predict_lucid: Predict Cluster Assignment and Outcome From a Fitted LUCID...

In LUCIDus: LUCID with Multiple Omics Data

Predict Cluster Assignment and Outcome From a Fitted LUCID Model

Description

Predict cluster assignment and outcome using new data on G, Z, and optional Y. If g_computation = TRUE, prediction uses only the G-to-X path from the fitted model and returns counterfactual-style predictions under modified G. This function can also be used to extract latent cluster assignments when using the training data as input.

Usage

predict_lucid(
  model,
  lucid_model = c("early", "parallel", "serial"),
  G,
  Z = NULL,
  Y = NULL,
  CoG = NULL,
  CoY = NULL,
  response = TRUE,
  g_computation = FALSE,
  verbose = FALSE
)

Arguments

model	A model fitted and returned by estimate_lucid
lucid_model	Specifying LUCID model, "early" for early integration, "parallel" for LUCID in parallel, and "serial" for LUCID in serial.
G	Exposures, a numeric vector, matrix, or data frame. Categorical variable should be transformed into dummy variables. If a matrix or data frame, rows represent observations and columns correspond to variables.
Z	Omics data. If "early", an N by M matrix. If "parallel", a list, each element i is a matrix with N rows and P_i features. If "serial", a list, each element i is a matrix with N rows and p_i features (or a list with two or more matrices with N rows and a certain number of features). For g_computation = TRUE, Z can be NULL for "early", "parallel", and "serial".
Y	Outcome, a numeric vector. Categorical variable is not allowed. Binary outcome should be coded as 0 and 1.
CoG	Optional, covariates to be adjusted for estimating the latent cluster. A numeric vector, matrix or data frame. Categorical variable should be transformed into dummy variables.
CoY	Optional, covariates to be adjusted for estimating the association between latent cluster and the outcome. A numeric vector, matrix or data frame. Categorical variable should be transformed into dummy variables.
response	If TRUE, when predicting binary outcomes, class labels (0/1) are returned using a 0.5 threshold. If FALSE, predicted probabilities are returned.
g_computation	If TRUE, prediction uses only information on G. For this mode, supplied Z and Y are ignored for "early", "parallel", and "serial".
verbose	A flag indicates whether detailed information is printed in console. Default is FALSE.

Value

A list containing:

inclusion.p	Posterior inclusion probabilities for latent clusters (a matrix for "early"; a list by layer for "parallel" and "serial").
pred.x	Predicted latent-cluster labels (a numeric vector for "early"; a list by layer for "parallel" and "serial").
pred.y	Predicted outcome values. For binary outcomes, this is class labels when response = TRUE and probabilities when response = FALSE.
pred.z	Predicted omics means under g-computation mode (g_computation = TRUE).

Examples

# prepare data
G <- sim_data$G
Z <- sim_data$Z
Y_normal <- sim_data$Y_normal

# fit lucid model
fit1 <- estimate_lucid(G = G, Z = Z, Y = Y_normal, lucid_model = "early", K = 2, family = "normal")

# prediction on training set
pred1 <- predict_lucid(model = fit1, G = G, Z = Z, Y = Y_normal, lucid_model = "early")
pred2 <- predict_lucid(model = fit1, G = G, Z = Z, lucid_model = "early")

# g-computation style prediction using only G
pred_g <- predict_lucid(model = fit1, G = G, Z = NULL, g_computation = TRUE, lucid_model = "early")

LUCIDus documentation built on March 11, 2026, 9:06 a.m.