R：记录函数的索引迭代
R: Recording the Index Iterations of a Function
我正在使用 R 编程语言。在上一题（）中，我学习了如何“根据不同的条件将数据集中的行随机替换为0”：
第 1 步：数据集有 10 个变量 - 在第 1 步中，随机 select“n”个变量（“n”必须小于 10）。
第 2 步：对于上述“n”个变量，如果它们是“因子”，则随机 select 每个水平的子集（大小为“m”）因子变量。对于每个非因子变量，在它们的最小值和最大值之间的一个点随机拆分它们（称这个点为“p”）。
第 3 步：生成一个介于 0 和 1 之间的随机数（称为“r”）。
步骤 4：Select 在步骤 2 中标识的所有行。对于这些行，请考虑逻辑条件中未使用的列。对于这些列，这些行中的任何元素都可以用 0 替换的概率为“r”。
步骤 5：重复步骤 1 - 步骤 4 10 次。
set.seed(123)
num_var_1 <- rnorm(1000, 10, 1)
num_var_2 <- rnorm(1000, 10, 5)
num_var_3 <- rnorm(1000, 10, 10)
num_var_4 <- rnorm(1000, 10, 10)
num_var_5 <- rnorm(1000, 10, 10)
factor_1 <- c("A","B", "C")
factor_2 <- c("AA","BB", "CC")
factor_3 <- c("AAA","BBB", "CCC", "DDD")
factor_4 <- c("AAAA","BBBB", "CCCC", "DDDD", "EEEE")
factor_5 <- c("AAAAA","BBBBB", "CCCCC", "DDDDD", "EEEEE", "FFFFFF")
factor_var_1 <- as.factor(sample(factor_1, 1000, replace=TRUE, prob=c(0.3, 0.5, 0.2)))
factor_var_2 <- as.factor(sample(factor_2, 1000, replace=TRUE, prob=c(0.5, 0.3, 0.2)))
factor_var_3 <- as.factor(sample(factor_3, 1000, replace=TRUE, prob=c(0.5, 0.2, 0.2, 0.1)))
factor_var_4 <- as.factor(sample(factor_4, 1000, replace=TRUE, prob=c(0.5, 0.2, 0.1, 0.1, 0.1)))
factor_var_5 <- as.factor(sample(factor_4, 1000, replace=TRUE, prob=c(0.3, 0.2, 0.1, 0.1, 0.1)))
my_data = data.frame(num_var_1, num_var_2, num_var_3, num_var_4, num_var_5, factor_var_1, factor_var_2, factor_var_3, factor_var_4, factor_var_5)
random_drop <- function(x) {
# Randomly select variables
which_vars <- names(x[, sort(sample(ncol(x), sample(ncol(x), 1)))])
# Randomly select factor levels subset or generate continuous cutoff value
cutoff_vals <- lapply(
which_vars,
function(i) {
if (is.factor(x[[i]])) {
return(sample(levels(x[[i]]), sample(nlevels(x[[i]]), 1)))
}
runif(1, min(x[[i]], na.rm = TRUE), max(x[[i]], na.rm = TRUE))
}
)
names(cutoff_vals) <- which_vars
# Create random prob value
r <- runif(1,0,1)
# Generate idx for which rows to select
row_idx <- Reduce(
`&`,
lapply(
which_vars,
function(i) {
if (is.factor(x[[i]])) {
return(x[[i]] %in% cutoff_vals[[i]])
}
x[[i]] > cutoff_vals[[i]]
}
)
)
x_sub <- x[row_idx, !colnames(x) %in% which_vars, drop = FALSE]
# With prob. 'r' fill row values in with '0'
r_mat <- matrix(
sample(
c(TRUE, FALSE),
ncol(x_sub)*nrow(x_sub),
replace = TRUE,
prob = c(r, 1 - r)
),
nrow = nrow(x_sub),
ncol = ncol(x_sub)
)
x_sub[r_mat] <- 0
x[row_idx, !colnames(x) %in% which_vars] <- x_sub
return(x)
}
random_drop_recurse <- function(x, n = 10) {
if (n == 1) return(random_drop(x))
random_drop_recurse(random_drop(x), n = n - 1)
}
suppressWarnings(
head(
random_drop_recurse(my_data[, c(1:3, 6:8)], 10),
20
)
)
#> num_var_1 num_var_2 num_var_3 factor_var_1 factor_var_2 factor_var_3
#> 1 9.439524 5.021006 4.883963 B AA AAA
#> 2 9.769823 4.800225 12.369379 B AA AAA
#> 3 11.558708 9.910099 0.000000 C AA BBB
#> 4 10.070508 9.339124 22.192276 B CC DDD
