---
title: "LUCID's Three Model Architectures: Early, Parallel, and Serial -- Continuous Outcome (HELIX Example)"
output:
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 3
vignette: >
  %\VignetteIndexEntry{LUCID's Three Model Architectures: Early, Parallel, and Serial -- Continuous Outcome (HELIX Example)}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## 1) Tutorial Goal and Scope

This tutorial is designed as a **hands-on, end-to-end guide** for fitting LUCID models on HELIX-style multi-omics data.

What you will learn:

- How to prepare common inputs (`G`, `Z`, `Y`, `CoG`, `CoY`) from one dataset.
- How to fit and interpret:
  - **LUCID early integration**
  - **LUCID in parallel**
  - **LUCID in serial** (all-early stages and mixed parallel+early stages)
- How to run the practical two-step strategy:
  1. Penalized screening fit (`Rho_* > 0`) for selection.
  2. Zero-penalty refit (`Rho_* = 0`) for stable inference + bootstrap CI.

Important runtime note:

- Sections 7-10 fix `K` and do the screen-then-refit by hand, so the steps are
  visible. Section 13 then shows `lucid()` doing the same thing natively, with
  tuning over a small `K` and penalty grid.
- We keep `K` fixed and use small bootstrap `R` so the vignette remains reproducible and fast.

## 2) Data Objects and Statistical Roles

The HELIX example data (`simulated_HELIX_data.rda`) provides:

- `phenotype`: exposure/covariate/outcome-style variables.
- three omics layers: `methylome`, `transcriptome`, `miRNA`.

Model inputs used throughout:

- `G`: exposure matrix (main predictors of latent cluster assignment). Built with
  a known signal structure -- three exposures correlated with the outcome and six
  pure-noise exposures -- so that feature selection can be checked against the
  truth rather than taken on trust. The outcome itself is deliberately kept out
  of `G`.
- `Z`: omics input (matrix for early, list for parallel, nested list for serial mixed).
- `Y`: continuous outcome vector; `Y_binary` is its median split, used to
  demonstrate the binary outcome model on the same subjects.
- `CoG`: covariates for `G -> X` model.
- `CoY`: covariates for `X -> Y` model.

## 3) Hyperparameter Guide (Practical)

| Hyperparameter | Meaning | Tutorial choice and rationale |
|---|---|---|
| `K` | number of latent clusters | fixed to small values for speed and interpretability |
| `Rho_G` | penalty on `G -> X` coefficients | positive in screening fit, zero in inference refit |
| `Rho_Z_Mu` | penalty on cluster-specific omics means | positive in screening fit, zero in inference refit |
| `Rho_Z_Cov` | penalty on omics covariance matrices | positive in screening fit, zero in inference refit |
| `max_itr`, `max_tot.itr`, `tol` | EM controls | modest values to balance speed/stability |
| `family` | outcome model family | `normal` for this tutorial |
| `R` (in `boot_lucid()`) | number of bootstrap resamples | kept tiny (2-5) here for speed; use hundreds for a real analysis |
| `seed` | reproducibility | fixed before each fit/bootstrap |

## 4) Setup and Source Package Code

```{r setup, message=FALSE, warning=FALSE}
# Keep knitting on error, so that one failing step reports itself and the rest of
# the tutorial still runs. The status table in section 17 records what happened.
knitr::opts_chunk$set(error = TRUE)

# Lightweight registry so the document can verify itself rather than relying on
# the reader to notice a missing output.
.reg <- new.env(parent = emptyenv()); .reg$rows <- list()
check_obj <- function(name, expected_class = NULL, section = "") {
  ok <- exists(name, envir = globalenv())
  cls <- if (ok) class(get(name, envir = globalenv()))[1] else NA_character_
  status <- if (!ok) "MISSING"
            else if (!is.null(expected_class) && !identical(cls, expected_class)) "unexpected class"
            else "ok"
  .reg$rows[[length(.reg$rows) + 1L]] <-
    data.frame(section = section, object = name, class = cls,
               status = status, stringsAsFactors = FALSE)
  invisible(NULL)
}

library(LUCIDus)

# The HELIX simulation object bundled with the package.
data(simulated_HELIX_data)
```

## 5) Build Modeling Inputs (With Missingness Injection)

This chunk creates a compact tutorial dataset and deliberately injects both:

- **listwise missingness** (whole row missing for a layer), and
- **sporadic missingness** (some cells missing),

so we can observe missing-data handling in summaries.

```{r data-prep}
# Use a smaller subset for vignette speed while preserving model behavior.
idx <- 1:90
ph <- simulated_HELIX_data$phenotype[idx, ]
n <- nrow(ph)

set.seed(2026)

# ---------------------------------------------------------------------------
# A tutorial dataset with a KNOWN answer.
#
# The HELIX omics matrices are real simulated data with their own structure, and
# the exposures shipped with them have no relationship to it. That is fine for
# demonstrating that code runs, but it makes feature selection impossible to
# judge: there is no right answer to compare against. So we plant one.
#
# The generating story, which is the DAG LUCID assumes:
#
#     causal exposures  ->  latent subgroup  ->  omics profile
#                                            ->  outcome
#
# Three exposures carry the subgroup signal with graded strength; six are pure
# noise. Half the features of each omics layer are shifted by subgroup
# membership; the rest are left as they came. Selection therefore has an
# unambiguous target, and the tutorial can check its answer instead of asserting
# it.
# ---------------------------------------------------------------------------

# The true latent subgroup. Retained so every selection claim below can be
# checked against it.
x_true <- rbinom(n, 1, 0.5)

# Exposures. g_causal_* predict subgroup membership; g_noise_* do not.
# Effect sizes are deliberately moderate. Stronger exposures make selection
# look better but drive the G -> X model to saturation, where every subject sits
# at posterior probability 1 and no counterfactual shift can move anything --
# which would make the g-computation demonstration in section 12.3 vacuous.
G <- cbind(
  g_causal_1 =  1.0 * (x_true - 0.5) + rnorm(n, sd = 0.8),   # strongest
  g_causal_2 = -0.8 * (x_true - 0.5) + rnorm(n, sd = 0.8),   # moderate, negative
  g_causal_3 =  0.6 * (x_true - 0.5) + rnorm(n, sd = 0.8),   # weakest
  g_noise_1 = rnorm(n), g_noise_2 = rnorm(n), g_noise_3 = rnorm(n),
  g_noise_4 = rnorm(n), g_noise_5 = rnorm(n), g_noise_6 = rnorm(n)
)
G <- as.matrix(scale(G))

causal_exposures <- c("g_causal_1", "g_causal_2", "g_causal_3")

# Exposure penalty used throughout. Section 7.2 shows what this value recovers;
# section 11 sweeps it, and sweeps the omics penalty separately.
RHO_G <- 0.05

# Covariates for G->X (CoG) and X->Y (CoY).
# Here we use age-related and sex covariates from phenotype.
CoG <- cbind(
  hs_child_age_yrs_None = as.numeric(ph$hs_child_age_yrs_None),
  sex_male = as.numeric(ph$e3_sex_None == "male")
)
CoY <- CoG

# Two outcomes on the SAME subjects, so the normal and binary results below are
# directly comparable: the only thing that changes between them is the outcome
# model, not the sample, the omics, or the injected missingness.
#
# Continuous outcome: the real CK-18 measurement, plus a subgroup effect so the
# cluster -> outcome arm of the model has something to estimate.
Y <- as.numeric(ph$ck18_scaled) + 1.2 * x_true

# Binary outcome: median split. The median is used rather than a higher
# threshold because it splits these 90 subjects 45/45, and a balanced outcome
# gives the K = 2 outcome model the most to work with at this sample size.
Y_binary <- as.integer(Y > median(Y))
cat("binary outcome balance:\n"); print(table(Y_binary))

# Construct three omics layers and standardize each, then plant the subgroup
# signal in the first three features of every layer. The remaining seven per
# layer are left as they came and act as omics noise.
meth <- scale(simulated_HELIX_data$methylome[idx, 1:10, drop = FALSE])
tran <- scale(simulated_HELIX_data$transcriptome[idx, 1:10, drop = FALSE])
mir  <- scale(simulated_HELIX_data$miRNA[idx, 1:10, drop = FALSE])

signal_features <- 1:3
omics_shift <- 3.0
meth[, signal_features] <- meth[, signal_features] + omics_shift * x_true
tran[, signal_features] <- tran[, signal_features] - omics_shift * x_true
mir[,  signal_features] <- mir[,  signal_features] + omics_shift * x_true

# Column positions of the signal features once the layers are stacked for the
# early model, so selection can be scored against them later.
signal_cols_early <- c(signal_features,
                       ncol(meth) + signal_features,
                       ncol(meth) + ncol(tran) + signal_features)

# Early model uses one combined Z matrix.
Z_early <- cbind(meth, tran, mir)

# Parallel model uses list-of-layers.
Z_parallel <- list(methylome = meth, transcriptome = tran, miRNA = mir)

# Inject listwise + sporadic missingness for demonstration.
Z_early_miss <- Z_early
Z_early_miss[1, ] <- NA      # listwise row
Z_early_miss[2:4, 1] <- NA   # sporadic block
Z_early_miss[5, 3] <- NA     # sporadic cell

Z_parallel_miss <- Z_parallel
Z_parallel_miss[[1]][1, ] <- NA  # listwise in layer 1
Z_parallel_miss[[2]][2, 2] <- NA # sporadic in layer 2
Z_parallel_miss[[3]][3, 1] <- NA # sporadic in layer 3

# Quick structural sanity check.
str(list(
  G = G,
  CoG = CoG,
  CoY = CoY,
  Y = Y,
  Z_early = Z_early_miss,
  Z_parallel = Z_parallel_miss
), max.level = 1)
```

