---
title: "Monte-Carlo Consistent Partial Least Squares"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Monte-Carlo Consistent Partial Least Squares}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

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

library(plssem)
```

The `plssem` package implements *Monte-Carlo Consistent Partial Least Squares*
(MC-PLSc). This vignette gives an intuitive explanation of the core idea
using a small toy example (a single correlation).

For a more formal definition see
<https://osf.io/preprints/psyarxiv/fwzj6_v1>.

MC-PLSc starts from the observation that PLS can be a biased estimator for
common factor models (e.g., models with reflective measurement), and that
additional bias is introduced when ordinal indicators are treated as continuous.

The key move is to *explicitly model the bias* of the estimator under the
assumed data-generating process, and then adjust parameters until simulated
data reproduces the observed (biased) estimates.

# A Toy Example: Bias From Dichotomization

Consider the correlation $r$ between continuous variables $x$ and $y$.

```{r}
set.seed(7208094)
n <- 10000
r <- 0.6
x <- rnorm(n)
y <- r * x + rnorm(n, sd = sqrt(1 - r^2))
cor(x, y)
```

Let's then say that we only have access to a set of binary representations of
$x$ and $y$, $z$ and $w$. Here we just split $x$ and $y$ in the middle.

```{r}
z <- as.integer(x - mean(x) > 0)
w <- as.integer(y - mean(y) > 0)
```

If we calculate the correlation between $z$ and $w$, we get a biased estimate
of the underlying correlation $r$.

```{r}
cor(z, w)
```

Even though `cor(z, w)` is a biased estimate of $r$, it is still an informative
measure of $r$. The MC-PLSc algorithm formalizes this by:

1. proposing a data-generating model,
2. simulating data under candidate parameters, and
3. searching for parameters that make the simulated estimator match the observed one.

In this toy example:

- `g()` simulates *binary* data given a candidate value of $r$.
- `f()` is the estimator applied to that simulated data (here: `cor()`).

```{r}
g <- function(r_hat, R = 5000) {
  # Generate continuous data
  x <- rnorm(R)
  y <- r_hat * x + rnorm(R, sd = sqrt(1 - r_hat^2))

  # Generate binary data
  z <- as.integer(x - mean(x) > 0)
  w <- as.integer(y - mean(y) > 0)

  # Return a data.frame
  data.frame(z, w)
}

f <- function(df) {
  # Biased correlation between binary data
  cor(df$z, df$w)
}
```

Putting `f()` and `g()` together, we can try different values of $\hat{r}$ and see
which one reproduces the observed `cor(z, w)`.

```{r}
print(f(g(0.0)))
print(f(g(0.3)))
print(f(g(0.5)))
print(f(g(0.6)))
```

The true value (here, $r = 0.6$) gets us close, but in practice we do not know $r$.
So we rephrase this as a *root-finding problem*: find $\hat{r}$ such that the
simulated correlation matches the observed biased one.

```{r}
r_biased_observed <- cor(z, w)
h <- function(r_hat) {
  f(g(r_hat)) - r_biased_observed
}
```

The full MC-PLSc algorithm uses Robbins-Monro (1951) stochastic approximation,
to estimate the root of $h(\hat{r})$. Below we use a simplified approach,
where we iteratively nudge $\hat{r}$ in the direction that reduces the mismatch $h(\hat{r})$.

```{r}
r_hat <- r_biased_observed # reasonable starting value
iter <- 20
history <- r_hat

for (i in seq_len(iter)) {
  temperature <- 1 / sqrt(i)
  epsilon <- h(r_hat)

  cat(sprintf("Iteration: %2d, r_hat: %.3f, epsilon: %6.3f\n", i, r_hat, epsilon))

  r_hat <- r_hat - temperature * epsilon
  history <- c(history, r_hat)
}
```

The sequence typically stabilizes near the underlying continuous correlation.
To visualize the stabilization more clearly, we can fit a simple exponential
curve to the iteration history and plot the fitted trajectory. Here we can
see that we seem to approach a very good approximation of $r$.

```{r echo=FALSE}
t <- seq_along(history)

fit <- stats::nls(
  history ~ c + a * exp(-k * t),
  start = list(
    c = mean(utils::tail(history, 3)),
    a = history[1] - mean(utils::tail(history, 3)),
    k = 0.1
  ),
  algorithm = "port",
  lower = c(c = -Inf, a = -Inf, k = 0)
)

fpredict <- function(t) {
  stats::predict(fit, newdata = data.frame(t = t))
}

df <- data.frame(t = t, r_hat = history)

ggplot2::ggplot(df, ggplot2::aes(x = t, y = r_hat)) +
  ggplot2::geom_hline(
    yintercept = r,
    linetype = 2,
    linewidth = 0.8,
    color = "grey40"
  ) +
  ggplot2::geom_function(
    fun = fpredict,
    linewidth = 1.2,
    color = "#0072B2"
  ) +
  ggplot2::geom_point(size = 2, color = "#D55E00") +
  ggplot2::labs(
    x = "Iteration",
    y = "r_hat",
    title = "Convergence (with exponential fit)"
  ) +
  ggplot2::theme_minimal()
```

# From The Toy Example to MC-PLSc

This toy example has the same basic structure as MC-PLSc:

- `g()` becomes a simulator for your SEM (measurement + structural model,
  including the indicator scales, e.g., ordinal thresholds).
- `f()` becomes "run PLS under the same estimation settings as for the real
  data, then extract the parameters of interest".

The Monte-Carlo loop then searches for parameters that are *consistent* with
the observed PLS estimates under the assumed data model.

Here we can see the corresponding estimates using the MC-PLSc algorithm, via the
`pls()` function.

```{r}
set.seed(2346259)
pls('y ~ x', data = data.frame(x = z, y = w),
    ordered = c("y", "x"), mcpls = TRUE)
```