#> 5 10.129288 -2.746714 11.741359 B AA AAA
#> 6 11.715065 15.202867 3.847317 <NA> AA CCC
#> 7 10.460916 11.248629 -8.068930 C CC <NA>
#> 8 8.734939 22.081037 0.000000 C AA BBB
#> 9 9.313147 13.425991 30.460189 C AA BBB
#> 10 9.554338 7.765203 4.392376 B AA AAA
#> 11 11.224082 23.986956 1.640007 A <NA> AAA
#> 12 10.359814 24.161130 16.529475 A AA AAA
#> 13 0.000000 3.906441 0.000000 A CC <NA>
#> 14 10.110683 12.345160 17.516291 B CC AAA
#> 15 9.444159 8.943765 7.220249 A AA DDD
#> 16 11.786913 10.935256 21.226542 B CC DDD
#> 17 10.497850 11.137714 -1.726089 B AA AAA
#> 18 8.033383 3.690498 9.511232 B CC CCC
#> 19 10.701356 11.427948 2.958597 B BB AAA
#> 20 9.527209 18.746237 16.807586 C AA BBB
问题：现在，我正在尝试学习如何记录每次迭代的结果——也就是说，每次变量组合被select编辑到替换为0，我想记录那个组合。
有人可以告诉我怎么做吗？
谢谢！
嘿@stats555 感谢您将其分解为一个新问题！我对前一个问题中的代码做了非常小的调整；即 random_drop_recurse 函数现在如下所示：
random_drop_recurse <- function(x, n = 10) {
if (n == 1) {
dropped <- random_drop(x)
return(list(x = dropped, x_dropped = is.na(dropped) | dropped == 0))
}
random_drop_recurse(random_drop(x), n = n - 1)
}
它现在 returns 是一个布尔矩阵，显示所有已删除的索引，而不是仅返回包含已删除条目的矩阵。以下代码块演示了您提供的数据：
set.seed(123)
num_var_1 <- rnorm(1000, 10, 1)
num_var_2 <- rnorm(1000, 10, 5)
num_var_3 <- rnorm(1000, 10, 10)
num_var_4 <- rnorm(1000, 10, 10)
num_var_5 <- rnorm(1000, 10, 10)
factor_1 <- c("A","B", "C")
factor_2 <- c("AA","BB", "CC")
factor_3 <- c("AAA","BBB", "CCC", "DDD")
factor_4 <- c("AAAA","BBBB", "CCCC", "DDDD", "EEEE")
factor_5 <- c("AAAAA","BBBBB", "CCCCC", "DDDDD", "EEEEE", "FFFFFF")
factor_var_1 <- as.factor(sample(factor_1, 1000, replace=TRUE, prob=c(0.3, 0.5, 0.2)))
factor_var_2 <- as.factor(sample(factor_2, 1000, replace=TRUE, prob=c(0.5, 0.3, 0.2)))
factor_var_3 <- as.factor(sample(factor_3, 1000, replace=TRUE, prob=c(0.5, 0.2, 0.2, 0.1)))
factor_var_4 <- as.factor(sample(factor_4, 1000, replace=TRUE, prob=c(0.5, 0.2, 0.1, 0.1, 0.1)))
factor_var_5 <- as.factor(sample(factor_4, 1000, replace=TRUE, prob=c(0.3, 0.2, 0.1, 0.1, 0.1)))
my_data = data.frame(num_var_1, num_var_2, num_var_3, num_var_4, num_var_5, factor_var_1, factor_var_2, factor_var_3, factor_var_4, factor_var_5)
random_drop <- function(x) {
# Randomly select variables
which_vars <- names(x[, sort(sample(ncol(x), sample(ncol(x), 1)))])
# Randomly select factor levels subset or generate continuous cutoff value
cutoff_vals <- lapply(
which_vars,
function(i) {
if (is.factor(x[[i]])) {
return(sample(levels(x[[i]]), sample(nlevels(x[[i]]), 1)))
}
runif(1, min(x[[i]], na.rm = TRUE), max(x[[i]], na.rm = TRUE))
}
)
names(cutoff_vals) <- which_vars
# Create random prob value
r <- runif(1,0,1)
# Generate idx for which rows to select
row_idx <- Reduce(
`&`,
lapply(
which_vars,
function(i) {
if (is.factor(x[[i]])) {
return(x[[i]] %in% cutoff_vals[[i]])
}
x[[i]] > cutoff_vals[[i]]
}
)
)
x_sub <- x[row_idx, !colnames(x) %in% which_vars, drop = FALSE]