## 6) Helper Functions for Selected-Feature Refit

These helpers implement a robust refit pipeline:

1. Read feature-selection indicators from penalized fit.
2. Build selected-only `G`/`Z` inputs.
3. Refit with all penalties set to zero for bootstrap inference.

```{r helper-functions}
# get_selected_G()/get_selected_Z() (from the package itself) already return a
# well-shaped, aligned logical mask straight from the fitted object -- no
# length mismatch is possible, since they derive it from the model's own
# recorded fields. The one thing left for a tutorial to decide is what to do
# if a penalty happened to deselect EVERY feature: refitting on zero columns
# would fail, so this keeps everything instead in that one edge case.
keep_or_all <- function(mask) if (any(mask, na.rm = TRUE)) mask else rep(TRUE, length(mask))

# Build selected-only inputs for early model.
prepare_early_selected_inputs <- function(fit_pen, G, Z) {
  list(
    G = as.matrix(G[, keep_or_all(get_selected_G(fit_pen)), drop = FALSE]),
    Z = as.matrix(Z[, keep_or_all(get_selected_Z(fit_pen)), drop = FALSE])
  )
}

# Build selected-only inputs for parallel model.
prepare_parallel_selected_inputs <- function(fit_pen, G, Z) {
  keep_g <- keep_or_all(get_selected_G(fit_pen))
  Z_sel <- lapply(seq_along(Z), function(i) {
    zi <- as.matrix(Z[[i]])
    zi[, keep_or_all(get_selected_Z(fit_pen, layer = i)), drop = FALSE]
  })
  names(Z_sel) <- names(Z)
  list(
    G = as.matrix(G[, keep_g, drop = FALSE]),
    Z = Z_sel
  )
}

# Serial stage>1 uses latent-cluster-derived "G" internally.
# We therefore subset stage-1 original G and each stage's Z where applicable.
prepare_serial_selected_inputs <- function(fit_pen, G, Z) {
  G_refit <- as.matrix(G)
  keep_g1 <- get_selected_G(fit_pen)
  if (length(keep_g1) == ncol(G_refit)) {
    G_refit <- G_refit[, keep_or_all(keep_g1), drop = FALSE]
  }

  selected_z <- get_selected_Z(fit_pen)
  Z_refit <- Z
  for (i in seq_along(fit_pen$submodel)) {
    sm <- fit_pen$submodel[[i]]
    if (inherits(sm, "early_lucid")) {
      zi <- as.matrix(Z_refit[[i]])
      Z_refit[[i]] <- zi[, keep_or_all(selected_z[[i]]), drop = FALSE]
    } else if (inherits(sm, "lucid_parallel")) {
      zi_list <- Z_refit[[i]]
      for (j in seq_along(zi_list)) {
        zij <- as.matrix(zi_list[[j]])
        zi_list[[j]] <- zij[, keep_or_all(selected_z[[i]][[j]]), drop = FALSE]
      }
      Z_refit[[i]] <- zi_list
    }
  }

  list(G = G_refit, Z = Z_refit)
}

# Zero-penalty refit, for any model type.
#
# The three model types previously had three byte-identical wrappers differing
# only in `lucid_model` and whether `useY` was forwarded; they are one function
# here. Everything about the model -- family, K, initialization, EM controls --
# is carried over from the screening fit, so the ONLY difference between the
# screening fit and this one is that the penalties are zero. That is what makes
# the refit estimates unshrunk and therefore suitable for bootstrap inference.
refit_selected <- function(model_type, fit_pen, inputs, Y,
                           CoG = NULL, CoY = NULL, seed = 1, verbose = FALSE) {
  args <- list(
    lucid_model = model_type,
    G = inputs$G,
    Z = inputs$Z,
    Y = Y,
    CoG = CoG,
    CoY = CoY,
    family = fit_pen$family,
    K = fit_pen$K,
    init_omic.data.model = fit_pen$init_omic.data.model,
    init_impute = fit_pen$init_impute,
    init_par = fit_pen$init_par,
    Rho_G = 0,
    Rho_Z_Mu = 0,
    Rho_Z_Cov = 0,
    max_itr = fit_pen$em_control$max_itr,
    max_tot.itr = fit_pen$em_control$max_tot.itr,
    tol = fit_pen$em_control$tol,
    seed = seed,
    verbose = verbose
  )
  # Every fitted class records useY, so it is carried over for all three model
  # types. The original three wrappers omitted it on the early path, which meant
  # an unsupervised screening fit would have been silently refitted supervised.
  args$useY <- fit_pen$useY
  do.call(estimate_lucid, args)
}

# Dispatcher for the three input-preparation helpers above.
prepare_selected_inputs <- function(model_type, fit_pen, G, Z) {
  switch(model_type,
    early    = prepare_early_selected_inputs(fit_pen, G, Z),
    parallel = prepare_parallel_selected_inputs(fit_pen, G, Z),
    serial   = prepare_serial_selected_inputs(fit_pen, G, Z),
    stop("unknown model_type: ", model_type)
  )
}

# Compact stage-wise feature-selection report for serial fits, built entirely
# from get_selected_G()/get_selected_Z() -- no per-stage dispatch of its own.
serial_selection_report <- function(fit_serial_pen) {
  selected_z <- get_selected_Z(fit_serial_pen)
  out <- vector("list", length(fit_serial_pen$submodel))
  for (i in seq_along(fit_serial_pen$submodel)) {
    sm <- fit_serial_pen$submodel[[i]]
    if (inherits(sm, "early_lucid")) {
      out[[i]] <- list(
        stage = i,
        model = "early",
        selected_G = if (i == 1) sum(get_selected_G(fit_serial_pen)) else NA,
        total_G = if (i == 1) length(get_selected_G(fit_serial_pen)) else NA,
        selected_Z = sum(selected_z[[i]]),
        total_Z = length(selected_z[[i]])
      )
    } else {
      out[[i]] <- list(
        stage = i,
        model = "parallel",
        selected_G = if (i == 1) sum(get_selected_G(fit_serial_pen)) else NA,
        total_G = if (i == 1) length(get_selected_G(fit_serial_pen)) else NA,
        selected_Z_by_layer = sapply(selected_z[[i]], sum),
        total_Z_by_layer = sapply(selected_z[[i]], length)
      )
    }
  }
  out
}
```

