Package 'missoNet'

missoNet: Multi-Task Regression and Conditional Network Estimation with Missing Responses

Description

missoNet fits a joint multivariate regression and conditional dependency (precision–matrix) model when some response entries are missing. The method estimates a sparse coefficient matrix $B$ linking predictors $X$ to multivariate responses $Y$, together with a sparse inverse covariance $\Theta$ for the residuals in $Y = \mathbf{1}\mu^{\mathsf{T}} + XB + E$, $E \sim \mathcal{N}(0, \Theta^{-1})$. Responses may contain missing values (e.g., MCAR/MAR); predictors must be finite. The package provides cross-validation, prediction, publication-ready plotting, and simple simulation utilities.

Details

Key features

• Joint estimation of $B$ (regression) and $\Theta$ (conditional network).

• $\ell_1$-regularization on both $B$ and $\Theta$ with user-controlled grids.

• K-fold cross-validation with optional 1-SE model selections.

• Heatmap and 3D surface visualizations for CV error or GoF across $(\lambda_B,\lambda_\Theta)$.

• Fast prediction for new data using stored solutions.

• Lightweight data generator for simulation studies.

Workflow

1. Fit a model across a grid of penalties with missoNet or select penalties via cv.missoNet.

2. Visualize the CV error/GoF surface with plot.missoNet.

3. Predict responses for new observations with predict.missoNet.

Main functions

missoNet

Fit models over user-specified penalty grids for $\lambda_B$ and $\lambda_\Theta$; returns estimated $\mu$, $B$, $\Theta$, and metadata (grids, GoF).

cv.missoNet

Perform k-fold cross-validation over a penalty grid; stores est.min and (optionally) est.1se.beta, est.1se.theta.

plot.missoNet

S3 plotting method; heatmap or 3D scatter of CV error or GoF.

predict.missoNet

S3 prediction method; returns $\hat{Y} = \mathbf{1}\hat{\mu}^{\mathsf{T}} + X_\mathrm{new}\hat{B}$ for a chosen solution.

generateData

Generate synthetic datasets with controllable dimensions, signal, and missingness mechanisms for benchmarking.

License

GPL-2.

Author(s)

Maintainer: Yixiao Zeng [email protected] [copyright holder]

Authors:

• Celia Greenwood [email protected] [thesis advisor]

See Also

missoNet, cv.missoNet, plot.missoNet, predict.missoNet, generateData, and browseVignettes("missoNet") for tutorials.

Examples

sim <- generateData(n = 100, p = 8, q = 5, rho = 0.1, missing.type = "MCAR")


fit <- missoNet(X = sim$X, Y = sim$Z)             # fit over a grid
plot(fit)                                         # GoF heatmap

cvfit <- cv.missoNet(X = sim$X, Y = sim$Z, kfold = 5, compute.1se = TRUE)
plot(cvfit, type = "scatter", plt.surf = TRUE)    # CV error surface
yhat  <- predict(cvfit, newx = sim$X, s = "lambda.min")

---

Cross-validation for missoNet

Description

Perform $k$-fold cross-validation to select the regularization pair (lambda.beta, lambda.theta) for missoNet. For each fold the model is trained on $k-1$ partitions and evaluated on the held-out partition over a grid of lambda pairs; the pair with minimum mean CV error is returned, with optional 1-SE models for more regularized solutions.

Usage

cv.missoNet(
  X,
  Y,
  kfold = 5,
  rho = NULL,
  lambda.beta = NULL,
  lambda.theta = NULL,
  lambda.beta.min.ratio = NULL,
  lambda.theta.min.ratio = NULL,
  n.lambda.beta = NULL,
  n.lambda.theta = NULL,
  beta.pen.factor = NULL,
  theta.pen.factor = NULL,
  penalize.diagonal = NULL,
  beta.max.iter = 10000,
  beta.tol = 1e-05,
  theta.max.iter = 10000,
  theta.tol = 1e-05,
  eta = 0.8,
  eps = 1e-08,
  standardize = TRUE,
  standardize.response = TRUE,
  compute.1se = TRUE,
  relax.net = FALSE,
  adaptive.search = FALSE,
  shuffle = TRUE,
  seed = NULL,
  parallel = FALSE,
  cl = NULL,
  verbose = 1
)