# With prob. 'r' fill row values in with '0'
r_mat <- matrix(
sample(
c(TRUE, FALSE),
ncol(x_sub)*nrow(x_sub),
replace = TRUE,
prob = c(r, 1 - r)
),
nrow = nrow(x_sub),
ncol = ncol(x_sub)
)
x_sub[r_mat] <- 0
x[row_idx, !colnames(x) %in% which_vars] <- x_sub
return(x)
}
random_drop_recurse <- function(x, n = 10) {
if (n == 1) {
dropped <- random_drop(x)
return(list(x = dropped, x_dropped = is.na(dropped) | dropped == 0))
}
random_drop_recurse(random_drop(x), n = n - 1)
}
test <- suppressWarnings(
random_drop_recurse(my_data[, c(1:3, 6:8)], 10)
)
# View the first 20 entries of the matrix with the dropped entries
test$x[1:20, ]
#> num_var_1 num_var_2 num_var_3 factor_var_1 factor_var_2 factor_var_3
#> 1 9.439524 5.021006 4.883963 B AA AAA
#> 2 9.769823 4.800225 12.369379 B AA AAA
#> 3 11.558708 9.910099 0.000000 C AA BBB
#> 4 10.070508 9.339124 22.192276 B CC DDD
#> 5 10.129288 -2.746714 11.741359 B AA AAA
#> 6 11.715065 15.202867 3.847317 <NA> AA CCC
#> 7 10.460916 11.248629 -8.068930 C CC <NA>
#> 8 8.734939 22.081037 0.000000 C AA BBB
#> 9 9.313147 13.425991 30.460189 C AA BBB
#> 10 9.554338 7.765203 4.392376 B AA AAA
#> 11 11.224082 23.986956 1.640007 A <NA> AAA
#> 12 10.359814 24.161130 16.529475 A AA AAA
#> 13 0.000000 3.906441 0.000000 A CC <NA>
#> 14 10.110683 12.345160 17.516291 B CC AAA
#> 15 9.444159 8.943765 7.220249 A AA DDD
#> 16 11.786913 10.935256 21.226542 B CC DDD
#> 17 10.497850 11.137714 -1.726089 B AA AAA
#> 18 8.033383 3.690498 9.511232 B CC CCC
#> 19 10.701356 11.427948 2.958597 B BB AAA
#> 20 9.527209 18.746237 16.807586 C AA BBB
# View the corresponding boolean matrix showing dropped indices
test$x_dropped[1:20, ]
#> num_var_1 num_var_2 num_var_3 factor_var_1 factor_var_2 factor_var_3
#> [1,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [2,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [3,] FALSE FALSE TRUE FALSE FALSE FALSE
#> [4,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [5,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [6,] FALSE FALSE FALSE TRUE FALSE FALSE
#> [7,] FALSE FALSE FALSE FALSE FALSE TRUE
#> [8,] FALSE FALSE TRUE FALSE FALSE FALSE
#> [9,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [10,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [11,] FALSE FALSE FALSE FALSE TRUE FALSE
#> [12,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [13,] TRUE FALSE TRUE FALSE FALSE TRUE
#> [14,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [15,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [16,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [17,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [18,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [19,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [20,] FALSE FALSE FALSE FALSE FALSE FALSE
# If you want the actual indices
which(test$x_dropped[1:20, ], arr.ind = TRUE)
#> row col
#> [1,] 13 1
#> [2,] 3 3
#> [3,] 8 3
#> [4,] 13 3