## 7) Early Model Tutorial

### 7.1 Penalized screening fit (`Rho_* > 0`)

Interpretation target:

- Which exposures/omics features survive shrinkage?
- Is missing-data handling reported as expected?

```{r early-penalized-fit, warning=FALSE}
set.seed(1101)

# Screening model: penalties help identify a parsimonious subset.
early_fit_pen <- estimate_lucid(
  lucid_model = "early",
  G = G,
  Z = Z_early_miss,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  family = "normal",
  K = 2,
  Rho_G = RHO_G,
  Rho_Z_Mu = 0,
  Rho_Z_Cov = 0,
  max_itr = 15,
  max_tot.itr = 40,
  tol = 1e-2,
  seed = 1101,
  verbose = FALSE
)

# Summary shows model fit, missing profile, selection, and parameter tables.
summary(early_fit_pen)
```

### 7.1b Object structure: what's in the fitted object

`summary()` above is a printed report; `early_fit_pen` itself is a plain R
list, and every field on it is documented on `?estimate_lucid`'s `@return`.
This is what an early-integration fit actually holds:

```{r early-object-structure}
names(early_fit_pen)
```

The fields fall into two groups:

- **Common to every model type** (early, parallel, serial all have these,
  though the shape differs): `res_Beta`/`res_Mu`/`res_Sigma`/`res_Gamma` (the
  G->X, X->Z, X->Y estimates), `likelihood`, `select` (feature-selection
  indicators), `inclusion.p` (posterior cluster membership), `K`,
  `var.names`, `family`, `useY`, `Z`, `Rho` (the penalties applied),
  `missing_summary`, `em_control` (convergence diagnostics), `init_impute`,
  `init_par`.
- **Early-specific**: none of the remaining fields are early-only -- `N` (the
  sample size) is present for parallel/serial but not early (use
  `nrow(fit$inclusion.p)` instead); `z`, `res_Delta`, and `submodel` belong
  to parallel and serial only, shown in sections 8 and 9.

```{r early-object-fields}
cat("log-likelihood:", early_fit_pen$likelihood, "\n")
cat("exposures selected:", sum(get_selected_G(early_fit_pen)),
    "of", length(get_selected_G(early_fit_pen)), "\n")
str(early_fit_pen$em_control, max.level = 1)
```

### 7.2 Inspect selected features

```{r early-selected-features}
early_selected_G <- names(which(get_selected_G(early_fit_pen)))

cat("exposures retained:", paste(early_selected_G, collapse = ", "), "\n\n")

# Score the selection against the planted truth from section 5: the three
# g_causal_* exposures drive the latent subgroup, the six g_noise_* do not.
data.frame(
  exposure = colnames(G),
  truth    = ifelse(colnames(G) %in% causal_exposures, "causal", "noise"),
  selected = colnames(G) %in% early_selected_G
)

cat("\ncausal exposures kept :", sum(causal_exposures %in% early_selected_G), "of 3\n")
cat("noise  exposures kept :",
    sum(!(early_selected_G %in% causal_exposures)), "of 6\n")

# The omics side is unpenalized in this screening fit, so every feature is
# retained. Section 11.3 penalizes the omics separately and shows why.
cat("omics features retained:", sum(get_selected_Z(early_fit_pen)),
    "of", length(get_selected_Z(early_fit_pen)), "(Rho_Z_Mu = 0 here)\n")
```

### 7.3 Zero-penalty selected-only refit

Why this step:

- We keep the selected feature space,
- then estimate final coefficients without shrinkage bias,
- and use this model as the bootstrap target.

```{r early-selected-refit, warning=FALSE}
set.seed(1102)

# Build selected-only inputs.
early_inputs_refit <- prepare_early_selected_inputs(early_fit_pen, G, Z_early_miss)

# Refit with all penalties set to zero.
early_fit_refit <- refit_selected(
  "early",
  fit_pen = early_fit_pen,
  inputs = early_inputs_refit,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  seed = 1102,
  verbose = TRUE
)

summary(early_fit_refit)
```

Compare this summary against the penalized fit's in 7.1: the coefficients on
the selected exposures/features should be of similar sign and rough
magnitude (the model didn't change what it found, only stopped shrinking
it), while any exposure/feature that was zeroed out in the screening step is
now simply absent from the model rather than penalized toward zero. This
refit -- not the penalized screening fit -- is the model bootstrapped next.

### 7.4 Bootstrap CI + summary

```{r early-bootstrap, warning=FALSE}
set.seed(1103)

# Bootstrap on zero-penalty refit for CI inference.
early_boot <- boot_lucid(
  G = early_inputs_refit$G,
  Z = early_inputs_refit$Z,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  model = early_fit_refit,
  R = 30,
  conf = 0.90
)

# Print summary with CI columns integrated into parameter tables.
summary(early_fit_refit, boot.se = early_boot)
```

Each coefficient table above now carries a normal-theory confidence interval,
an odds-ratio view (`OR`, `OR_lower`, `OR_upper` -- the `exp()` of the
coefficient-scale columns), and the `sig` column: `"*"` marks a row whose
interval excludes 0, i.e. an effect the bootstrap distinguishes from no effect
at all. `R = 30` here keeps the vignette fast; a real analysis needs `R` in the
hundreds, as noted in section 3's hyperparameter guide. A poorly identified
coefficient -- the exposure-model intercept when the clusters separate cleanly,
or a covariate nearly collinear with cluster membership --
can still show a very wide or off-centre interval; that is the bootstrap being honest,
not a bug.

## 8) Parallel Model Tutorial

### 8.1 Penalized screening fit

Interpretation target:

- Layer-specific cluster structure and missingness,
- exposure selection union/per-layer behavior,
- layer-specific omics selection.

```{r parallel-penalized-fit, warning=FALSE}
set.seed(1201)

parallel_fit_pen <- estimate_lucid(
  lucid_model = "parallel",
  G = G,
  Z = Z_parallel_miss,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  family = "normal",
  K = c(2, 2, 2),
  Rho_G = RHO_G,
  Rho_Z_Mu = 0,
  Rho_Z_Cov = 0,
  max_itr = 15,
  max_tot.itr = 40,
  tol = 1e-2,
  seed = 1201,
  verbose = FALSE
)

summary(parallel_fit_pen)
```

### 8.1b Object structure: what's in the fitted object

```{r parallel-object-structure}
names(parallel_fit_pen)
```

Compared to the early fit in section 7.1b, two fields are new and two
common fields change shape:

- **`N`** (sample size) is present here but not for early.
- **`z`** is the joint E-step responsibility array across all layers, before
  it is marginalized into `inclusion.p` -- present for parallel only; nothing
  else in the package reads it, so it is shown here purely for completeness.
- **`select`** gains layer structure: `selectG_layer` (per-layer exposure
  selection, alongside the `selectG` union already seen for early), and
  `selectZ` is now a list, one entry per omics layer.
- **`res_Beta`/`res_Mu`/`res_Sigma`/`inclusion.p`** are lists indexed by
  layer rather than single matrices.

```{r parallel-object-fields}
cat("log-likelihood:", parallel_fit_pen$likelihood, "\n")
cat("sample size (N):", parallel_fit_pen$N, "\n")
cat("exposures selected per layer:\n")
print(sapply(seq_along(parallel_fit_pen$K), function(i) sum(get_selected_G(parallel_fit_pen, layer = i))))
```

