README.Rmd

---
date: "`r format(Sys.time(), '%d/%m, %Y')`"
output:
  bookdown::github_document2:
    fig_caption: true
    toc: true
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = F,
  warning = F
)
```

# AICcPermanova

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The goal of AICcPermanova is to evaluate the best models for plant communities and bacterial communities in Denmark in order to do that we require the following packages

```{r LoadPackages}
library(ampvis2)
library(readxl)
library(tidyverse)
library(AICcPermanova)
```

# Vegetation models

## Presence-absence data

### Generation of all possible models

After that we read in the datasets for environmental layers and generate all possible models to fit, in this case we will limit ourselves to only using at most one variable per ten observations, in this case that means up to 5 variables per model. The code for generating all possible models can be expanded bellow

<details style=\"margin-bottom:10px;\">
<summary>Model generator</summary>
```{r ModelGenerator, cache = T}
METADATAS <- list.files(pattern = "PERMANOVA_VEGETATION_", full.names = T) |> 
  purrr::map(read_excel) |> 
  purrr::reduce(full_join) |> 
  janitor::clean_names()

AllForms <- AICcPermanova::make_models(vars = c("p_h_water", "ec", "water_content", "wr", "ammonium", "nitrat_nitrit", 
"oc_beregnet", "clay", "fine_silt", "coarse_silt_sand", "shannon_bak", 
"finesilt_and_clay", "dexter_n")
, ncores = 21, k = floor(nrow(METADATAS)/10))

saveRDS(AllForms, "AllForms.rds")
openxlsx::write.xlsx(AllForms, "AllForms.xlsx")
```
</details>

This generate up to `r prettyNum(nrow(AllForms), big.mark = ",")` models to evaluate, which can be downloaded as an excel file [here](https://github.com/Sustainscapes/AICcPermanova/raw/master/AllForms.xlsx) an rds [here](https://github.com/Sustainscapes/AICcPermanova/blob/master/AllForms.rds).



## Avoiding colinearity

Now before the model fitting, we will filter out models with high degree of multicolinearity using VIF with 5 as a cut off

<details style=\"margin-bottom:10px;\">
<summary>VIF filter</summary>
```{r colinearityfilter3, cache =TRUE}
Filtered <- AICcPermanova::filter_vif(all_forms = AllForms , env_data = METADATAS, ncores = 21)
```
</details>

This is then narrowed down to `r prettyNum(nrow(Filtered), big.mark = ",")` models to evaluate

### Model fitting

Then in the following code each model is fitted and AICc is calculated to order it

<details style=\"margin-bottom:10px;\">
<summary>Model fitting code</summary>
```{r ModelFitting, cache = T}
vegetation_data = read_excel("Presence_absence_vegetation_AC_Danielsen.xlsx")%>% 
    janitor::clean_names()

vegetation_data_no_ID = subset(vegetation_data, select = -plot)

FS <- AICcPermanova::fit_models(all_forms = Filtered, 
                 env_data = METADATAS, 
                 veg_data = vegetation_data_no_ID, 
                 ncores = 21, 
                 method = "jaccard",
                logfile = "new.txt",
                strata = "area"
                 )


saveRDS(FS, "FS.rds")
openxlsx::write.xlsx(FS, "FS.xlsx")
```
</details>

As seen in table  \@ref(tab:SummaryPlantPA) there are `r AICcPermanova::select_models(readRDS("FS.rds")) |> nrow()` models within 2 AICc where the max VIF is lower or equal than 6 of each other, you can see there how many times a variable has been selected

```{r SummaryPlantPA, echo = F}

GlobalSummary <- AICcPermanova::select_models(readRDS("FS.rds")) |> AICcPermanova::akaike_adjusted_rsq()

knitr::kable(GlobalSummary, digits = 3, caption = "Number of selected models were variables are present and their Akaike Weighted R squared for the Marginal effect of the terms", format.args	= list(big.mark = ','))
```


Now we can see the top models that have a delta AICc within 2 of the best model in table  \@ref(tab:BestPLantModels) if expanded

<details style=\"margin-bottom:10px;\">
<summary>Show table of selected models for vegetation presence absence</summary>
```{r BestPLantModels, echo=FALSE}
Fs <- readRDS("FS.rds")
Fs  |> AICcPermanova::select_models() |>  
  knitr::kable(digits = 3, caption = "Best models for vegetation presence absence", format.args	= list(big.mark = ','))
```
</details>

## Abundance data

Since the environmental variables are the same, we don´t need to do any of the filtering or model generation again, only the model fitting, based on the filtered data.frame

### Model fitting

Then in the following code each model is fitted and AICc is calculated to order it

<details style=\"margin-bottom:10px;\">
<summary>Model fitting code for vegetation abundance</summary>
```{r ModelFittingVegAbund, cache = T}
vegetation_data = read_excel("Pinpoint-data til ordination_minus_Lønstrup.xlsx")%>% 
    janitor::clean_names()
  # Remocing the first column (ID) in vegetation-dataset and cheking there are no NAs
  
vegetation_data_no_ID = vegetation_data
  
Fs <- AICcPermanova::fit_models(all_forms = Filtered, 
                 env_data = METADATAS, 
                 veg_data = vegetation_data_no_ID, 
                 ncores = 21, 
                 method = "bray",
                logfile = "PlantAbund.txt",
                strata = "area"
                 )

saveRDS(Fs, "FSVegAbund.rds")