#> [5,] 6 4
#> [6,] 11 5
#> [7,] 7 6
#> [8,] 13 6
我正在使用 R 编程语言。在上一题（
第 1 步：数据集有 10 个变量 - 在第 1 步中，随机 select“n”个变量（“n”必须小于 10）。
第 2 步：对于上述“n”个变量，如果它们是“因子”，则随机 select 每个水平的子集（大小为“m”）因子变量。对于每个非因子变量，在它们的最小值和最大值之间的一个点随机拆分它们（称这个点为“p”）。
第 3 步：生成一个介于 0 和 1 之间的随机数（称为“r”）。
步骤 4：Select 在步骤 2 中标识的所有行。对于这些行，请考虑逻辑条件中未使用的列。对于这些列，这些行中的任何元素都可以用 0 替换的概率为“r”。
步骤 5：重复步骤 1 - 步骤 4 10 次。
set.seed(123) num_var_1 <- rnorm(1000, 10, 1) num_var_2 <- rnorm(1000, 10, 5) num_var_3 <- rnorm(1000, 10, 10) num_var_4 <- rnorm(1000, 10, 10) num_var_5 <- rnorm(1000, 10, 10) factor_1 <- c("A","B", "C") factor_2 <- c("AA","BB", "CC") factor_3 <- c("AAA","BBB", "CCC", "DDD") factor_4 <- c("AAAA","BBBB", "CCCC", "DDDD", "EEEE") factor_5 <- c("AAAAA","BBBBB", "CCCCC", "DDDDD", "EEEEE", "FFFFFF") factor_var_1 <- as.factor(sample(factor_1, 1000, replace=TRUE, prob=c(0.3, 0.5, 0.2))) factor_var_2 <- as.factor(sample(factor_2, 1000, replace=TRUE, prob=c(0.5, 0.3, 0.2))) factor_var_3 <- as.factor(sample(factor_3, 1000, replace=TRUE, prob=c(0.5, 0.2, 0.2, 0.1))) factor_var_4 <- as.factor(sample(factor_4, 1000, replace=TRUE, prob=c(0.5, 0.2, 0.1, 0.1, 0.1))) factor_var_5 <- as.factor(sample(factor_4, 1000, replace=TRUE, prob=c(0.3, 0.2, 0.1, 0.1, 0.1))) my_data = data.frame(num_var_1, num_var_2, num_var_3, num_var_4, num_var_5, factor_var_1, factor_var_2, factor_var_3, factor_var_4, factor_var_5) random_drop <- function(x) { # Randomly select variables which_vars <- names(x[, sort(sample(ncol(x), sample(ncol(x), 1)))]) # Randomly select factor levels subset or generate continuous cutoff value cutoff_vals <- lapply( which_vars, function(i) { if (is.factor(x[[i]])) { return(sample(levels(x[[i]]), sample(nlevels(x[[i]]), 1))) } runif(1, min(x[[i]], na.rm = TRUE), max(x[[i]], na.rm = TRUE)) } ) names(cutoff_vals) <- which_vars # Create random prob value r <- runif(1,0,1) # Generate idx for which rows to select row_idx <- Reduce( `&`, lapply( which_vars, function(i) { if (is.factor(x[[i]])) { return(x[[i]] %in% cutoff_vals[[i]]) } x[[i]] > cutoff_vals[[i]] } ) ) x_sub <- x[row_idx, !colnames(x) %in% which_vars, drop = FALSE] # With prob. 'r' fill row values in with '0' r_mat <- matrix( sample( c(TRUE, FALSE), ncol(x_sub)*nrow(x_sub), replace = TRUE, prob = c(r, 1 - r) ), nrow = nrow(x_sub), ncol = ncol(x_sub) ) x_sub[r_mat] <- 0 x[row_idx, !colnames(x) %in% which_vars] <- x_sub return(x) } random_drop_recurse <- function(x, n = 10) { if (n == 1) return(random_drop(x)) random_drop_recurse(random_drop(x), n = n - 1) } suppressWarnings( head( random_drop_recurse(my_data[, c(1:3, 6:8)], 10), 20 ) ) #> num_var_1 num_var_2 num_var_3 factor_var_1 factor_var_2 factor_var_3 #> 1 9.439524 5.021006 4.883963 B AA AAA #> 2 9.769823 4.800225 12.369379 B AA AAA #> 3 11.558708 9.910099 0.000000 C AA BBB #> 4 10.070508 9.339124 22.192276 B CC DDD #> 5 10.129288 -2.746714 11.741359 B AA AAA #> 6 