### 8.2 Inspect selected features (union + per-layer)

```{r parallel-selected-features}
# Exposure selection union across layers.
parallel_selected_G_union <- names(which(get_selected_G(parallel_fit_pen)))

# Exposure selection per layer.
parallel_selected_G_layer <- lapply(seq_along(parallel_fit_pen$K), function(i) {
  names(which(get_selected_G(parallel_fit_pen, layer = i)))
})

# Omics selection per layer (get_selected_Z already collapses any
# vector/matrix selection encoding to one logical value per feature).
parallel_selected_Z_layer <- lapply(get_selected_Z(parallel_fit_pen), function(s) names(which(s)))

parallel_selected_G_union
parallel_selected_G_layer
parallel_selected_Z_layer
```

### 8.3 Zero-penalty selected-only refit

```{r parallel-selected-refit, warning=FALSE}
set.seed(1202)

parallel_inputs_refit <- prepare_parallel_selected_inputs(parallel_fit_pen, G, Z_parallel_miss)
parallel_fit_refit <- refit_selected(
  "parallel",
  fit_pen = parallel_fit_pen,
  inputs = parallel_inputs_refit,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  seed = 1202,
  verbose = TRUE
)

summary(parallel_fit_refit)
```

Unlike the early refit in 7.3, this summary prints one `G -> X` and one
`X -> Z` block **per layer** -- each layer's cluster variable is its own
model, refit unpenalized on that layer's own selected features.

### 8.4 Bootstrap CI + summary

```{r parallel-bootstrap, warning=FALSE}
set.seed(1203)

parallel_boot <- boot_lucid(
  G = parallel_inputs_refit$G,
  Z = parallel_inputs_refit$Z,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  model = parallel_fit_refit,
  R = 30,
  conf = 0.90
)

summary(parallel_fit_refit, boot.se = parallel_boot)
```

As in 7.4, the `sig` column flags rows whose normal-theory interval excludes
0 in each layer's coefficient table; with layer-specific cluster variables,
a feature can be significant in one layer's table and not another's, since
each layer's bootstrap resamples and refits that layer's own model.

## 9) Serial Model Tutorial A: All-Early Stages

Serial model logic here:

- Stage 1: early on methylome
- Stage 2: early on transcriptome
- Stage 3: early on miRNA

Each stage uses previous latent information as designed by the serial pipeline.

### 9.1 Penalized screening fit

```{r serial-all-early-penalized, warning=FALSE}
set.seed(1301)

# Serial structure: list of early-stage matrices.
Z_serial_all_early <- list(
  methylome = Z_parallel_miss[[1]],
  transcriptome = Z_parallel_miss[[2]],
  miRNA = Z_parallel_miss[[3]]
)

serial_all_early_pen <- estimate_lucid(
  lucid_model = "serial",
  G = G,
  Z = Z_serial_all_early,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  family = "normal",
  K = list(2, 2, 2),
  Rho_G = RHO_G,
  Rho_Z_Mu = 0,
  Rho_Z_Cov = 0,
  max_itr = 15,
  max_tot.itr = 40,
  tol = 1e-2,
  seed = 1301,
  verbose = FALSE
)

summary(serial_all_early_pen)
serial_selection_report(serial_all_early_pen)
```

### 9.1b Object structure: what's in the fitted object

```{r serial-object-structure}
names(serial_all_early_pen)
```

Two fields are serial-only, beyond what early/parallel already showed:

- **`submodel`**: the fitted stage models themselves, one per stage, each a
  complete `early_lucid` or `lucid_parallel` object in its own right (with
  its own `res_Beta`, `select`, `likelihood`, etc.) -- explored below.
- **`res_Delta`**: the between-stage transition coefficients, one element
  per transition (`n_stages - 1` of them), structured like `res_Beta` for
  the stage that estimated it.

`likelihood` and `select` at the top level are present for serial too, the
same as early/parallel -- but unlike those two, a serial fit has no single
joint EM loop, so both are aggregates over stages rather than values from
one fit:

```{r serial-object-fields}
cat("top-level likelihood (sum over stages):", serial_all_early_pen$likelihood, "\n")
cat("per-stage likelihoods:",
    paste(round(sapply(serial_all_early_pen$submodel, `[[`, "likelihood"), 2),
          collapse = ", "), "\n")
cat("n_stages:", length(serial_all_early_pen$submodel), "\n")
```

The fit's top-level `select` is **stage 1's own selection only**, not one
combined across all three stages. That is deliberate: stage 1 is the only
stage whose "G" is the cohort's actual exposures. From stage 2 onward, "G" is
the *previous* stage's latent cluster-membership probabilities -- there are no
real exposures left to select among there, so `Rho_G` is forced to 0 for every
stage after the first regardless of what penalty was requested, and that
stage's `select$selectG` is not a meaningful exposure-selection result. The
complete per-stage record (including every stage's real omics selection,
`selectZ`) is always available via `serial_selection_report()` above, or
directly at `fit$submodel[[i]]$select`.

```{r serial-all-early-select, warning=FALSE}
# Top-level select == stage 1's select
identical(serial_all_early_pen$select, serial_all_early_pen$submodel[[1]]$select)

cat("Stage 1 exposures kept:",
    paste(names(which(get_selected_G(serial_all_early_pen))), collapse = ", "), "\n")

# Rho_G is 0 from stage 2 on -- there is nothing there to select
sapply(serial_all_early_pen$submodel, function(sm) sm$Rho$Rho_G)
```

### 9.2 Zero-penalty selected-input refit

```{r serial-all-early-refit, warning=FALSE}
set.seed(1302)

serial_all_early_inputs_refit <- prepare_serial_selected_inputs(
  serial_all_early_pen, G, Z_serial_all_early
)
serial_all_early_refit <- refit_selected(
  "serial",
  fit_pen = serial_all_early_pen,
  inputs = serial_all_early_inputs_refit,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  seed = 1302,
  verbose = TRUE
)

summary(serial_all_early_refit)
```

As with the other architectures, this refit's coefficients are unshrunk
versions of the screening fit's; a serial model additionally reports one
`X -> Z`/`G -> X` block per stage, plus `res_Delta`, the transition
coefficients linking each stage's cluster to the next stage's.

### 9.3 Bootstrap CI + summary

```{r serial-all-early-bootstrap, warning=FALSE}
set.seed(1303)

serial_all_early_boot <- boot_lucid(
  G = serial_all_early_inputs_refit$G,
  Z = serial_all_early_inputs_refit$Z,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  model = serial_all_early_refit,
  R = 30,
  conf = 0.90
)

summary(serial_all_early_refit, boot.se = serial_all_early_boot)
```

Each stage's coefficient tables get their own `sig` markers, independently
of the other stages -- a stage-2 effect being significant says nothing about
stage 3, since each stage's bootstrap resamples and refits that stage's own
conditional model.

