The CodeEff_Matrix function estimates code effects using
left and right embeddings from source and target sites. This vignette
demonstrates how to use this function with example data provided in the
package.
Ensure the MUGS package is loaded before running the
example:
Load the required datasets for the example:
# Load required data
data(S.1)
data(S.2)
data(U.1)
data(U.2)
data(X.group.source)
data(X.group.target)Prepare the variables required for the CodeEff_Matrix
function:
# Set parameters
n1 <- 100
n2 <- 100
p <- 5
# Initial right embeddings
V.1 <- U.1
V.2 <- U.2
# Fix rownames to ensure alignment
n1.no <- n1 - 50 # if you know n.common = 50
rownames(U.1) <- as.character(seq_len(nrow(U.1))) # "1" to "n1"
rownames(U.2) <- as.character(seq(from = n1.no + 1, length.out = nrow(U.2)))
rownames(S.1) <- rownames(U.1)
rownames(S.2) <- rownames(U.2)
rownames(V.1) <- rownames(U.1)
rownames(V.2) <- rownames(U.2)
# Extract names and find common codes
names.list.1 <- rownames(S.1)
names.list.2 <- rownames(S.2)
common_codes <- intersect(names.list.1, names.list.2)
# Check for overlap
if (length(common_codes) == 0) stop("Error: No common codes found between S.1 and S.2.")
# Create zeta.int
full.name.list <- c(names.list.1, names.list.2)
zeta.int <- matrix(0, length(full.name.list), p)
rownames(zeta.int) <- full.name.listRun the CodeEff_Matrix function:
# Estimate code effects
CodeEff_Matrix.out <- CodeEff_Matrix(
S.1=S.1,
S.2=S.2,
n1=n1,
n2=n2,
U.1=U.1,
U.2=U.2,
V.1=U.1,
V.2=U.2,
common_codes = common_codes,
zeta.int = zeta.int,
lambda=10,
p=5
)Explore the structure and key components of the output:
# View structure of the output
str(CodeEff_Matrix.out)
#> List of 4
#> $ zeta : num [1:200, 1:5] -0.0398 -0.0586 0.0439 0.2047 -0.0651 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:200] "1" "2" "3" "4" ...
#> .. ..$ : NULL
#> $ dif_F : num 0.066
#> $ V.1.new: num [1:100, 1:5] 0.167 1.378 0.324 0.915 -0.608 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:100] "1" "2" "3" "4" ...
#> .. ..$ : NULL
#> $ V.2.new: num [1:100, 1:5] 1.3503 1.5287 0.0172 -1.1758 -0.2535 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:100] "51" "52" "53" "54" ...
#> .. ..$ : NULL
# Print specific components of the result
cat("\nEstimated Code Effects (zeta):\n")
#>
#> Estimated Code Effects (zeta):
print(CodeEff_Matrix.out$zeta[1:5, 1:3]) # Show the first 5 rows and 3 columns of zeta
#> [,1] [,2] [,3]
#> 1 -0.03977591 -0.016382264 0.072139614
#> 2 -0.05861561 0.102410067 -0.063516152
#> 3 0.04393681 -0.115463039 -0.199989211
#> 4 0.20468193 -0.002688834 -0.004118189
#> 5 -0.06513334 -0.153351503 -0.130099178
cat("\nFrobenius Norm Difference (dif_F):\n")
#>
#> Frobenius Norm Difference (dif_F):
print(CodeEff_Matrix.out$dif_F)
#> [1] 0.06595868
cat("\nUpdated Right Embeddings for Source Site (V.1.new):\n")
#>
#> Updated Right Embeddings for Source Site (V.1.new):
print(CodeEff_Matrix.out$V.1.new[1:5, 1:3]) # Show first 5 rows and 3 columns of V.1.new
#> [,1] [,2] [,3]
#> 1 0.1665095 0.814170162 0.3663227
#> 2 1.3783222 -1.960960373 0.2720757
#> 3 0.3239034 -0.006156115 0.7451411
#> 4 0.9147213 -0.935517116 -1.1990329
#> 5 -0.6076534 1.054516303 1.0303034
cat("\nUpdated Right Embeddings for Target Site (V.2.new):\n")
#>
#> Updated Right Embeddings for Target Site (V.2.new):
print(CodeEff_Matrix.out$V.2.new[1:5, 1:3]) # Show first 5 rows and 3 columns of V.2.new
#> [,1] [,2] [,3]
#> 51 1.35027749 -0.9642268 0.4754664
#> 52 1.52874085 0.9799293 -0.5918077
#> 53 0.01721724 0.4860534 -1.8969629
#> 54 -1.17575003 0.6388669 0.2976318
#> 55 -0.25350442 -0.7676733 0.5316585n1, n2, p, and
lambda to test different scenarios.S.1, S.2, U.1, U.2,
etc.) are correctly loaded and aligned.This vignette demonstrated how to use the CodeEff_Matrix
function for estimating code effects. Adjust input parameters and
datasets to test different scenarios and interpret the output components
for your analysis.