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## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
eval = FALSE
)
## ----setup--------------------------------------------------------------------
# library(MUGS)
## ----load_data----------------------------------------------------------------
# # Load required data
# data(S.1)
# data(S.2)
# data(X.group.source)
# data(X.group.target)
# data(U.1)
# data(U.2)
## ----prepare_variables--------------------------------------------------------
# # Extract names and create name lists
# names.list.1 <- rownames(S.1)
# names.list.2 <- rownames(S.2)
# full.name.list <- c(names.list.1, names.list.2)
#
# # Initialize beta matrix
# beta.names.1 <- unique(c(colnames(X.group.source), colnames(X.group.target)))
# beta.int <- matrix(0, 400, 10) # Replace with appropriate dimensions
# rownames(beta.int) <- beta.names.1
## ----run_function, eval=FALSE-------------------------------------------------
# GroupEff_par.out <- GroupEff_par(
# S.MGB = S.1,
# S.BCH = S.2,
# n.MGB = 2000,
# n.BCH = 2000,
# U.MGB = U.1,
# U.BCH = U.2,
# V.MGB = U.1,
# V.BCH = U.2,
# X.MGB.group = X.group.source,
# X.BCH.group = X.group.target,
# n.group = 400,
# name.list = full.name.list,
# beta.int = beta.int,
# lambda = 0,
# p = 10,
# n.core = 2
# )
## ----examine_output-----------------------------------------------------------
# # View structure of the output
# str(GroupEff_par.out)
#
# # Print specific components of the result
# cat("\nEstimated Group Effects:\n")
# print(GroupEff_par.out$effects[1:5, 1:3]) # Show the first 5 rows and 3 columns of effects
#
# cat("\nRegularization Path:\n")
# print(GroupEff_par.out$path)
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