Arguments

X	Numeric matrix ($n \times p$). Predictors (no missing values).
Y	Numeric matrix ($n \times q$). Responses. Missing values should be coded as NA/NaN.
kfold	Integer $\ge 2$. Number of folds (default 5).
rho	Optional numeric vector of length $q$. Working missingness probabilities (per response). If NULL (default), estimated from Y.
lambda.beta, lambda.theta	Optional numeric vectors. Candidate regularization paths for $\mathbf{B}$ and $\Theta$. If NULL, sequences are generated automatically from the data. Avoid supplying a single value because warm starts along a path are used.
lambda.beta.min.ratio, lambda.theta.min.ratio	Optional numerics in (0,1]. Ratio of the smallest to the largest value when generating lambda sequences (ignored if the corresponding lambda.* is supplied).
n.lambda.beta, n.lambda.theta	Optional integers. Lengths of the automatically generated lambda paths (ignored if the corresponding lambda.* is supplied).
beta.pen.factor	Optional $p \times q$ non-negative matrix of element-wise penalty multipliers for $\mathbf{B}$. Inf = maximum penalty; 0 = no penalty for the corresponding coefficient. Default: all 1s (equal penalty).
theta.pen.factor	Optional $q \times q$ non-negative matrix of element-wise penalty multipliers for $\Theta$. Off-diagonal entries control edge penalties; diagonal treatment is governed by penalize.diagonal. Inf = maximum penalty; 0 = no penalty for that element. Default: all 1s (equal penalty).
penalize.diagonal	Logical or NULL. Whether to penalize diagonal entries of $\Theta$. If NULL (default) the choice is made automatically.
beta.max.iter, theta.max.iter	Integers. Max iterations for the $\mathbf{B}$ update (FISTA) and $\Theta$ update (graphical lasso). Defaults: 10000.
beta.tol, theta.tol	Numerics $> 0$. Convergence tolerances for the $\mathbf{B}$ and $\Theta$ updates. Defaults: 1e-5.
eta	Numeric in (0,1). Backtracking line-search parameter for the $\mathbf{B}$ update (default 0.8).
eps	Numeric in (0,1). Eigenvalue floor used to stabilize positive definiteness operations (default 1e-8).
standardize	Logical. Standardize columns of X internally? Default TRUE.
standardize.response	Logical. Standardize columns of Y internally? Default TRUE.
compute.1se	Logical. Also compute 1-SE solutions? Default TRUE.
relax.net	(Experimental) Logical. If TRUE, refit active edges of $\Theta$ without $\ell_1$ penalty (de-biased network). Default FALSE.
adaptive.search	(Experimental) Logical. Use adaptive two-stage lambda search? Default FALSE.
shuffle	Logical. Randomly shuffle fold assignments? Default TRUE.
seed	Optional integer seed (used when shuffle = TRUE).
parallel	Logical. Evaluate folds in parallel using a provided cluster? Default FALSE.
cl	Optional cluster from parallel::makeCluster() (required if parallel = TRUE).
verbose	Integer in 0,1,2. 0 = silent, 1 = progress (default), 2 = detailed tracing (not supported in parallel mode).

Details

Internally, predictors X and responses Y can be standardized for optimization; all reported estimates are re-scaled back to the original data scale. Missingness in Y is handled via unbiased estimating equations using column-wise observation probabilities estimated from Y (or supplied via rho). This is appropriate when the missingness of each response is independent of its unobserved value (e.g., MCAR).

If adaptive.search = TRUE, a fast two-stage pre-optimization narrows the lambda grid before computing fold errors on a focused neighborhood; this can be substantially faster on large grids but may occasionally miss the global optimum.