## 10) Serial Model Tutorial B: Mixed Parallel + Early

Serial mixed architecture used here:

- Stage 1: **parallel** submodel (methylome + transcriptome together)
- Stage 2: **early** submodel (miRNA)

### 10.1 Penalized screening fit

```{r serial-mixed-penalized, warning=FALSE}
set.seed(1401)

# Nested list signals a parallel submodel at stage 1, followed by early stage 2.
Z_serial_mixed <- list(
  list(
    methylome = Z_parallel_miss[[1]],
    transcriptome = Z_parallel_miss[[2]]
  ),
  miRNA = Z_parallel_miss[[3]]
)

serial_mixed_pen <- estimate_lucid(
  lucid_model = "serial",
  G = G,
  Z = Z_serial_mixed,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  family = "normal",
  K = list(list(2, 2), 2),
  Rho_G = RHO_G,
  Rho_Z_Mu = 0,
  Rho_Z_Cov = 0,
  max_itr = 15,
  max_tot.itr = 40,
  tol = 1e-2,
  seed = 1401,
  verbose = FALSE
)

summary(serial_mixed_pen)
serial_selection_report(serial_mixed_pen)
```

### 10.2 Zero-penalty selected-input refit

```{r serial-mixed-refit, warning=FALSE}
set.seed(1402)

serial_mixed_inputs_refit <- prepare_serial_selected_inputs(serial_mixed_pen, G, Z_serial_mixed)
serial_mixed_refit <- refit_selected(
  "serial",
  fit_pen = serial_mixed_pen,
  inputs = serial_mixed_inputs_refit,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  seed = 1402,
  verbose = FALSE
)

summary(serial_mixed_refit)
```

Stage 1's block here is itself a parallel-model summary (one `G -> X`/`X -> Z`
per layer, as in section 8), nested inside the serial report; stage 2's block
looks like the single-layer early summary from section 7. This is the same
mix-and-match structure `estimate_lucid()` fit -- the summary just mirrors it.

### 10.3 Bootstrap CI + summary

```{r serial-mixed-bootstrap, warning=FALSE}
set.seed(1403)

serial_mixed_boot <- boot_lucid(
  G = serial_mixed_inputs_refit$G,
  Z = serial_mixed_inputs_refit$Z,
  Y = Y,
  CoG = CoG,
  CoY = CoY,
  model = serial_mixed_refit,
  R = 30,
  conf = 0.90
)

summary(serial_mixed_refit, boot.se = serial_mixed_boot)
```

With this, every architecture the package supports (early, parallel, a
serial chain of early stages, and a serial chain mixing a parallel stage
with an early stage) has been fit, refit unpenalized, bootstrapped, and
summarized with significance markers -- the full pipeline this tutorial set
out to demonstrate.

## 11) Missing-Data Handling: Diagnostics and Verification

Sections 7-10 injected missingness and let `summary()` display the resulting
profile. That shows the model *noticed* the missing values; it does not show it
handled them correctly. This section checks the handling.

LUCID classifies every subject, per omics layer, into one of three states:

| code | state | meaning |
|---|---|---|
| 1 | complete | every feature observed |
| 2 | sporadic | some features observed, some missing |
| 3 | listwise | no feature observed for that layer |

The distinction matters: **imputation is triggered by sporadic subjects only**.
A subject missing an entire layer has no within-layer information to condition
on, so the omics term drops out of that subject's likelihood and they contribute
through the exposure and outcome arms instead. Inventing values for them would
be fabricating data.

```{r missing-diagnostics}
na_early <- check_na(Z_early_miss, lucid_model = "early")

cat("subjects by missingness code (1 complete, 2 sporadic, 3 listwise):\n")
print(table(na_early$indicator_na))

cat("\nimpute_flag (TRUE only when sporadic subjects exist):",
    na_early$impute_flag, "\n")
```

`analyze_missing_pattern()` gives the per-feature and per-subject view. The
count of *distinct* patterns is the number worth watching: the observed-data
likelihood is evaluated once per distinct missingness pattern, so pattern
diversity, not sparsity alone, is what drives fitting cost.

```{r missing-pattern}
pat <- analyze_missing_pattern(Z_early_miss)

cat("distinct missingness patterns :", pat$n_patterns, "\n")
cat("complete subjects             :", pat$n_complete, "\n")
cat("overall proportion missing    :", round(pat$total_missing, 4), "\n")
```

### 11.1 Verify the handling, do not just display it

The two claims above are checkable against the fitted object, so we check them
rather than assert them in prose. Recall the injection at section 5: row 1 of
`Z_early` was blanked entirely (listwise), and a few cells in rows 2-5 were
blanked individually (sporadic).

```{r missing-verify}
fitted_Z <- early_fit_pen$Z

# The listwise subject must still be NA after fitting.
stopifnot(all(is.na(fitted_Z[1, ])))

# The sporadic cells must have been filled.
stopifnot(!anyNA(fitted_Z[2:5, ]))

# Observed values must never have been altered.
obs <- !is.na(Z_early_miss)
stopifnot(isTRUE(all.equal(fitted_Z[obs], Z_early_miss[obs])))

cat("verified: listwise row still NA, sporadic cells filled,",
    "observed values unchanged\n")
```

One asymmetry to know about when you go looking for imputed values: `fit$Z`
holds the *fitted* omics for early and parallel models, but for a **serial**
model it holds the omics *as supplied*, because imputation happens inside each
stage. The imputed data for serial stage `i` is at `fit$submodel[[i]]$Z`.

```{r missing-serial-note}
cat("early  fit$Z still has NAs? ", anyNA(early_fit_pen$Z), "\n")
cat("serial fit$Z still has NAs? ", anyNA(serial_all_early_pen$Z[[1]]),
    "  (imputed data lives on submodel[[i]]$Z)\n")
```

### 11.2 Single-value imputation, and scoring it

`safe_impute()` is the cruder alternative: fill each feature with one summary of
its observed values. It is what LUCID uses to *initialize* before the EM
algorithm takes over with model-based imputation, and it is exported so you can
inspect that starting point. `check_imputation_quality()` scores any imputation
by asking whether the filled values have shifted the centre or spread of the
data relative to what was observed.

```{r safe-impute}
Z_mean_filled <- safe_impute(Z_early_miss, method = "mean")

qual <- check_imputation_quality(Z_early_miss, Z_mean_filled)
cat("mean-filled  : valid =", qual$is_valid,
    " mean shift =", round(qual$mean_diff, 3),
    " sd ratio =", round(qual$sd_ratio, 3), "\n")
```

An `sd_ratio` well below 1 is the signature of mean-filling: every imputed value
sits exactly at the centre, so the imputed cells carry no variation. The
model-based imputation inside the EM loop conditions on each subject's *observed*
coordinates and on their cluster, which is why it recovers the withheld values
more accurately than this baseline.

### 11.3 Penalizing the omics: what the two penalties actually do

`Rho_G` and `Rho_Z_Mu` are both called LASSO penalties, but they act on
different parts of the model and they live on **completely different scales**.
This is the single easiest thing to get wrong.

`Rho_G` penalizes the exposure coefficients in the `G -> X` model. Values in the
region of 0.01-0.1 are meaningful, which is why this tutorial uses
`RHO_G = 0.05`.

`Rho_Z_Mu` penalizes the cluster-specific omics **means**, and it is compared
against those means directly. Our signal features are separated by roughly three
standard deviations, so a penalty of 0.02 -- the value this tutorial used before
-- is far too small to threshold anything at all.

```{r penalty-sweep-z, warning=FALSE}
sweep_rho_z <- function(rz) {
  invisible(capture.output(
    f <- estimate_lucid(
      lucid_model = "early", G = G, Z = Z_early_miss, Y = Y, CoG = CoG, CoY = CoY,
      family = "normal", K = 2, init_omic.data.model = "EEV",
      Rho_G = 0, Rho_Z_Mu = rz, Rho_Z_Cov = if (rz > 0) 0.02 else 0,
      max_itr = 30, max_tot.itr = 90, tol = 1e-2, seed = 1101
    )
  ))
  kept <- which(get_selected_Z(f))
  acc <- mean(get_cluster_assignment(f) == x_true + 1); acc <- max(acc, 1 - acc)
  data.frame(
    Rho_Z_Mu       = rz,
    features_kept  = length(kept),
    signal_kept    = sum(kept %in% signal_cols_early),
    converged      = isTRUE(f$em_control$converged),
    subgroup_recovery = round(acc, 2)
  )
}

do.call(rbind, lapply(c(0, 2, 10), sweep_rho_z))
```

Two things to take from that table, and the second is the reason this tutorial
leaves the omics unpenalized.