11.715065 15.202867 3.847317 <NA> AA CCC #> 7 10.460916 11.248629 -8.068930 C CC <NA> #> 8 8.734939 22.081037 0.000000 C AA BBB #> 9 9.313147 13.425991 30.460189 C AA BBB #> 10 9.554338 7.765203 4.392376 B AA AAA #> 11 11.224082 23.986956 1.640007 A <NA> AAA #> 12 10.359814 24.161130 16.529475 A AA AAA #> 13 0.000000 3.906441 0.000000 A CC <NA> #> 14 10.110683 12.345160 17.516291 B CC AAA #> 15 9.444159 8.943765 7.220249 A AA DDD #> 16 11.786913 10.935256 21.226542 B CC DDD #> 17 10.497850 11.137714 -1.726089 B AA AAA #> 18 8.033383 3.690498 9.511232 B CC CCC #> 19 10.701356 11.427948 2.958597 B BB AAA #> 20 9.527209 18.746237 16.807586 C AA BBB
问题：现在，我正在尝试学习如何记录每次迭代的结果——也就是说，每次变量组合被select编辑到替换为0，我想记录那个组合。
有人可以告诉我怎么做吗？
谢谢！
嘿@stats555 感谢您将其分解为一个新问题！我对前一个问题中的代码做了非常小的调整；即 random_drop_recurse 函数现在如下所示：
random_drop_recurse <- function(x, n = 10) {
if (n == 1) {
dropped <- random_drop(x)
return(list(x = dropped, x_dropped = is.na(dropped) | dropped == 0))
}
random_drop_recurse(random_drop(x), n = n - 1)
}
它现在 returns 是一个布尔矩阵，显示所有已删除的索引，而不是仅返回包含已删除条目的矩阵。以下代码块演示了您提供的数据：
set.seed(123)
num_var_1 <- rnorm(1000, 10, 1)
num_var_2 <- rnorm(1000, 10, 5)
num_var_3 <- rnorm(1000, 10, 10)
num_var_4 <- rnorm(1000, 10, 10)
num_var_5 <- rnorm(1000, 10, 10)
factor_1 <- c("A","B", "C")
factor_2 <- c("AA","BB", "CC")
factor_3 <- c("AAA","BBB", "CCC", "DDD")
factor_4 <- c("AAAA","BBBB", "CCCC", "DDDD", "EEEE")
factor_5 <- c("AAAAA","BBBBB", "CCCCC", "DDDDD", "EEEEE", "FFFFFF")
factor_var_1 <- as.factor(sample(factor_1, 1000, replace=TRUE, prob=c(0.3, 0.5, 0.2)))
factor_var_2 <- as.factor(sample(factor_2, 1000, replace=TRUE, prob=c(0.5, 0.3, 0.2)))
factor_var_3 <- as.factor(sample(factor_3, 1000, replace=TRUE, prob=c(0.5, 0.2, 0.2, 0.1)))
factor_var_4 <- as.factor(sample(factor_4, 1000, replace=TRUE, prob=c(0.5, 0.2, 0.1, 0.1, 0.1)))
factor_var_5 <- as.factor(sample(factor_4, 1000, replace=TRUE, prob=c(0.3, 0.2, 0.1, 0.1, 0.1)))
my_data = data.frame(num_var_1, num_var_2, num_var_3, num_var_4, num_var_5, factor_var_1, factor_var_2, factor_var_3, factor_var_4, factor_var_5)
random_drop <- function(x) {
# Randomly select variables
which_vars <- names(x[, sort(sample(ncol(x), sample(ncol(x), 1)))])
# Randomly select factor levels subset or generate continuous cutoff value
cutoff_vals <- lapply(
which_vars,
function(i) {
if (is.factor(x[[i]])) {
return(sample(levels(x[[i]]), sample(nlevels(x[[i]]), 1)))
}
runif(1, min(x[[i]], na.rm = TRUE), max(x[[i]], na.rm = TRUE))
}
)
names(cutoff_vals) <- which_vars
# Create random prob value
r <- runif(1,0,1)
# Generate idx for which rows to select
row_idx <- Reduce(
`&`,
lapply(
which_vars,
function(i) {
if (is.factor(x[[i]])) {
return(x[[i]] %in% cutoff_vals[[i]])
}
x[[i]] > cutoff_vals[[i]]
}
)
)
x_sub <- x[row_idx, !colnames(x) %in% which_vars, drop = FALSE]
# With prob. 'r' fill row values in with '0'
r_mat <- matrix(
sample(