When compute.1se = TRUE, two additional solutions are reported: the largest lambda.beta and the largest lambda.theta whose CV error is within one standard error of the minimum (holding the other lambda fixed at its optimal value). At the end, three special lambda pairs are identified:

• lambda.min: Parameters giving minimum CV error

• lambda.1se.beta: Largest $\lambda_B$ within 1 SE of minimum (with $\lambda_\Theta$ fixed at optimum)

• lambda.1se.theta: Largest $\lambda_\Theta$ within 1 SE of minimum (with $\lambda_B$ fixed at optimum)

The 1SE rules provide more regularized models that may generalize better.

Value

A list of class "missoNet" with components:

est.min

List of estimates at the CV minimum: Beta ($p \times q$), Theta ($q \times q$), intercept mu (length $q$), lambda.beta, lambda.theta, lambda.beta.idx, lambda.theta.idx, and (if requested) relax.net.

est.1se.beta

List of estimates at the 1-SE lambda.beta (if compute.1se = TRUE); NULL otherwise.

est.1se.theta

List of estimates at the 1-SE lambda.theta (if compute.1se = TRUE); NULL otherwise.

rho

Length-$q$ vector of working missingness probabilities.

kfold

Number of folds used.

fold.index

Integer vector of length $n$ giving fold assignments (names are "fold-k").

lambda.beta.seq, lambda.theta.seq

Unique lambda values explored along the grid for $\mathbf{B}$ and $\Theta$.

penalize.diagonal

Logical indicating whether the diagonal of $\Theta$ was penalized.

beta.pen.factor, theta.pen.factor

Penalty factor matrices actually used.

param_set

List with CV diagnostics: n, p, q, standardize, standardize.response, mean errors cv.errors.mean, bounds cv.errors.upper/lower, and the evaluated grids cv.grid.beta, cv.grid.theta (length equals number of fitted models).

Author(s)

Yixiao Zeng [email protected], Celia M. T. Greenwood

References

Zeng, Y., et al. (2025). Multivariate regression with missing response data for modelling regional DNA methylation QTLs. arXiv:2507.05990.

See Also

missoNet for model fitting; generic methods such as plot() and predict() for objects of class "missoNet".

Examples

sim <- generateData(n = 120, p = 12, q = 6, rho = 0.1)
X <- sim$X; Y <- sim$Z


# Basic 5-fold cross-validation
cvfit <- cv.missoNet(X = X, Y = Y, kfold = 5, verbose = 0)

# Extract optimal estimates
Beta.min <- cvfit$est.min$Beta
Theta.min <- cvfit$est.min$Theta

# Extract 1SE estimates (if computed)
if (!is.null(cvfit$est.1se.beta)) {
  Beta.1se <- cvfit$est.1se.beta$Beta
}
if (!is.null(cvfit$est.1se.theta)) {
  Theta.1se <- cvfit$est.1se.theta$Theta
}

# Make predictions
newX <- matrix(rnorm(10 * 12), 10, 12)
pred.min <- predict(cvfit, newx = newX, s = "lambda.min")
pred.1se <- predict(cvfit, newx = newX, s = "lambda.1se.beta")

# Parallel cross-validation
library(parallel)
cl <- makeCluster(min(detectCores() - 1, 2))
cvfit2 <- cv.missoNet(X = X, Y = Y, kfold = 5, 
                      parallel = TRUE, cl = cl)
stopCluster(cl)

# Adaptive search for efficiency
cvfit3 <- cv.missoNet(X = X, Y = Y, kfold = 5,
                      adaptive.search = TRUE)

# Reproducible CV with specific lambdas
cvfit4 <- cv.missoNet(X = X, Y = Y, kfold = 5,
                      lambda.beta = 10^seq(0, -2, length = 20),
                      lambda.theta = 10^seq(0, -2, length = 20),
                      seed = 486)

# Plot CV results
plot(cvfit, type = "heatmap")
plot(cvfit, type = "scatter")

---

Generate synthetic data with missing values for missoNet

Description

Generates synthetic data from a conditional Gaussian graphical model with user-specified missing data mechanisms. This function is designed for simulation studies and testing of the missoNet package, supporting three types of missingness: Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR).

