GroupEff_par_usage

Introduction

The GroupEff_par function estimates group effects using embeddings and structured input data. This vignette demonstrates the usage of the GroupEff_par function with example data included in the package.

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Load the Required Library

Ensure the MUGS package is loaded before running the example:

library(MUGS)

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Load the Data

Load the required datasets for the example:

# Load required data
data(S.1)
data(S.2)
data(X.group.source)
data(X.group.target)
data(U.1)
data(U.2)

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Prepare Variables

Prepare the variables required for the GroupEff_par function:

# 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

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Run the Function

Run the GroupEff_par function:

  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
  )

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Examine the Output

Explore the structure and key components of the 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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Notes

1. Custom Parameters: Modify parameters like n.MGB, n.BCH, p, and lambda to test different scenarios.
2. Data Preparation: Ensure datasets (S.1, S.2, U.1, U.2, etc.) are correctly loaded and aligned.
3. Output: Key components include the estimated group effects matrix and regularization path.

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Summary

This vignette demonstrated how to use the GroupEff_par function for estimating group effects. Adjust input parameters and datasets to test different scenarios and interpret the output components for your analysis.