---
# System prepended metadata

title: 'Meta-analysis: Modeling (Part 3)'

---

# Meta-analysis: Modeling (Part 3)


1. Pull in data from data extraction. 

....


### Taxonomic data 

Pulled taxanomic data from rfishbase. Using this code: 
```
library(rfishbase)
library(dplyr)
rfishbase::load_taxa() %>% 
  filter(Species %in% clean.dat2$Species.name) %>%
 collect() -> tax.data

dplyr::rename(tax.data, Species.name = Species) %>%  # join taxonomic data to working dataframe
  left_join(clean.dat2,tax.data,by="Species.name") ->
  clean.dat3
```

Now that all data is merged, let's do some exploring on the effect of taxanomic status. 

#### Models

Ran models in single exploratory variables along with full additive and interactive model. 

```
t0 <- glmmTMB(data=clean.dat3, lnrr~1 + (1 | ID)+ (1 | Order))
t1 <- glmmTMB(data=clean.dat3, lnrr~Length + (1 | ID)+ (1 | Order))
t2 <- glmmTMB(data=clean.dat3, lnrr~RepGuild1 + (1 | ID)+ (1 | Order))
t3 <- glmmTMB(data=clean.dat3, lnrr~FoodTroph + (1 | ID)+ (1 | Order))
t4 <- glmmTMB(data=clean.dat3, lnrr~Length + RepGuild1 + FoodTroph + (1 | ID) + (1 | Order))
t5 <- glmmTMB(data=clean.dat3, lnrr~ Length * RepGuild1 * FoodTroph + (1 | ID) + (1 | Order))
t6 <- glmmTMB(data=clean.dat3, lnrr~ Length + RepGuild1 + FoodTroph + (1 | ID:Order)) # nested Order within Study ID as a random effect
t7 <- glmmTMB(data=clean.dat3, lnrr~ Length * RepGuild1 * FoodTroph + (1 | ID:Order)) # nested Order within Study ID as a random effect
```



Calculate confidence intervals from model: 

Here, we aim to calculate confidence intervals of predictions made from a glmmTMB model, specifically intersted in taxonomic status.




- Example of how it can be done: [Pore et al. paper](https://onlinelibrary.wiley.com/doi/full/10.1111/j.1461-0248.2012.01804.x) 
    - To obtain CIs, authors used *REML estimates of fixed effects and their 95% confidence intervals were derived from the linear mixed models and used in all figures. Or, in other words, the means and 95% confidence intervals are derived from linear mixed models of log response ratio against predictor variables*
- Note: can we [bootstrap](https://www.alexejgossmann.com/bootstap_confidence_intervals/)? 



### Model Diagnostics

#### Resources
- [Regression diagnostics](https://data.princeton.edu/wws509/notes/c2s9)


#### DHARMa

Posts that are helpful: 
- Blog post from Florian's website-- [Theorietical Ecology](https://theoreticalecology.wordpress.com/2016/08/28/dharma-an-r-package-for-residual-diagnostics-of-glmms/)
- [R Documentation](https://www.rdocumentation.org/packages/DHARMa/versions/0.4.1)
- [CRAN vinette](https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html)
- [GitHub](http://florianhartig.github.io/DHARMa/)
- [Reference Manuel](https://cran.r-project.org/web/packages/DHARMa/DHARMa.pdf)

Notes: 
- Quantile tests: 
    - Quantile regression: Model quantiles of the conditional distribution of a variable (instead of the conditional mean).
    - Quantile regression does not make distributional assumptions, i.e., assumptions about residuals, other than assuming that the response variable is almost continuous.
    - More info: https://people.umass.edu/biep640w/pdf/diagnosticsStudentNotes.pdf