Usage

generateData(
  n,
  p,
  q,
  rho,
  missing.type = "MCAR",
  X = NULL,
  Beta = NULL,
  E = NULL,
  Theta = NULL,
  Sigma.X = NULL,
  Beta.row.sparsity = 0.2,
  Beta.elm.sparsity = 0.2,
  seed = NULL
)

Arguments

n	Integer. Sample size (number of observations). Must be at least 2.
p	Integer. Number of predictor variables. Must be at least 1.
q	Integer. Number of response variables. Must be at least 2.
rho	Numeric scalar or vector of length q. Proportion of missing values for each response variable. Values must be in [0, 1). If scalar, the same missing rate is applied to all responses.
missing.type	Character string specifying the missing data mechanism. One of: "MCAR" (default): Missing Completely At Random "MAR": Missing At Random (depends on predictors) "MNAR": Missing Not At Random (depends on response values)
X	Optional n x p matrix. User-supplied predictor matrix. If NULL (default), predictors are simulated from a multivariate normal distribution with mean zero and covariance Sigma.X.
Beta	Optional p x q matrix. Regression coefficient matrix. If NULL (default), a sparse coefficient matrix is generated with sparsity controlled by Beta.row.sparsity and Beta.elm.sparsity.
E	Optional n x q matrix. Error/noise matrix. If NULL (default), errors are simulated from a multivariate normal distribution with mean zero and precision matrix Theta.
Theta	Optional q x q positive definite matrix. Precision matrix (inverse covariance) for the response variables. If NULL (default), a block-structured precision matrix is generated with four types of graph structures. Only used when E = NULL.
Sigma.X	Optional p x p positive definite matrix. Covariance matrix for the predictors. If NULL (default), an AR(1) covariance structure with correlation 0.7 is used. Only used when X = NULL.
Beta.row.sparsity	Numeric in [0, 1]. Proportion of rows in Beta that contain at least one non-zero element. Default is 0.2. Only used when Beta = NULL.
Beta.elm.sparsity	Numeric in [0, 1]. Proportion of non-zero elements within active rows of Beta. Default is 0.2. Only used when Beta = NULL.
seed	Optional integer. Random seed for reproducibility.

Details

The function generates data through the following model:

$Y = XB + E$

where:

• $X \in \mathbb{R}^{n \times p}$ is the predictor matrix

• $B \in \mathbb{R}^{p \times q}$ is the coefficient matrix

• $E \sim \mathcal{MVN}(0, \Theta^{-1})$ is the error matrix

• $Y \in \mathbb{R}^{n \times q}$ is the complete response matrix

Missing values are then introduced to create $Z$ (the observed response matrix with NAs) according to the specified mechanism:

MCAR: Each element has probability rho[j] of being missing, independent of all variables.

MAR: Missingness depends on the predictors through a logistic model:

$P(Z_{ij} = NA) = \mathrm{logit}^{-1}(XB)_{ij} \times c_j$

where $c_j$ is calibrated to achieve the target missing rate.

MNAR: The lowest rho[j] proportion of values in each column are set as missing.

Value

A list containing:

X	n x p matrix. Predictor matrix (either user-supplied or simulated).
Y	n x q matrix. Complete response matrix without missing values.
Z	n x q matrix. Response matrix with missing values (coded as NA).
Beta	p x q matrix. Regression coefficient matrix used in generation.
Theta	q x q matrix or NULL. Precision matrix (if used in generation).
rho	Numeric vector of length q. Missing rates for each response.
missing.type	Character string. The missing mechanism used.

