# 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.

## Load the Required Library

Ensure the `MUGS` package is loaded before running the example:

    library(MUGS)

## 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)

## 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

## 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
      )

## 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)

## 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.

## 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.