml.json

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+    "markdown": "---\ntitle: Quantitative approach to topology generators\nauthor: Tanya Strydom\nbibliography: ../references.bib\n---\n\n\n\n\nI think in the long run I will probably turn the other repo ([BecksLab/topology_generators](https://github.com/BecksLab/topology_generators)) into a project that we can then access and I will port all of the working code here... I think it will just come down to what the 'cost' is of the computational side of things and how convoluted it might end up looking.\n\nAnyway so for now I am just pulling in the INTERIM data from some of the models. This uses (for now) 20 mangal [@poisotMangalMakingEcological2016] datasets and just generates some networks for using the niche and cascade models. The 'results' for now is just counting the number of links each model generates for each network because that's what my brain decided on doing...\n\n\n\n\n::: {.cell .hidden}\n\n```{.r .cell-code .hidden}\n#| include: false\n#| echo: false\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr .hidden}\n\n```\n── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──\n✔ dplyr     1.1.4     ✔ readr     2.1.5\n✔ forcats   1.0.0     ✔ stringr   1.5.1\n✔ ggplot2   3.4.4     ✔ tibble    3.2.1\n✔ lubridate 1.9.3     ✔ tidyr     1.3.1\n✔ purrr     1.0.2     \n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag()    masks stats::lag()\nℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n```\n\n\n:::\n\n```{.r .cell-code .hidden}\n#| include: false\n#| echo: false\n# download file from other repo\ndownload.file(\"https://raw.githubusercontent.com/BecksLab/topology_generators/main/data/topology_summary.csv\", \"notebooks/data/topology_models.csv\")\n```\n\n::: {.cell-output .cell-output-stderr .hidden}\n\n```\nWarning in\ndownload.file(\"https://raw.githubusercontent.com/BecksLab/topology_generators/main/data/topology_summary.csv\",\n: URL\nhttps://raw.githubusercontent.com/BecksLab/topology_generators/main/data/topology_summary.csv:\ncannot open destfile 'notebooks/data/topology_models.csv', reason 'No such file\nor directory'\n```\n\n\n:::\n\n::: {.cell-output .cell-output-stderr .hidden}\n\n```\nWarning in\ndownload.file(\"https://raw.githubusercontent.com/BecksLab/topology_generators/main/data/topology_summary.csv\",\n: download had nonzero exit status\n```\n\n\n:::\n\n```{.r .cell-code .hidden}\n#| include: false\n#| echo: false\ndf = read.csv(\"data/topology_models.csv\")\n```\n:::\n\n\n\n\nLets just do a simple little plot where we plot real vs model observed\n\n\n\n\n::: {#cell-fig-topology .cell}\n\n```{.r .cell-code .hidden}\n#| echo: false\n#| label: fig-topology\n#| fig-cap: \"Real vs observed values for network summary statistics\"\n#| fig-alt: \"TODO\"\nggplot() +\n    geom_abline(slope = 1,\n                intercept = 0) +\n    geom_point(data = df,\n                aes(x = links,\n                    y = niche_links),\n                fill = \"blue\",\n                shape = 21,\n                alpha = 0.7,\n                size = 5) +\n    geom_point(data = df,\n                aes(x = links,\n                    y = cascade_links),\n                fill = \"green\",\n                shape = 23,\n                alpha = 0.7,\n                size  = 5) +\n    theme_bw()\n```\n\n::: {.cell-output-display}\n![Real vs observed values for network summary statistics](model_quantitative_files/figure-jats/fig-topology-1.png){#fig-topology fig-alt='TODO'}\n:::\n:::\n\n\n\n\n## References {.unnumbered}\n\n::: {#refs}\n:::",
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index.qmd

@@ -92,7 +92,7 @@ At some point we are going to need to discuss the key differences and implicatio
 
