Introduction
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.
Load the Data
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 Variables
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 Function
Run 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
)
#> [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"
#> [13] "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24"
#> [25] "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36"
#> [37] "37" "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48"
#> [49] "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
#> [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72"
#> [73] "73" "74" "75" "76" "77" "78" "79" "80" "81" "82" "83" "84"
#> [85] "85" "86" "87" "88" "89" "90" "91" "92" "93" "94" "95" "96"
#> [97] "97" "98" "99" "100"
#> [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"
#> [13] "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24"
#> [25] "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36"
#> [37] "37" "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48"
#> [49] "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
#> [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72"
#> [73] "73" "74" "75" "76" "77" "78" "79" "80" "81" "82" "83" "84"
#> [85] "85" "86" "87" "88" "89" "90" "91" "92" "93" "94" "95" "96"
#> [97] "97" "98" "99" "100"
#> [1] "51" "52" "53" "54" "55" "56" "57" "58" "59" "60" "61" "62"
#> [13] "63" "64" "65" "66" "67" "68" "69" "70" "71" "72" "73" "74"
#> [25] "75" "76" "77" "78" "79" "80" "81" "82" "83" "84" "85" "86"
#> [37] "87" "88" "89" "90" "91" "92" "93" "94" "95" "96" "97" "98"
#> [49] "99" "100" "101" "102" "103" "104" "105" "106" "107" "108" "109" "110"
#> [61] "111" "112" "113" "114" "115" "116" "117" "118" "119" "120" "121" "122"
#> [73] "123" "124" "125" "126" "127" "128" "129" "130" "131" "132" "133" "134"
#> [85] "135" "136" "137" "138" "139" "140" "141" "142" "143" "144" "145" "146"
#> [97] "147" "148" "149" "150"
#> [1] "51" "52" "53" "54" "55" "56" "57" "58" "59" "60" "61" "62"
#> [13] "63" "64" "65" "66" "67" "68" "69" "70" "71" "72" "73" "74"
#> [25] "75" "76" "77" "78" "79" "80" "81" "82" "83" "84" "85" "86"
#> [37] "87" "88" "89" "90" "91" "92" "93" "94" "95" "96" "97" "98"
#> [49] "99" "100" "101" "102" "103" "104" "105" "106" "107" "108" "109" "110"
#> [61] "111" "112" "113" "114" "115" "116" "117" "118" "119" "120" "121" "122"
#> [73] "123" "124" "125" "126" "127" "128" "129" "130" "131" "132" "133" "134"
#> [85] "135" "136" "137" "138" "139" "140" "141" "142" "143" "144" "145" "146"
#> [97] "147" "148" "149" "150"
#> [1] 200 5
#> [1] 200 5Examine the Output
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 0.5316585Notes
-
Custom Parameters: Modify parameters like
n1,n2,p, andlambdato test different scenarios. -
Data Preparation: Ensure datasets
(
S.1,S.2,U.1,U.2, etc.) are correctly loaded and aligned. - Output: Key components include the estimated zeta matrix, Frobenius norm difference, and updated embeddings.