Author(s)

Yixiao Zeng [email protected], Celia M. T. Greenwood

See Also

missoNet for fitting models to data with missing values, cv.missoNet for cross-validation

Examples

# Example 1: Basic usage with default settings
sim.dat <- generateData(n = 300, p = 50, q = 20, rho = 0.1, seed = 857)

# Check dimensions and missing rate
dim(sim.dat$X)      # 300 x 50
dim(sim.dat$Z)      # 300 x 20
mean(is.na(sim.dat$Z))  # approximately 0.1

# Example 2: Variable missing rates with MAR mechanism
rho.vec <- seq(0.05, 0.25, length.out = 20)
sim.dat <- generateData(n = 300, p = 50, q = 20, 
                       rho = rho.vec, 
                       missing.type = "MAR")

# Example 3: High sparsity in coefficient matrix
sim.dat <- generateData(n = 500, p = 100, q = 30,
                       rho = 0.15,
                       Beta.row.sparsity = 0.1,  # 10% active predictors
                       Beta.elm.sparsity = 0.3)  # 30% active in each row

# Example 4: User-supplied matrices
n <- 300; p <- 50; q <- 20
X <- matrix(rnorm(n*p), n, p)
Beta <- matrix(rnorm(p*q) * rbinom(p*q, 1, 0.1), p, q)  # 10% non-zero
Theta <- diag(q) + 0.1  # Simple precision structure

sim.dat <- generateData(X = X, Beta = Beta, Theta = Theta,
                       n = n, p = p, q = q,
                       rho = 0.2, missing.type = "MNAR")


# Example 5: Use generated data with missoNet
library(missoNet)
sim.dat <- generateData(n = 400, p = 50, q = 10, rho = 0.15)

# Split into training and test sets
train.idx <- 1:300
test.idx <- 301:400

# Fit missoNet model
fit <- missoNet(X = sim.dat$X[train.idx, ], 
               Y = sim.dat$Z[train.idx, ],
               lambda.beta = 0.1, 
               lambda.theta = 0.1)

# Evaluate on test set
pred <- predict(fit, newx = sim.dat$X[test.idx, ])

---

Fit missoNet models with missing responses

Description

Fit a penalized multi-task regression with a response-network ($\Theta$) under missing responses. The method jointly estimates the coefficient matrix $\mathbf{B}$ and the precision matrix $\Theta$ via penalized likelihood with $\ell_1$ penalties on $\mathbf{B}$ and the off-diagonal entries of $\Theta$.

Usage

missoNet(
  X,
  Y,
  rho = NULL,
  GoF = "eBIC",
  lambda.beta = NULL,
  lambda.theta = NULL,
  lambda.beta.min.ratio = NULL,
  lambda.theta.min.ratio = NULL,
  n.lambda.beta = NULL,
  n.lambda.theta = NULL,
  beta.pen.factor = NULL,
  theta.pen.factor = NULL,
  penalize.diagonal = NULL,
  beta.max.iter = 10000,
  beta.tol = 1e-05,
  theta.max.iter = 10000,
  theta.tol = 1e-05,
  eta = 0.8,
  eps = 1e-08,
  standardize = TRUE,
  standardize.response = TRUE,
  relax.net = FALSE,
  adaptive.search = FALSE,
  parallel = FALSE,
  cl = NULL,
  verbose = 1
)