Fs <- Fs %>% arrange(AICc)

saveRDS(Fs, "FSVegAbund.rds")
openxlsx::write.xlsx(Fs, "FSVegAbund.xlsx")
```
</details>

As seen in table  \@ref(tab:SummaryVegAbund) there are `r AICcPermanova::select_models(readRDS("FSVegAbund.rds")) |> nrow()` models within 2 AICc of each other, you can see there how many times a variable has been selected


```{r SummaryVegAbund, echo = F}

GlobalSummary <- AICcPermanova::select_models(readRDS("FSVegAbund.rds")) |> AICcPermanova::akaike_adjusted_rsq()

knitr::kable(GlobalSummary, digits = 3, caption = "Number of selected models were variables are present and their Akaike Weighted R squared for the Marginal effect of the terms", format.args	= list(big.mark = ','))
```


Now we can see the top models that have a delta AICc within 2 of the best model in table  \@ref(tab:BestVegAbundModels) if expanded

<details style=\"margin-bottom:10px;\">
<summary>Show table of selected models for vegetation presence absence</summary>
```{r BestVegAbundModels, echo=FALSE}
Fs <- readRDS("FSVegAbund.rds")
Fs  |> AICcPermanova::select_models() |>  
  knitr::kable(digits = 3, caption = "Best models for vegetation abundance", format.args	= list(big.mark = ','))
```
</details>


# Baterial models

## Abundance data

### Generation of all possible models

We read in the datasets for environmental layers and generate all possible models to fit, in this case we will limit ourselves to only using at most one variable per ten observations, in this case that means up to 5 variables per model. The code for generating all possible models can be expanded bellow

<details style=\"margin-bottom:10px;\">
<summary>Model generator bacterial abundance</summary>
```{r ModelGeneratorBacterialAbundance, cache = T}
METADATAS <- list.files(pattern = "PERMANOVA_BACTERIA_", full.names = T) |> 
  purrr::map(read_excel) |> 
  purrr::reduce(full_join) |> 
  janitor::clean_names()

AllForms <- AICcPermanova::make_models(vars = c("p_h_water", "ec", 
"water_content", "wr", "ammonium", "nitrat_nitrit", "oc_beregnet", 
"clay", "fine_silt", "coarse_silt_sand", "shannon_veg", "finesilt_and_clay", 
"dexter_n")
, ncores = 21, k = floor(nrow(METADATAS)/10))
  

saveRDS(AllForms, "AllFormsBacterialAbund.rds")
openxlsx::write.xlsx(AllForms, "AllFormsBacterialAbund.xlsx")
```
</details>

This generate up to `r prettyNum(nrow(AllForms), big.mark = ",")` models to evaluate, which can be downloaded as an excel file [here](https://github.com/Sustainscapes/AICcPermanova/raw/master/AllFormsBacterialAbund.xlsx) an rds [here](https://github.com/Sustainscapes/AICcPermanova/blob/master/AllFormsBacterialAbund.rds).

## Avoiding colinearity

Now before the model fitting, we will filter out models with high degree of multicolinearity using VIF with 5 as a cut off

<details style=\"margin-bottom:10px;\">
<summary>VIF filter</summary>
```{r colinearityfilter2, cache =TRUE}
Filtered <- AICcPermanova::filter_vif(all_forms = AllForms , env_data = METADATAS, ncores = 21)
```
</details>

This is then narrowed down to `r prettyNum(nrow(Filtered), big.mark = ",")` models to evaluate

### Model fitting

Then in the following code each model is fitted and AICc is calculated to order it

<details style=\"margin-bottom:10px;\">
<summary>Model fitting code bacterial abund</summary>
```{r ModelFittingBacterialABund, cache = T}

d <- amp_load(
otutable = "AC_otutale_new.txt",
metadata = "Metadata_mix-samples_AC_Danielsen_final.xlsx")
bac.data.subset = amp_subset_taxa(d, "d__Bacteria")
bacterial_data = amp_subset_samples(d, Investigator == "AC")
bacterial_data = as.data.frame(t(bacterial_data$abund)) %>% 
    janitor::clean_names()
METADATAS <- METADATAS[match(rownames(bacterial_data), METADATAS$seq_id),]

Fs <- AICcPermanova::fit_models(all_forms = Filtered, 
                 env_data = METADATAS, 
                 veg_data = bacterial_data, 
                 ncores = 21, 
                 method = "bray",
                logfile = "BactLog.txt",
                strata = "area"
                 )



saveRDS(Fs, "FSBacterialAbund.rds")
openxlsx::write.xlsx(Fs, "FSBacterialAbund.xlsx")
```
</details>



As seen in table  \@ref(tab:SummaryBacterialAbund) there are `r AICcPermanova::select_models(readRDS("FSBacterialAbund.rds")) |> nrow()` models within 2 AICc of each other, you can see there how many times a variable has been selected


```{r SummaryBacterialAbund, echo = F}

GlobalSummary <- AICcPermanova::select_models(readRDS("FSBacterialAbund.rds")) |> AICcPermanova::akaike_adjusted_rsq()

knitr::kable(GlobalSummary, digits = 3, caption = "Number of selected models were variables are present and their Akaike Weighted R squared for the Marginal effect of the terms", format.args	= list(big.mark = ','))
```


Now we can see the top models that have a delta AICc within 2 of the best model in table  \@ref(tab:BestBacterialAbundModels) if expanded


<details style=\"margin-bottom:10px;\">
<summary>Show table of selected models for vegetation presence absence</summary>
```{r BestBacterialAbundModels, echo=FALSE}
Fs <- readRDS("FSBacterialAbund.rds")
Fs  |> AICcPermanova::select_models() |>  
  knitr::kable(digits = 3, caption = "Best models for bacterial abundance", format.args	= list(big.mark = ','))
```
</details>