**The penalty does eventually select.** At 0.02 -- the value this tutorial used
before -- nothing is dropped at all. It takes a penalty two to three orders of
magnitude larger before the feature count moves.

**But look at `signal_kept` as it does.** The penalty is not preferentially
keeping the features that carry the subgroup signal; it drops those alongside
the rest. On this data it is shrinking indiscriminately rather than selecting.

**And it costs cluster recovery.** Watch the
`subgroup_recovery` column against the known `x_true`. Unpenalized, the model
recovers the planted subgroup well above chance. As the omics penalty rises, the
cluster means are shrunk toward each other, the subgroups become harder to tell
apart, and recovery falls back toward 0.5 while the fit stops converging.

That is not a bug; it is the bias-variance trade the penalty exists to make, and
here the variance saving is not worth the bias. Two practical rules follow:

- **Tune `Rho_Z_Mu` on the scale of your omics means**, not by analogy with
  `Rho_G`. The documented working range is roughly 1-100.
- **Check that penalization has not destroyed the structure you are modelling.**
  `em_control$converged` and the cluster sizes are the cheap diagnostics; with
  simulated data you also have the truth, as here.

## 12) Prediction and G-Computation

Fitting is only half of it. `predict_lucid()` assigns clusters and predicts
outcomes, either on new data or -- which is how you extract the fitted cluster
memberships -- on the training data.

```{r predict-training}
# CoG and CoY must be supplied here exactly as they were at fitting time: the
# G -> X design includes the CoG columns, so omitting them makes the design
# matrix too narrow for the fitted coefficients.
# lucid_model is auto-detected from early_fit_refit's own class.
pred_train <- predict_lucid(
  model = early_fit_refit,
  G = early_inputs_refit$G, Z = early_inputs_refit$Z, Y = Y,
  CoG = CoG, CoY = CoY
)

# Predicting on the training data reproduces the fit's own assignment.
stopifnot(identical(
  as.numeric(pred_train$pred.x),
  as.numeric(get_cluster_assignment(early_fit_refit))
))

cat("cluster sizes:\n"); print(table(pred_train$pred.x))
```

Cluster labels run `1..K`. Versions before 3.1.0 returned `0..K-1`, so code
written against the old convention needs its `+ 1` removed.

### 12.1 What you may omit, and why

`predict_lucid()` takes both `Z` and `Y`, and both look optional from the
signature. Only one of them is.

| | `Y` supplied | `Y` omitted |
|---|---|---|
| `Z` supplied | works -- supervised | works -- unsupervised |
| `Z` omitted | **error** | **error** |
| `Z` omitted, `g_computation = TRUE` | works | works |

The asymmetry follows from what the E-step actually does. Cluster membership is
a posterior formed from three likelihood terms: the exposures, the omics, and
(optionally) the outcome. Dropping `Y` removes one term and leaves a perfectly
well-defined posterior over the other two -- that is unsupervised prediction.
Dropping `Z` removes the term the clusters are *defined by*, and there is
nothing left to condition on.

`g_computation = TRUE` is not a way around that. It is a different estimator:
it discards the omics and outcome terms entirely and derives the posterior from
the exposure path alone, which is exactly why it can run with no omics data and
why it is the only mode that returns `pred.z`. Passing `Z` or `Y` to it is
accepted but they are ignored, with a printed notice to that effect.

Rather than take the table on trust, run it:

```{r predict-legality}
legal <- function(expr) {
  r <- try(suppressWarnings(suppressMessages(invisible(capture.output(force(expr))))),
           silent = TRUE)
  if (inherits(r, "try-error")) "error" else "works"
}

Gp <- early_inputs_refit$G
Zp <- early_inputs_refit$Z
call_p <- function(...) predict_lucid(model = early_fit_refit,
                                      G = Gp, CoG = CoG, CoY = CoY, ...)

data.frame(
  Z              = c("supplied", "supplied", "omitted", "omitted", "omitted"),
  Y              = c("supplied", "omitted",  "supplied", "omitted", "omitted"),
  g_computation  = c(FALSE, FALSE, FALSE, FALSE, TRUE),
  result         = c(
    legal(call_p(Z = Zp, Y = Y)),
    legal(call_p(Z = Zp)),
    legal(call_p(Z = NULL, Y = Y)),
    legal(call_p(Z = NULL)),
    legal(call_p(Z = NULL, g_computation = TRUE))
  )
)
```

And this is the message you get if you forget it, which names the one mode that
relaxes the rule:

```{r predict-missing-z-message}
cat(tryCatch(
  call_p(Z = NULL, Y = Y),
  error = function(e) conditionMessage(e)
))
```

Two traps worth knowing before the sections below.

**Pass `CoG` and `CoY` exactly as you fitted them.** The `G -> X` design
includes the `CoG` columns, so omitting them at prediction leaves the design
matrix narrower than the fitted coefficients and you get
`non-conformable arguments` -- an error that says nothing about the actual
mistake.

**A single-stage serial model cannot be predicted.** It fits without complaint,
but a one-stage chain is a fully equivalent early or parallel model, and
prediction declines it with a message saying so. Fit it as the model it is.

### 12.2 Prediction across all three architectures

The rule above applies identically to every model type. Here it is exercised on
the three models this tutorial has already fitted, reporting what was actually
predicted rather than only that the call returned.

```{r predict-all-models, warning=FALSE}
# pred.x nests: a vector for early, a list by layer for parallel, and for serial
# a list by stage whose elements are themselves lists when that stage is a
# parallel submodel. Flatten to the leaves so each cluster variable is counted on
# its own -- pooling a parallel stage's layers would report twice as many
# assignments as there are subjects.
flatten_blocks <- function(x, path = "") {
  if (!is.list(x)) return(stats::setNames(list(as.numeric(x)), path))
  out <- list()
  for (i in seq_along(x)) {
    nm <- if (nzchar(path)) paste0(path, ".", i) else as.character(i)
    out <- c(out, flatten_blocks(x[[i]], nm))
  }
  out
}

cluster_sizes <- function(pred_x, label) {
  blocks <- flatten_blocks(pred_x)
  nms <- names(blocks)
  # index by position: the single block of an early model is named "", and
  # blocks[[""]] does not select anything.
  do.call(rbind, lapply(seq_along(blocks), function(i) {
    tb <- table(factor(blocks[[i]]))
    data.frame(model = label,
               block = if (!nzchar(nms[i])) "-" else nms[i],
               cluster = names(tb), n = as.integer(tb), row.names = NULL)
  }))
}

outcome_summary <- function(pred_y, label) {
  v <- as.numeric(unlist(pred_y))
  data.frame(model = label, n = length(v),
             mean = round(mean(v), 3), sd = round(stats::sd(v), 3),
             min = round(min(v), 3), median = round(stats::median(v), 3),
             max = round(max(v), 3), row.names = NULL)
}

pred_early_m <- predict_lucid(
  model = early_fit_refit,
  G = early_inputs_refit$G, Z = early_inputs_refit$Z, Y = Y,
  CoG = CoG, CoY = CoY
)
pred_parallel_m <- predict_lucid(
  model = parallel_fit_refit,
  G = parallel_inputs_refit$G, Z = parallel_inputs_refit$Z, Y = Y,
  CoG = CoG, CoY = CoY
)
pred_serial_m <- predict_lucid(
  model = serial_all_early_refit,
  G = serial_all_early_inputs_refit$G, Z = serial_all_early_inputs_refit$Z, Y = Y,
  CoG = CoG, CoY = CoY
)

rbind(
  cluster_sizes(pred_early_m$pred.x,    "early"),
  cluster_sizes(pred_parallel_m$pred.x, "parallel (per layer)"),
  cluster_sizes(pred_serial_m$pred.x,   "serial (per stage)")
)
```

Predicted outcomes for the same three models:

```{r predict-all-models-y}
rbind(
  outcome_summary(pred_early_m$pred.y,    "early"),
  outcome_summary(pred_parallel_m$pred.y, "parallel"),
  outcome_summary(pred_serial_m$pred.y,   "serial"),
  outcome_summary(Y,                      "observed outcome")
)
```

Two things are worth reading off that comparison.