Arguments

X	Numeric matrix ($n \times p$). Predictors (no missing values).
Y	Numeric matrix ($n \times q$). Responses, may contain NA/NaN.
rho	Optional numeric vector of length $q$. Working missingness probabilities; if NULL (default), estimated from Y.
GoF	Character. Goodness-of-fit criterion: "AIC", "BIC", or "eBIC" (default).
lambda.beta, lambda.theta	Optional numeric vectors (or scalars). Candidate regularization paths for $\mathbf{B}$ and $\Theta$. If NULL, paths are generated automatically.
lambda.beta.min.ratio, lambda.theta.min.ratio	Optional numerics in (0,1]. Ratio of the smallest to largest lambda when generating paths (ignored if the corresponding lambda.* is supplied).
n.lambda.beta, n.lambda.theta	Optional integers. Lengths of automatically generated lambda paths (ignored if the corresponding lambda.* is supplied).
beta.pen.factor	Optional $p \times q$ non-negative matrix of element-wise penalty multipliers for $\mathbf{B}$. Inf = maximum penalty; 0 = no penalty for that coefficient. Default: all 1s (equal penalty).
theta.pen.factor	Optional $q \times q$ non-negative matrix of element-wise penalty multipliers for $\Theta$. Off-diagonal entries control edge penalties; diagonal treatment is governed by penalize.diagonal. Inf = maximum penalty; 0 = no penalty for that coefficient. Default: all 1s (equal penalty).
penalize.diagonal	Logical or NULL. Whether to penalize the diagonal of $\Theta$. If NULL (default) the choice is made automatically.
beta.max.iter, theta.max.iter	Integers. Max iterations for the $\mathbf{B}$ update (FISTA) and $\Theta$ update (graphical lasso). Defaults: 10000.
beta.tol, theta.tol	Numerics $> 0$. Convergence tolerances for the $\mathbf{B}$ and $\Theta$ updates. Defaults: 1e-5.
eta	Numeric in (0,1). Backtracking line-search parameter for the $\mathbf{B}$ update (default 0.8).
eps	Numeric in (0,1). Eigenvalue floor used to stabilize positive definiteness operations (default 1e-8).
standardize	Logical. Standardize columns of X internally? Default TRUE.
standardize.response	Logical. Standardize columns of Y internally? Default TRUE.
relax.net	(Experimental) Logical. If TRUE, refit active edges of $\Theta$ without $\ell_1$ penalty (de-biased network). Default FALSE.
adaptive.search	(Experimental) Logical. Use adaptive two-stage lambda search? Default FALSE.
parallel	Logical. Evaluate parts of the grid in parallel using a provided cluster? Default FALSE.
cl	Optional cluster from parallel::makeCluster() (required if parallel = TRUE).
verbose	Integer in 0,1,2. 0 = silent, 1 = progress (default), 2 = detailed tracing (not supported in parallel mode).

Details

The conditional Gaussian model is

$Y_i = \mu + X_i \mathbf{B} + E_i, \qquad E_i \sim \mathcal{N}_q(0, \Theta^{-1}).$

where:

• $Y_i$ is the $i$-th observation of $q$ responses

• $X_i$ is the $i$-th observation of $p$ predictors

• $\mathbf{B}$ is the $p \times q$ coefficient matrix

• $\Theta$ is the $q \times q$ precision matrix

• $\mu$ is the intercept vector

The parameters are estimated by solving:

$\min_{\mathbf{B}, \Theta \succ 0} \quad g(\mathbf{B}, \Theta) + \lambda_B \|\mathbf{B}\|_1 + \lambda_\Theta \|\Theta\|_{1,\mathrm{off}}$

where $g$ is the negative log-likelihood.

Missing values in Y are accommodated through unbiased estimating equations using column-wise observation probabilities. Internally, X and Y may be standardized for numerical stability; returned estimates are re-scaled back to the original units.

The grid search spans lambda.beta and lambda.theta. The optimal pair is selected by the user-chosen goodness-of-fit criterion GoF: "AIC", "BIC", or "eBIC" (default). If adaptive.search = TRUE, a two-stage pre-optimization narrows the grid before the main search (faster on large problems, with a small risk of missing the global optimum).

Value

A list of class "missoNet" with components:

est.min

List at the selected lambda pair: Beta ($p \times q$), Theta ($q \times q$), intercept mu (length $q$), lambda.beta, lambda.theta, lambda.beta.idx, lambda.theta.idx, scalar gof (AIC/BIC/eBIC at optimum), and (if requested) relax.net.

rho

Length-$q$ vector of working missingness probabilities.

lambda.beta.seq, lambda.theta.seq

Unique lambda values explored along the grid for $\mathbf{B}$ and $\Theta$.

penalize.diagonal

Logical indicating whether the diagonal of $\Theta$ was penalized.

beta.pen.factor, theta.pen.factor

Penalty factor matrices actually used.

param_set

List with fitting diagnostics: n, p, q, standardize, standardize.response, the vector of criterion values gof, and the evaluated grids gof.grid.beta, gof.grid.theta (length equals number of fitted models).

Author(s)

Yixiao Zeng [email protected], Celia M. T. Greenwood

References

Zeng, Y., et al. (2025). Multivariate regression with missing response data for modelling regional DNA methylation QTLs. arXiv:2507.05990.