 **Trait-based** [@caronAddressingEltonianShortfall2022]:
 
-**Graph embedding** [@strydomFoodWebReconstruction2022; @strydomGraphEmbeddingTransfer2023]: At a high level graph embedding focuses on capturing the structural data of a network as opposed to a list of pairwise (*i.e.,* mechanistic) interactions. Here specifically the embedding is preformed on a known interaction network and captures information as to where species (nodes) are positioned in a network *e.g.,* are they basal prey species or top predators, similar to the log ratio model. In  @strydomFoodWebReconstruction2022 the products of the embedding process are fed into a transfer leanring framework for novel prediction...
+**Graph embedding** [@strydomFoodWebReconstruction2022; @strydomGraphEmbeddingTransfer2023]: At a high level graph embedding focuses on capturing the structural data of a network as opposed to a list of pairwise (*i.e.,* mechanistic) interactions. Here specifically the embedding is preformed on a known interaction network and captures information as to where species (nodes) are positioned in a network *e.g.,* are they basal prey species or top predators, similar to the log ratio model. In  @strydomFoodWebReconstruction2022 the products of the embedding process are fed into a transfer learning framework for novel prediction...
 
 I know tables are awful but in this case they may make more sense. Also I don't think I'm at the point where I can say that the table is complete/comprehensive but it getting there Not sure about putting in some papers that have used the model - totes happy to drop those I think...
 
@@ -164,6 +164,12 @@ How we want to compare and contrast. I think there won't be a 'winner' and thus
 
 {{< embed notebooks/model_qualitative.qmd#fig-venn >}}
 
+### Quantitative stuff
+
+{{< embed notebooks/model_quantitative.qmd#fig-topology >}}
+
+This is actually an awful way to try and summarise the data but rolling with it for now...
+
 ## Discussion
 
 I think a big take home will (hopefully) be how different approaches do better in different situations and so you as an end user need to take this into consideration and pick accordingly. I think @petcheyFitEfficiencyBiology2011 might have (and share) some thoughts on this (thanks Andrew). I feel like I need to look at @berlowGoldilocksFactorFood2008 but maybe not exactly in this context but vaguely adjacent.

notebooks/data/topology_models.csv

@@ -0,0 +1,11 @@
+id,richness,links,connectance,niche_links,cascade_links
+19,1327,9334,0.005300611211468492,9572,9292
+12,101,150,0.014704440741103813,182,152
+13,54,92,0.03155006858710562,99,100
+18,29,38,0.04518430439952437,43,30
+15,280,950,0.01211734693877551,929,970
+3259,20,50,0.125,58,56
+908,43,63,0.03407247160627366,75,58
+23,19,20,0.055401662049861494,24,28
+909,100,192,0.0192,208,186
+946,96,343,0.03721788194444445,368,363

notebooks/model_quantitative.qmd

@@ -0,0 +1,53 @@
+---
+title: Quantitative approach to topology generators
+author: Tanya Strydom
+bibliography: ../references.bib
+---
+
+I think in the long run I will probably turn the other repo ([BecksLab/topology_generators](https://github.com/BecksLab/topology_generators)) into a project that we can then access and I will port all of the working code here... I think it will just come down to what the 'cost' is of the computational side of things and how convoluted it might end up looking.
+
+Anyway so for now I am just pulling in the INTERIM data from some of the models. This uses (for now) 20 mangal [@poisotMangalMakingEcological2016] datasets and just generates some networks for using the niche and cascade models. The 'results' for now is just counting the number of links each model generates for each network because that's what my brain decided on doing...
+
+```{r}
+#| include: false
+#| echo: false
+library(tidyverse)
+
+# download file from other repo
+download.file("https://raw.githubusercontent.com/BecksLab/topology_generators/main/data/topology_summary.csv", "notebooks/data/topology_models.csv")
+
+df = read.csv("data/topology_models.csv")
+```
+
+Lets just do a simple little plot where we plot real vs model observed
+
+```{r}
+#| echo: false
+#| label: fig-topology
+#| fig-cap: "Real vs observed values for network summary statistics"
+#| fig-alt: "TODO"
+ggplot() +
+    geom_abline(slope = 1,
+                intercept = 0) +
+    geom_point(data = df,
+                aes(x = links,
+                    y = niche_links),
+                fill = "blue",
+                shape = 21,
+                alpha = 0.7,
+                size = 5) +
+    geom_point(data = df,
+                aes(x = links,
+                    y = cascade_links),
+                fill = "green",
+                shape = 23,
+                alpha = 0.7,
+                size  = 5) +
+    theme_bw()
+
+```
+
+## References {.unnumbered}
+
+::: {#refs}
+:::