c(TRUE, FALSE),
ncol(x_sub)*nrow(x_sub),
replace = TRUE,
prob = c(r, 1 - r)
),
nrow = nrow(x_sub),
ncol = ncol(x_sub)
)
x_sub[r_mat] <- 0
x[row_idx, !colnames(x) %in% which_vars] <- x_sub
return(x)
}
random_drop_recurse <- function(x, n = 10) {
if (n == 1) {
dropped <- random_drop(x)
return(list(x = dropped, x_dropped = is.na(dropped) | dropped == 0))
}
random_drop_recurse(random_drop(x), n = n - 1)
}
test <- suppressWarnings(
random_drop_recurse(my_data[, c(1:3, 6:8)], 10)
)
# View the first 20 entries of the matrix with the dropped entries
test$x[1:20, ]
#> num_var_1 num_var_2 num_var_3 factor_var_1 factor_var_2 factor_var_3
#> 1 9.439524 5.021006 4.883963 B AA AAA
#> 2 9.769823 4.800225 12.369379 B AA AAA
#> 3 11.558708 9.910099 0.000000 C AA BBB
#> 4 10.070508 9.339124 22.192276 B CC DDD
#> 5 10.129288 -2.746714 11.741359 B AA AAA
#> 6 11.715065 15.202867 3.847317 <NA> AA CCC
#> 7 10.460916 11.248629 -8.068930 C CC <NA>
#> 8 8.734939 22.081037 0.000000 C AA BBB
#> 9 9.313147 13.425991 30.460189 C AA BBB
#> 10 9.554338 7.765203 4.392376 B AA AAA
#> 11 11.224082 23.986956 1.640007 A <NA> AAA
#> 12 10.359814 24.161130 16.529475 A AA AAA
#> 13 0.000000 3.906441 0.000000 A CC <NA>
#> 14 10.110683 12.345160 17.516291 B CC AAA
#> 15 9.444159 8.943765 7.220249 A AA DDD
#> 16 11.786913 10.935256 21.226542 B CC DDD
#> 17 10.497850 11.137714 -1.726089 B AA AAA
#> 18 8.033383 3.690498 9.511232 B CC CCC
#> 19 10.701356 11.427948 2.958597 B BB AAA
#> 20 9.527209 18.746237 16.807586 C AA BBB
# View the corresponding boolean matrix showing dropped indices
test$x_dropped[1:20, ]
#> num_var_1 num_var_2 num_var_3 factor_var_1 factor_var_2 factor_var_3
#> [1,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [2,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [3,] FALSE FALSE TRUE FALSE FALSE FALSE
#> [4,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [5,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [6,] FALSE FALSE FALSE TRUE FALSE FALSE
#> [7,] FALSE FALSE FALSE FALSE FALSE TRUE
#> [8,] FALSE FALSE TRUE FALSE FALSE FALSE
#> [9,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [10,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [11,] FALSE FALSE FALSE FALSE TRUE FALSE
#> [12,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [13,] TRUE FALSE TRUE FALSE FALSE TRUE
#> [14,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [15,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [16,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [17,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [18,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [19,] FALSE FALSE FALSE FALSE FALSE FALSE
#> [20,] FALSE FALSE FALSE FALSE FALSE FALSE
# If you want the actual indices
which(test$x_dropped[1:20, ], arr.ind = TRUE)
#> row col
#> [1,] 13 1
#> [2,] 3 3
#> [3,] 8 3
#> [4,] 13 3
#> [5,] 6 4
#> [6,] 11 5
#> [7,] 7 6
#> [8,] 13 6