LUCIDus (version 3.2.1)

lucid: Fit a lucid model for integrated analysis on exposure, outcome and multi-omics data, allowing for tuning

Description

Fit a lucid model for integrated analysis on exposure, outcome and multi-omics data, allowing for tuning

Usage

lucid(
  G,
  Z,
  Y,
  CoG = NULL,
  CoY = NULL,
  family = c("normal", "binary"),
  K = 2,
  lucid_model = c("early", "parallel", "serial"),
  Rho_G = 0,
  Rho_Z_Mu = 0,
  Rho_Z_Cov = 0,
  verbose_tune = FALSE,
  ...
)

Value

A fitted LUCID model of class early_lucid, lucid_parallel or lucid_serial -- the candidate with the lowest BIC when K or any penalty is given as a vector, and otherwise simply the single fitted model. The components are those documented in estimate_lucid, with one addition:

selection

Present for "early" only, and only when a non-zero penalty selected a strict subset of the input variables. Records what the tuned penalties dropped, as selectG and selectZ (logical vectors over the original inputs), the corresponding Gnames and Znames, and the tuned Rho that produced the selection. Because lucid refits the selected model unpenalized on the retained features, the model's own select component describes the refit dimensions and indexes res_Beta and res_Mu; use selection to see what was dropped from the original data.

Arguments

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 either a matrix with N rows and p_i features, or a list with two or more matrices with N rows.

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.

family

Distribution of outcome. For continuous outcome, use "normal"; for binary outcome, use "binary". Default is "normal".

K

Number of latent clusters to be tuned. For lucid_model = "early", number of latent clusters (should be greater or equal than 2). Either an integer or a vector of integer. If K is a vector, model selection on K is performed. For lucid_model = "parallel",a list with vectors of integers or just integers, same length as Z, if the element itself is a vector, model selection on K is performed; For lucid_model = "serial", a list, each element is either an integer or an list of integers, same length as Z, if the smallest element (integer) itself is a vector, model selection on K is performed

lucid_model

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

Rho_G

A scalar or a vector. This parameter is the LASSO penalty to regularize exposure coefficients in the G-to-X model; CoG covariates are not penalized. If it is a vector, lucid will call tune_lucid to conduct model selection and variable selection. User can try penalties from 0 to 1. Penalty tuning is supported for "early" and "parallel". For "serial", only scalar penalty inputs are supported.

Rho_Z_Mu

A scalar or a vector. This parameter is the LASSO penalty to regularize cluster-specific means for omics data (Z). If it is a vector, lucid will call tune_lucid to conduct model selection and variable selection. User can try penalties from 1 to 100. Penalty tuning is supported for "early" and "parallel". For "serial", only scalar penalty inputs are supported.

Rho_Z_Cov

A scalar or a vector. This parameter is the graphical LASSO penalty to estimate sparse cluster-specific variance-covariance matrices for omics data (Z). If it is a vector, lucid will call tune_lucid to conduct model selection and variable selection. User can try penalties from 0 to 1. Penalty tuning is supported for "early" and "parallel". For "serial", only scalar penalty inputs are supported.

verbose_tune

A flag to print details of tuning process.

...

Other parameters passed to estimate_lucid

Examples

# \donttest{
# LUCID early integration (quick smoke example)
G <- sim_data$G[1:80, , drop = FALSE]
Z <- sim_data$Z[1:80, , drop = FALSE]
Y <- sim_data$Y_normal[1:80]
fit_early <- lucid(
  G = G, Z = Z, Y = Y,
  lucid_model = "early", family = "normal", K = 2,
  max_itr = 30, max_tot.itr = 60, seed = 1008
)

# LUCID in parallel (two layers)
i <- 1008
set.seed(i)
G <- matrix(rnorm(240), nrow = 80)
Z1 <- matrix(rnorm(320), nrow = 80)
Z2 <- matrix(rnorm(320), nrow = 80)
Z <- list(Z1 = Z1, Z2 = Z2)
CoY <- matrix(rnorm(160), nrow = 80)
CoG <- matrix(rnorm(160), nrow = 80)
Y <- rnorm(80)
fit_parallel <- lucid(
  G = G, Z = Z, Y = Y, K = list(2, 2),
  CoG = CoG, CoY = CoY, lucid_model = "parallel",
  family = "normal", seed = i,
  max_itr = 30, max_tot.itr = 60
)
# }