Assuming aggregates are calculated via a dummy matrix by
z = t(x) %*% y,
the n largest contributions are found (value or index) for each aggregate.
Usage
MaxContribution(
x,
y,
n = 1,
decreasing = TRUE,
index = FALSE,
groups = NULL,
return2 = FALSE
)Arguments
- x
A (sparse) dummy matrix
- y
Vector of input values (contributors)
- n
Number of contributors to be found
- decreasing
Ordering parameter. Smallest contributors found when
FALSE.- index
Indices to
yreturned when TRUE- groups
When non-NULL, major contributions after aggregation within groups. Cannot be combined with
index = TRUE. The missing group category is excluded.- return2
When
TRUE, two matrices are returned,valueandid. The latter contains indices whengroupisNULLand otherwise a character matrix of groups.
Value
Matrix with lagest contributions in first column, second largest in second column and so on.
Alternative output when using parameters index or return2.
Examples
library(SSBtools)
#> Loading required package: Matrix
z <- SSBtoolsData("sprt_emp_withEU")
z$age[z$age == "Y15-29"] <- "young"
z$age[z$age == "Y30-64"] <- "old"
a <- ModelMatrix(z, formula = ~age + geo, crossTable = TRUE)
cbind(as.data.frame(a$crossTable), MaxContribution(a$modelMatrix, z$ths_per, 1))
#> age geo MaxContribution(a$modelMatrix, z$ths_per, 1)
#> 1 Total Total 122.1
#> 2 old Total 122.1
#> 3 young Total 69.1
#> 4 Total Iceland 1.9
#> 5 Total Portugal 25.8
#> 6 Total Spain 122.1
cbind(a$crossTable, MaxContribution(a$modelMatrix, z$ths_per, 10))
#> age geo 1 2 3 4 5 6 7 8 9 10
#> 1 Total Total 122.1 120.3 119.6 69.1 66.9 63.4 25.8 24.3 20.2 14.2
#> 2 old Total 122.1 120.3 119.6 25.8 24.3 20.2 1.9 1.6 1.5 NA
#> 3 young Total 69.1 66.9 63.4 14.2 12.7 11.6 1.9 1.9 1.8 NA
#> 4 Total Iceland 1.9 1.9 1.9 1.8 1.6 1.5 NA NA NA NA
#> 5 Total Portugal 25.8 24.3 20.2 14.2 12.7 11.6 NA NA NA NA
#> 6 Total Spain 122.1 120.3 119.6 69.1 66.9 63.4 NA NA NA NA
cbind(a$crossTable, MaxContribution(a$modelMatrix, z$ths_per, 10, index = TRUE))
#> age geo 1 2 3 4 5 6 7 8 9 10
#> 1 Total Total 16 4 10 13 1 7 18 12 6 9
#> 2 old Total 16 4 10 18 12 6 17 11 5 NA
#> 3 young Total 13 1 7 9 15 3 8 14 2 NA
#> 4 Total Iceland 8 14 17 2 11 5 NA NA NA NA
#> 5 Total Portugal 18 12 6 9 15 3 NA NA NA NA
#> 6 Total Spain 16 4 10 13 1 7 NA NA NA NA
# Both types of output can be achieved with return2 = TRUE)
identical(MaxContribution(a$modelMatrix, z$ths_per, 10, return2 = TRUE),
list(value = MaxContribution(a$modelMatrix, z$ths_per, 10),
id = MaxContribution(a$modelMatrix, z$ths_per, 10, index = TRUE)))
#> [1] FALSE
b <- ModelMatrix(z[, -4], crossTable = TRUE, inputInOutput = c(TRUE, FALSE, TRUE))
k <- cbind(b$crossTable, MaxContribution(b$modelMatrix, z$ths_per, 10))
gr18 <- paste0("g", 1:18) # Each row is a group
k18 <- cbind(b$crossTable, MaxContribution(b$modelMatrix, z$ths_per, 10, groups = gr18))
identical(k, k18) # TRUE
#> [1] TRUE
gr9 <- paste0("g", as.integer(10 * z$ths_per)%%10) # 9 groups from decimal
k9 <- cbind(b$crossTable, MaxContribution(b$modelMatrix, z$ths_per, 10, groups = gr9))
k18[c(4, 13, 17, 33), ]
#> age geo year 1 2 3 4 5 6 7 8 9 10
#> 4 Total Total 2016 122.1 69.1 25.8 12.7 1.9 1.9 NA NA NA NA
#> 13 old Total Total 122.1 120.3 119.6 25.8 24.3 20.2 1.9 1.6 1.5 NA
#> 17 old EU Total 122.1 120.3 119.6 25.8 24.3 20.2 NA NA NA NA
#> 33 young nonEU Total 1.9 1.9 1.8 NA NA NA NA NA NA NA
k9[c(4, 13, 17, 33), ]
#> age geo year 1 2 3 4 5 6 7 8 9 10
#> 4 Total Total 2016 191.2 25.8 12.7 3.8 NA NA NA NA NA NA
#> 13 old Total Total 144.6 122.1 121.2 25.8 20.2 1.9 1.5 NA NA NA
#> 17 old EU Total 144.6 122.1 119.6 25.8 20.2 NA NA NA NA NA
#> 33 young nonEU Total 3.8 1.8 NA NA NA NA NA NA NA NA
# Group info obtained with return2 = TRUE
k9_id <- cbind(b$crossTable, MaxContribution(b$modelMatrix, z$ths_per, 10, groups = gr9,
return2 = TRUE)$id)
k9_id[c(4, 13, 17, 33), ]
#> age geo year 1 2 3 4 5 6 7 8 9 10
#> 4 Total Total 2016 g1 g8 g7 g9 <NA> <NA> <NA> <NA> <NA> <NA>
#> 13 old Total Total g3 g1 g6 g8 g2 g9 g5 <NA> <NA> <NA>
#> 17 old EU Total g3 g1 g6 g8 g2 <NA> <NA> <NA> <NA> <NA>
#> 33 young nonEU Total g9 g8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
# Verify similarity
z$y <- z$ths_per + (1:nrow(z))/100 # to avoid equal values
id1 <- MaxContribution(b$modelMatrix, z$y, 10, index = TRUE)
id1[!is.na(id1)] <- paste0("g", id1[!is.na(id1)])
mc2 <- MaxContribution(b$modelMatrix, z$y, 10, groups = gr18, return2 = TRUE)
id2 <- mc2$id
identical(id1, id2)
#> [1] TRUE