**The predicted outcome is much less variable than the observed one.** It is a
posterior-weighted average of the cluster-specific outcome levels, so it carries
no residual variation at all -- only the between-cluster differences. The ratio
of its spread to the observed spread is a rough indication of how much of the
outcome the latent structure explains.

**Cluster sizes are worth checking before anything else.** A block where almost
every subject lands in one cluster is a degenerate solution, and any downstream
interpretation of that layer or stage is unsafe regardless of what the summary
tables report.

### 12.3 Supervised versus unsupervised prediction

Supplying `Y` lets the outcome inform the posterior, exactly as it does during
fitting. Omitting it predicts clusters from exposures and omics alone -- which is
what you want when the outcome is unavailable, or when it must not influence the
assignment.

```{r predict-supervised}
pred_unsup <- predict_lucid(
  model = early_fit_refit,
  G = early_inputs_refit$G, Z = early_inputs_refit$Z,
  CoG = CoG, CoY = CoY
)

cat("agreement between supervised and unsupervised assignment:",
    round(mean(pred_train$pred.x == pred_unsup$pred.x), 3), "\n")
```

### 12.5 G-computation: what if the exposure were different?

G-computation predicts from the exposures alone, dropping the omics and outcome
terms from the E-step. That makes it a counterfactual tool: hold the fitted model
fixed, change `G`, and read off what the model implies. A single call demonstrates
nothing on its own -- the contrast between two exposure scenarios is the point.

Here we shift the strongest signal exposure by one standard deviation in each
direction.

```{r g-computation}
G_ref <- early_inputs_refit$G

# Shift every retained causal exposure by 1.5 SD, each in the direction of its
# own effect, i.e. "what if this whole exposure profile were less favourable?"
kept_causal <- intersect(colnames(G_ref), causal_exposures)
direction <- c(g_causal_1 = 1, g_causal_2 = -1, g_causal_3 = 1)

shift_profile <- function(Gx, delta) {
  for (nm in kept_causal) Gx[, nm] <- Gx[, nm] + delta * direction[[nm]]
  Gx
}

gc_hi <- predict_lucid(model = early_fit_refit,
                       G = shift_profile(G_ref,  1.5), Z = NULL,
                       CoG = CoG, CoY = CoY, g_computation = TRUE)
gc_lo <- predict_lucid(model = early_fit_refit,
                       G = shift_profile(G_ref, -1.5), Z = NULL,
                       CoG = CoG, CoY = CoY, g_computation = TRUE)

data.frame(
  scenario          = c("profile +1.5 SD", "profile -1.5 SD"),
  mean_pred_outcome = c(mean(gc_hi$pred.y), mean(gc_lo$pred.y)),
  prop_in_cluster_2 = c(mean(gc_hi$inclusion.p[, 2]), mean(gc_lo$inclusion.p[, 2]))
)
```

Read that table as a decomposition. G-computation changes the outcome **only**
by moving subjects between clusters, so the effect it can produce is governed by

```
(change in cluster membership) x (gap between the clusters' outcome levels)
```

The two agree exactly when the outcome model has no covariates; with `CoY` in
the model each subject carries their own covariate offset, so the identity holds
up to that variation.

```{r g-computation-decompose}
# early_gamma_levels() is the internal accessor predict_lucid() itself uses to
# turn the fitted outcome parameters into one level per cluster -- not part of
# the public API, called here (via :::) only to show the arithmetic behind the
# g-computation contrast above. Reading res_Gamma$beta directly would give the
# stored parameterization, which is not the same thing.
levels_by_cluster <- LUCIDus:::early_gamma_levels(early_fit_refit$res_Gamma, early_fit_refit$K)
shift_in_membership <- mean(gc_hi$inclusion.p[, 2]) - mean(gc_lo$inclusion.p[, 2])

cat("cluster outcome levels     :", round(levels_by_cluster, 3), "\n")
cat("gap between clusters       :", round(diff(levels_by_cluster), 3), "\n")
cat("shift in cluster-2 share   :", round(shift_in_membership, 3), "\n")
cat("implied outcome contrast   :",
    round(shift_in_membership * diff(levels_by_cluster), 4), "\n")
cat("observed outcome contrast  :",
    round(mean(gc_hi$pred.y) - mean(gc_lo$pred.y), 4), "\n")
```

This is worth internalizing before applying g-computation to real data. If the
contrast comes out near zero there are exactly two possible reasons, and they
call for different responses: either the exposures barely move cluster
membership, or the clusters barely differ in outcome. Printing both terms tells
you which. A near-zero contrast with a large membership shift means the clusters
are not outcome-relevant; a large gap with no membership shift means the
exposures are not cluster-relevant.

A third possibility worth ruling out first: if `prop_in_cluster_2` is 0 or 1 in
both scenarios, the exposure model has saturated and no shift of any size will
move it. That is a property of the fit, not a finding about the exposure.

`pred.z` -- the omics profile the model implies under each scenario -- is
returned in this mode as well, so the counterfactual can be read on the omics
layer and not only on the outcome.

```{r g-computation-pred-z}
cat("predicted omics means differ between scenarios by (first 5 features):\n")
print(round(head(colMeans(gc_hi$pred.z) - colMeans(gc_lo$pred.z), 5), 4))
```

These differences reflect the same underlying cluster-membership shift as
the outcome contrast above, just read off the omics side instead of `Y` --
a feature with a larger implied shift here is one whose cluster-specific
means are further apart, consistent with `plot_cluster_omic_profile()`'s
ranking in section 15.

## 13) The `lucid()` Wrapper and Tuning K

Sections 7-10 deliberately did the screen-then-refit by hand, because seeing the
steps is the point of a tutorial. In practice `lucid()` does it for you: it tunes
over a grid, selects by BIC, and refits the winner on the selected features
without a penalty.

```{r lucid-wrapper, warning=FALSE}
set.seed(1501)
fit_tuned <- lucid(
  G = G, Z = Z_early_miss, Y = Y, CoG = CoG, CoY = CoY,
  lucid_model = "early", family = "normal",
  K = 2:3,
  Rho_G = c(0, 0.02),
  init_omic.data.model = NULL,
  max_itr = 15, max_tot.itr = 40, tol = 1e-2
)

cat("selected K :", fit_tuned$K, "\n")
cat("BIC        :", round(summary(fit_tuned, auto_print = FALSE)$BIC, 2), "\n")
```

When a penalty selects a strict subset, the fit carries a `selection` component
recording what was dropped from the **original** inputs, while `select`
describes the refit's own dimensions.

```{r lucid-selection}
if (!is.null(fit_tuned$selection)) {
  cat("exposures retained:",
      paste(fit_tuned$selection$Gnames[fit_tuned$selection$selectG], collapse = ", "), "\n")
  cat("exposures dropped :",
      paste(fit_tuned$selection$Gnames[!fit_tuned$selection$selectG], collapse = ", "), "\n")
} else {
  cat("no variable was deselected at this penalty grid\n")
}
```

One trap worth stating plainly: `fit$Rho` records the **tuned** penalty as
metadata -- the value that produced the selection -- not the penalty the final
refit ran with. The refit is unpenalized, which is precisely why its estimates
are not shrunk.

