07-EnsembleApproach.Rmd

# MBL Ensemble Approach
To make more robust predictions with measures of uncertainty, we use an ensemble approach with MBL. We run 20 mbl models, with different modeling parameters, so each samples has a distribution of predictions. We can then calculate the median prediction, as well as upper and lower confidence bounds.

## Model Iterations {-}
Model runs vary on 3 variables...

1. _How similarity is determined between samples_ **5 options**
- euclid
- cosine
- cor
- pc
- pls

2. _Whether the similarity matrix is used as a predictor variable or not_ **2 options**
- predictors
- none

3. _What modeling method is used for making local predictions_ **2 options**
- pls
- wapls

**5 x 2 x 2 = 20 model combinations**

```{r, echo=FALSE, message=FALSE, warning=FALSE}
library(readr)
t <- read_csv("Soil-Predictions-Ensemble-Example/para.grid.csv")
knitr::kable(
  data.frame(t), booktabs=TRUE,caption="Model Combinations for Ensemble Approach"
)
```


## Getting started {-}
The best way to get started using this code, is by downloading the `Soil-Predictions-Ensemble-Example` folder found here:    
[**Soil-Predictions-Ensemble-Example Folder**](https://github.com/whrc/Soil-Predictions-MIR/tree/master/Soil-Predictions-Ensemble-Example)

This folder, along with all source code for this guide, can be found in the following Github Repository:    
[**whrc/Soil-Predictions-MIR**](https://github.com/whrc/Soil-Predictions-MIR)


### File Walkthrough {-}
`setname_prep.R`

Performs the calibration transfer on the spectra and saves as RData file in ‘spc’ folder
Change the input csv file, the columns being selected as spectra (lines 11-12), and output name/location

`setname_oc.R`

Submit as a job through cloudops, creates all the mbl models with different parameter combinations, to output/oc folder
Change input validation and calibration sets (line 32-38), property (oc) throughout the file, output location (line 107) and create output folder for soil property

`setname-fratio.R`

Calculates the fratio for all samples in the calibration and validation sets and outputs a list of outlier indices from the combined dataset. Ex: calibration set indices are 1-15000, validation set indices are from 15001-15240
Change input calibration and validation spectra (5-6 and throughout), number of directories in line 8 as needed, output location- currently ‘fratio’ subfolder.

`setname-extract.R`

Creates comprehensive files containing all predictions for each mbl model by property. (ie. pred.oc.csv, pred.bd.csv)
Creates a file containing the lower, mean and upper prediction estimates for each property across all models (all-predictions.csv)

**Note:** _The calibration set spc.oc.RData, and the transfer matrix pls.moving.w2k.RData- called in the code- were both too large to be hosted in this repository_