LUCIDus (version 3.0.3)

predict_lucid: Predict cluster assignment and outcome based on LUCID model using new data of G,Z,(Y). If g_computation, predict cluster assignment, omics data, and outcome based on LUCID model using new data of G only This function can also be use to extract X assignment is using training data G,Z,Y as input.

Description

Predict cluster assignment and outcome based on LUCID model using new data of G,Z,(Y). If g_computation, predict cluster assignment, omics data, and outcome based on LUCID model using new data of G only This function can also be use to extract X assignment is using training data G,Z,Y as input.

Usage

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

Value

A list containing the following components:

1. inclusion.p: A list of inclusion probabilities for each sub-model in the LUCID model.

2. pred.x: A list of predicted values for the data matrix G.

3. pred.y: Predicted values for the response variable Y (if response is TRUE).

4. pred.z: Predicted values for the omics variables Z (if g_computation is TRUE).

Arguments

model

A model fitted and returned by estimate_lucid

lucid_model

Specifying LUCID model, "early" for early integration, "parallel" for lucid in parallel "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

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 outcome, the response will be returned. If FALSE, the linear predictor is returned.

g_computation

If TRUE, the prediction only uses information on G.

verbose

A flag indicates whether detailed information is printed in console. Default is FALSE.

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")