See Also

cv.missoNet for cross-validated selection; generic methods such as plot() and predict() for objects of class "missoNet".

Examples

sim <- generateData(n = 120, p = 10, q = 6, rho = 0.1)
X <- sim$X; Y <- sim$Z


# Fit with defaults (criterion = eBIC)
fit1 <- missoNet(X, Y)
# Extract the optimal estimates
Beta.hat <- fit1$est.min$Beta
Theta.hat <- fit1$est.min$Theta

# Plot missoNet results
plot(fit1, type = "heatmap")
plot(fit1, type = "scatter")

# Provide short lambda paths
fit2 <- missoNet(
  X, Y,
  lambda.beta  = 10^seq(0, -2, length.out = 5),
  lambda.theta = 10^seq(0, -2, length.out = 5),
  GoF = "BIC"
)

# Test single lambda choice
fit3 <- missoNet(
  X, Y,
  lambda.beta  = 0.1,
  lambda.theta = 0.1,
)

# De-biased network on the active set
fit4 <- missoNet(X, Y, relax.net = TRUE, verbose = 0)

# Adaptive search for large problems
fit5 <- missoNet(X = X, Y = Y, adaptive.search = TRUE, verbose = 0)

# Parallel (requires a cluster)
library(parallel)
cl <- makeCluster(2)
fit_par <- missoNet(X, Y, parallel = TRUE, cl = cl, verbose = 0)
stopCluster(cl)

---

Plot methods for missoNet and cross-validated fits

Description

Visualize either the cross-validation (CV) error surface or the goodness-of-fit (GoF) surface over the $\lambda_B$–$\lambda_\Theta$ grid for objects returned by missoNet or cv.missoNet. Two display types are supported: a 2D heatmap (default) and a 3D scatter surface.

Usage

## S3 method for class 'missoNet'
plot(
  x,
  type = c("heatmap", "scatter"),
  detailed.axes = TRUE,
  plt.surf = TRUE,
  ...
)

Arguments

x	A fitted object returned by missoNet or cv.missoNet.
type	Character string specifying the plot type. One of "heatmap" (default) or "scatter".
detailed.axes	Logical; if TRUE (default) show dense axis labels. If FALSE, a sparser labeling is used to avoid clutter.
plt.surf	Logical; for type = "scatter" only, draw light surface grid lines and highlight the minimum point. Ignored for heatmaps. Default TRUE.
...	Additional graphical arguments forwarded to Heatmap when type = "heatmap", or to scatterplot3d when type = "scatter".

Details

This S3 method detects whether x contains cross-validation results and chooses an appropriate plotting backend:

• Heatmap: uses Heatmap with a viridis-like color ramp (via colorRamp2). The selected $(\lambda_B, \lambda_\Theta)$ is outlined in white; 1-SE choices (if present) are highlighted with dashed/dotted outlines.

• Scatter: uses scatterplot3d to draw the error/GoF surface on $\log_{10}$ scales. When plt.surf = TRUE, light lattice lines are added, and the minimum is marked.

Value

• For type = "heatmap": a ComplexHeatmap Heatmap object (invisibly drawn by ComplexHeatmap).

• For type = "scatter": a "scatterplot3d" object, returned invisibly.

What gets plotted

• CV objects (created by cv.missoNet or any missoNet object that carries CV results): the color encodes the mean CV error for each $(\lambda_B, \lambda_\Theta)$ pair. The minimum-error solution is outlined; if 1-SE solutions were computed, they are also marked (dashed/dotted outlines).

• Non-CV objects (created by missoNet without CV): the color encodes the GoF value over the grid; the selected minimum (best) solution is outlined.

Axes and scales

For heatmaps, axes are the raw $\lambda$ values; rows are $\lambda_\Theta$ and columns are $\lambda_B$. For 3D scatter plots, both $\lambda$ axes are shown on the $\log_{10}$ scale for readability.

Color mapping

A viridis-like palette is used. Breaks are based on distribution quantiles of the CV error or GoF values to enhance contrast across the grid.

