---
title: "PLS Training"
output: html_notebook
---

PLS:


```{r}
#detach("package:MASS","plsdof") # to avoid conflict with dplyr::select
library(tidyverse)
library(pls)

## 1. load sample data
drinks <- read.csv("http://wiki.q-researchsoftware.com/images/d/db/Stacked_colas.csv")
#str(drinks)

## 2. clean data (remove brand and URLID)
drinks <- drinks %>% 
  select(-URLID, -brand)

## 3. use cross validatation to find the optimal number of dimensions
pls.model = plsr(pref ~ ., data = drinks, validation = "CV")

## 3.1. find the model with lowest cv error
cv <- RMSEP(pls.model)
best_dims <- which.min(cv$val[estimate = "adjCV", , ]) - 1

## 4. rebuild the model
pls.model <- plsr(pref ~ ., data = drinks, ncomp = best_dims)

## 5. Sort, and visualize top coefficients
coefs <- coef(pls.model)

barplot(tail(sort(coefs[,1,1])))
```