22_MISSINGchapters.Rmd

# CHAPTERS to APPEAR

# Latent Factor Models

There are many....

# LPLS

L-PLS - include reference. 

# Confirmatory Factor Analysis using lavaan

## Example - Food Neophobia

# Structured Equation Modelling

## Example - Theory of Planned Behaviour

# PLSDA on CATA and liking

[This needs more love. Skal vente til 2023! hvordan får vi det ud af bogen så?]

```{r}
library(caret)
mdl <- plsda(data.frame(beercata[,3:29]),factor(beercata$Beer),ncomp = 3)

scores <- mdl$scores %>% 
  unclass %>% 
  as.data.frame %>% 
  cbind(beercata)

loadings <- mdl$loadings %>% 
  unclass %>% 
  as.data.frame %>% 
  rownames_to_column('attrib') %>% 
  mutate(attrib2 = substr(attrib,3,50)) # lets remove the S_

g1 <- ggplot(data = loadings, aes(`Comp 1`, `Comp 2`, label = attrib2)) + 
  # geom_point() + 
  geom_text()

g2 <- ggplot(data = scores, aes(`Comp 1`, `Comp 2`, color = Beer)) + 
  # geom_point() + 
  stat_ellipse(level = 0.5)

library(patchwork)
g1 + g2


# do multiple splithalfs
# INPUT: judge id. CATA, class, ncomp
X <- beercata[,3:29]
clss <- factor(beercata$Beer)
judge <- beercata$Consumer.ID
k <- 3
A <- 30

mdl0 <- plsda(X,clss,ncomp = k)
lds0 <- mdl0$loadings %>% 
  unclass %>% 
  as.data.frame %>% 
  rownames_to_column('attrib') %>% 
  gather(cmp,val0,-attrib) 

unjudge <- unique(judge)
nindiv <- length(unjudge)

LOADS <- data.frame()
for (i in 1:A){
  ic <- judge %in% sample(unjudge)[1:round(nindiv/2)]
  mdlSH <- plsda(X[ic,],clss[ic],ncomp = k)
  df_flip <- data.frame(sng = sign(diag(t(mdl0$loadings) %*% mdlSH$loadings))) %>% 
    rownames_to_column('cmp')
  lds <- mdlSH$loadings %>% 
    unclass %>% 
    as.data.frame %>% 
    rownames_to_column('attrib') %>% 
    gather(cmp,val,-attrib) %>% 
    left_join(df_flip, by = 'cmp') %>% 
    mutate(SHiter = i, 
           val = val*sng)  
  
  LOADS <- bind_rows(LOADS,lds)
}
fc <- (1 / A)*((A - 1)/A)
sdloads <- LOADS %>% 
  left_join(lds0, by = c('attrib','cmp')) %>% 
  group_by(attrib,cmp) %>% 
  dplyr::summarise(sd = sum((val-val0)^2) *fc) %>% 
  mutate(cmp = paste('sd',cmp,sep = '')) %>% 
  spread(cmp,sd)

loadsSH <- lds0 %>% 
  spread(cmp,val0) %>% 
  left_join(sdloads, by = 'attrib')

library(ggforce)
ggplot(data = loadsSH, aes(x0 = `Comp 1`,y0 = `Comp 2`,a = `sdComp 1`,b = `sdComp 2`,angle = 0)) + geom_ellipse()
```


```{r}

```


# Text mining of comsumer reviews

# Text mining of open-ended survey responses