Dependencies

Requires ComplexHeatmap, circlize, scatterplot3d, and grid.

Author(s)

Yixiao Zeng [email protected], Celia M. T. Greenwood

See Also

missoNet, cv.missoNet, Heatmap, scatterplot3d

Examples

sim <- generateData(n = 150, p = 10, q = 8, rho = 0.1, missing.type = "MCAR")


## Fit a model without CV (plots GoF surface)
fit <- missoNet(X = sim$X, Y = sim$Z, verbose = 0)
plot(fit, type = "heatmap")                        # GoF heatmap
plot(fit, type = "scatter", plt.surf = TRUE)       # GoF 3D scatter

## Cross-validation (plots CV error surface)
cvfit <- cv.missoNet(X = sim$X, Y = sim$Z, verbose = 0)
plot(cvfit, type = "heatmap", detailed.axes = FALSE)
plot(cvfit, type = "scatter", plt.surf = FALSE)

---

Predict method for missoNet models

Description

Generate predicted responses for new observations from a fitted missoNet (or cross-validated) model. The prediction at a given regularization choice $(\lambda_B,\lambda_\Theta)$ uses the fitted intercept(s) $\hat{\mu}$ and coefficient matrix $\hat{B}$:

$\hat{Y} = \mathbf{1}_n \hat{\mu}^{\mathsf{T}} + X_\mathrm{new}\,\hat{B}.$

Usage

## S3 method for class 'missoNet'
predict(
  object,
  newx,
  s = c("lambda.min", "lambda.1se.beta", "lambda.1se.theta"),
  ...
)

## S3 method for class 'cv.missoNet'
predict(
  object,
  newx,
  s = c("lambda.min", "lambda.1se.beta", "lambda.1se.theta"),
  ...
)

Arguments

object	A fitted missoNet (or cross-validated missoNet) object that contains the components $est.min (and optionally $est.1se.beta, $est.1se.theta), each with numeric fields $mu (length $q$) and $Beta (p x q).
newx	Numeric matrix of predictors with $p$ columns (no intercept column of 1s). Missing or non-finite values are not allowed. Columns must be in the same order/scale used to fit object.
s	Character string selecting the stored solution; one of c("lambda.min","lambda.1se.beta","lambda.1se.theta").
...	Ignored; included for S3 compatibility.

Details

This method does not modify or standardize newx. If the model was trained with standardization, ensure that newx has been prepared in the same way as the training data (same centering/scaling and column order).

Value

A numeric matrix of predicted responses of dimension $n_\mathrm{new} x q$. Row names are taken from newx (if any), and column names are inherited from the fitted coefficient matrix (if any).

Which solution is used

The s argument selects the stored solution:

• "lambda.min" (default): the minimum CV error or selected GoF solution, stored in object$est.min.

• "lambda.1se.beta": the 1-SE solution favoring larger $\lambda_B$, stored in object$est.1se.beta.

• "lambda.1se.theta": the 1-SE solution favoring larger $\lambda_\Theta$, stored in object$est.1se.theta.

1-SE solutions are available only if the model was fit with compute.1se = TRUE during training or cross-validation.

See Also

missoNet, cv.missoNet, plot.missoNet

Examples

sim <- generateData(n = 200, p = 8, q = 6, rho = 0.1,
                    missing.type = "MCAR", seed = 123)
tr  <- 1:150
tst <- 151:200


## Cross-validated fit, keeping 1-SE solutions
cvfit <- cv.missoNet(X = sim$X[tr, ], Y = sim$Z[tr, ], kfold = 5,
                     compute.1se = TRUE, verbose = 0)

## Predict on held-out set
yhat_min  <- predict(cvfit, newx = sim$X[tst, ], s = "lambda.min")
yhat_b1se <- predict(cvfit, newx = sim$X[tst, ], s = "lambda.1se.beta")
yhat_t1se <- predict(cvfit, newx = sim$X[tst, ], s = "lambda.1se.theta")
dim(yhat_min)  # 50 x q

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