## 14) Visualizing the Model: Sankey Diagram

`plot()` renders an early-integration fit as a Sankey diagram: exposures flow
into the latent clusters, and the clusters flow on into the omics features and
the outcome. Node colour encodes variable type, link width is the magnitude of
the estimated effect, and link colour is its sign.

```{r sankey}
plot(early_fit_refit)
```

Only features the model retained are drawn, so a penalized fit produces a
correspondingly sparser diagram -- which makes this a quick visual check on
whether selection did what you expected.

`plot()` on a parallel or serial fit currently raises an error by design; those
diagrams are not implemented yet. That is a known limitation, not a bug.

## 15) Cluster Omics Profiles

The Sankey shows the path structure. It does not answer the question a reader of
the results actually asks first: **what are these clusters?** LUCID's clusters
are defined by their omics profiles, so that question is answered by looking at
the fitted cluster means -- and `plot_cluster_omic_profile()` does exactly that,
for whichever architecture you fitted.

### 15.1 The default view

```{r profile-early-heat, fig.width = 6.5, fig.height = 4.5, warning=FALSE}
prof_early <- plot_cluster_omic_profile(early_fit_refit, top_n = 10)
prof_early[[1]]
```

Read it the way you would a single-cell marker heatmap. Each row is an omics
feature, each column a latent cluster, and the fill says whether that cluster
sits high or low for that feature relative to the others.

**The fill is a z-score across clusters, not the raw mean.** Each feature is
standardized over the clusters before colouring, which is what stops a feature
with a large baseline from washing out every other row. The raw means are still
available -- see 15.4. Set `scale = FALSE` to colour by the mean instead.

### 15.2 Why the ranking is not the spread of the means

Only the `top_n` most discriminating features are drawn, and how "most
discriminating" is defined matters more than it first appears.

The obvious choice is the spread of a feature's cluster means: the further apart
they are, the more that feature separates the clusters. That is available as
`importance = "range"`, and it is what you get by eyeballing `res_Mu`. But it
ignores noise. A feature whose cluster means differ by two units tells you
nothing if its within-cluster standard deviation is also two -- the clusters
overlap almost completely on it.

The default, `"separation"`, divides the spread of the means by the typical
within-cluster spread. It is a between-to-within ratio, the same idea as an
effect size, and it is the only one of the three that can tell a genuinely
separating feature from a merely variable one. It is also scale-free, so
features on different measurement scales can be ranked in the same panel.

```{r profile-importance-compare, warning=FALSE}
rank_by <- function(measure) {
  d <- attr(plot_cluster_omic_profile(early_fit_refit, top_n = 5,
                                      importance = measure)[[1]], "profile_data")
  as.character(unique(d$feature)[order(-unique(d[, c("feature","score")])$score)])
}

data.frame(
  rank        = 1:5,
  separation  = rank_by("separation"),
  range       = rank_by("range")
)
```

Where those two columns disagree, the difference is entirely within-cluster
variance.

### 15.3 Bars, and more than one layer

The same information as bars, with clusters as shades of the layer's colour.
The shading is a sequential ramp rather than a categorical palette, so it keeps
working as the number of clusters grows instead of running out and recycling.

```{r profile-early-bar, fig.width = 6.5, fig.height = 4.5, warning=FALSE}
plot_cluster_omic_profile(early_fit_refit, type = "bar", top_n = 10)[[1]]
```

A parallel or serial fit returns **one plot per layer**, named, so nothing is
squeezed into a single figure. All three layers, in turn:

```{r profile-parallel, fig.width = 6.5, fig.height = 4.5, warning=FALSE}
prof_par <- plot_cluster_omic_profile(
  parallel_fit_refit,
  layer_names = c("methylome", "transcriptome", "miRNA"),
  top_n = 8
)
names(prof_par)
for (nm in names(prof_par)) print(prof_par[[nm]])
```

```{r profile-parallel-bar, fig.width = 6.5, fig.height = 4.5, warning=FALSE}
plot_cluster_omic_profile(parallel_fit_refit, type = "bar",
                          layer_names = c("methylome", "transcriptome", "miRNA"),
                          top_n = 8)[["transcriptome"]]
```

For a serial fit there is one plot per stage, and one per layer within any stage
that is itself a parallel sub-model. All three stages, in turn:

```{r profile-serial, fig.width = 6.5, fig.height = 4.5, warning=FALSE}
prof_ser <- plot_cluster_omic_profile(serial_all_early_refit, top_n = 8)
names(prof_ser)
for (nm in names(prof_ser)) print(prof_ser[[nm]])
```

```{r profile-serial-bar, fig.width = 6.5, fig.height = 4.5, warning=FALSE}
plot_cluster_omic_profile(serial_all_early_refit, type = "bar", top_n = 8)[[2]]
```

### 15.4 The numbers behind the figure

Each plot carries the data it drew, so the ranking can be reported in a table
without recomputing it:

```{r profile-data, warning=FALSE}
pd <- attr(prof_early[[1]], "profile_data")
head(pd[order(-pd$score), c("feature", "cluster", "mean", "sd", "score")], 6)
```

`mean` is the fitted cluster mean on the data's own scale and `sd` the
within-cluster standard deviation, so a claim made from the figure can be
quoted with its actual magnitude rather than a colour.

## 16) How to Read the Main Outputs

When interpreting `summary(...)` output, focus on:

1. **Model specification block**
   Confirms family, sample size, cluster structure.

2. **Missing-data profile**
   Verifies listwise vs sporadic missingness handling in each model/layer.

3. **Feature-selection overview**
   Shows selected counts and rates for `G` and `Z`.

4. **Detailed parameter estimates**
   - (1) `Y` model terms (including intercept and cluster effects)
   - (2) `Z` cluster means
   - (3) `E/G` effects on latent-cluster assignment (with OR)

5. **Bootstrap CI columns** (when `boot.se` is supplied)
   Compare `estimate` with its normal-theory confidence interval, and use
   the `sig` column (`"*"` where that interval excludes 0) as a quick scan
   across a table before reading every interval individually.

## 17) Practical Notes and Reproducibility

- Sections 7-10 keep `K` fixed for clarity and speed; section 13 demonstrates
  tuning on a deliberately small grid.
- The recommended analysis order in real projects is still:
  1. tune/search,
  2. selected-feature refit,
  3. bootstrap inference.
- `boot_lucid()` is designed for zero-penalty inference models and handles this workflow accordingly.
- Seeds are fixed before each fit/bootstrap step for deterministic tutorial output.

## 18) Verification Summary and Session Info

Every model this tutorial fits is registered below. This is the document
checking itself: if a fit failed, its object would be missing or of the wrong
class, and it would be listed here rather than passing unnoticed.

```{r status-table}
check_obj("early_fit_refit",       "early_lucid",    "7 early normal")
check_obj("parallel_fit_refit",    "lucid_parallel", "8 parallel normal")
check_obj("serial_all_early_refit","lucid_serial",   "9 serial all-early normal")
check_obj("serial_mixed_refit",    "lucid_serial",   "10 serial mixed normal")
check_obj("na_early",              "list",           "11 missing diagnostics")
check_obj("pred_train",            "list",           "12 prediction")
check_obj("gc_hi",                 "list",           "12.3 g-computation")
check_obj("fit_tuned",             "early_lucid",    "13 lucid() wrapper")
check_obj("prof_early",            "list",           "15 omics profile (early)")
check_obj("prof_par",              "list",           "15.3 omics profile (parallel)")
check_obj("prof_ser",              "list",           "15.3 omics profile (serial)")

status <- do.call(rbind, .reg$rows)
print(status, row.names = FALSE)

cat(sprintf("\n%d of %d registered steps ok; %d not ok\n",
            sum(status$status == "ok"), nrow(status),
            sum(status$status != "ok")))
```

### Session Info

```{r}
sessionInfo()
```
