I'm trying to fill a matrix with vectors of different sizes. In fact, my goal here is to generate multiple subsets of data to perform a process of retraining a time series for a forecasting model.
To operationalize this, I tried the following code in R:
ST_Nile <- Nile
n <- length(ST_Nile)
k <- 5
qty_modelos <- 6
m <- c(rep(NA,qty_modelos))
t <- c(rep(NA,qty_modelos))
TrainNile <- c(rep(NA,95))
Matriz_train <- matrix(ncol = 95, nrow = qty_modelos, byrow = TRUE)
for (i in 1:qty_modelos) {
if (i==1) {
m[i] <- 30
} else { m[i] <- m[i-1] - k}
t[i] <- n - m[i]
TrainNile[i] <- ST_Nile[1:t[i]]
if (length(TrainNile[i])==95) {
Matriz_train <- append(Matriz_train[i,],TrainNile[i])
} else{
TrainNile[i] = c(TrainNile[i], rep(NA, 95 - length(TrainNile[i])))
Matriz_train <- append(Matriz_train[i,],TrainNile[i])
}
}
I expected fill the vectors with size different from the largest one with 'NA', but I get the error "number of items to replace is not a multiple of replacement length". Any ideas on how to fill this matrix?
Output of dput(Nile):
structure(c(1120, 1160, 963, 1210, 1160, 1160, 813, 1230, 1370, 1140, 995, 935, 1110, 994, 1020, 960, 1180, 799, 958, 1140, 1100, 1210, 1150, 1250, 1260, 1220, 1030, 1100, 774, 840, 874, 694, 940, 833, 701, 916, 692, 1020, 1050, 969, 831, 726, 456, 824, 702, 1120, 1100, 832, 764, 821, 768, 845, 864, 862, 698, 845, 744, 796, 1040, 759, 781, 865, 845, 944, 984, 897, 822, 1010, 771, 676, 649, 846, 812, 742, 801, 1040, 860, 874, 848, 890, 744, 749, 838, 1050, 918, 986, 797, 923, 975, 815, 1020, 906, 901, 1170, 912, 746, 919, 718, 714, 740), .Tsp = c(1871, 1970, 1), class = "ts")
There's a good chance I interpreted this somewhat differently, but it should still illustrate the main point:
vectors can be set to a length and right-padded with NAs with lenght(x) <- y
To build a 6x95 matrix of varying-length vector subsets where vectors are padded with NA values to match the length of the longest vector, something like this should be enough:
Nile <- structure(c(1120, 1160, 963, 1210, 1160, 1160, 813, 1230, 1370, 1140, 995, 935, 1110, 994, 1020, 960, 1180, 799, 958, 1140, 1100, 1210, 1150, 1250, 1260, 1220, 1030, 1100, 774, 840, 874, 694, 940, 833, 701, 916, 692, 1020, 1050, 969, 831, 726, 456, 824, 702, 1120, 1100, 832, 764, 821, 768, 845, 864, 862, 698, 845, 744, 796, 1040, 759, 781, 865, 845, 944, 984, 897, 822, 1010, 771, 676, 649, 846, 812, 742, 801, 1040, 860, 874, 848, 890, 744, 749, 838, 1050, 918, 986, 797, 923, 975, 815, 1020, 906, 901, 1170, 912, 746, 919, 718, 714, 740), .Tsp = c(1871, 1970, 1), class = "ts")
ST_Nile <- Nile
(n <- length(ST_Nile))
#> [1] 100
# sequence of end indeces for subsets
(t <- n - seq(from = 30, length.out = 6, by = -5))
#> [1] 70 75 80 85 90 95
# varying-length subsets to a list
train_lst <- lapply(t, \(x) ST_Nile[1:x])
str(train_lst)
#> List of 6
#> $ : num [1:70] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#> $ : num [1:75] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#> $ : num [1:80] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#> $ : num [1:85] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#> $ : num [1:90] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#> $ : num [1:95] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
# length of the longest vector in the list
(max_len <- lengths(train_lst) |> max())
#> [1] 95
# apply length(x)<-max_len on all vectors in the list
train_lst_padded <- lapply(train_lst, `length<-`, max_len)
# updated lengths:
str(train_lst_padded)
#> List of 6
#> $ : num [1:95] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#> $ : num [1:95] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#> $ : num [1:95] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#> $ : num [1:95] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#> $ : num [1:95] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#> $ : num [1:95] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
# first vector, padded with NA values:
train_lst_padded[[1]]
#> [1] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 995 935 1110 994 1020
#> [16] 960 1180 799 958 1140 1100 1210 1150 1250 1260 1220 1030 1100 774 840
#> [31] 874 694 940 833 701 916 692 1020 1050 969 831 726 456 824 702
#> [46] 1120 1100 832 764 821 768 845 864 862 698 845 744 796 1040 759
#> [61] 781 865 845 944 984 897 822 1010 771 676 NA NA NA NA NA
#> [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
#> [91] NA NA NA NA NA
# bind all rows, return 6x95 matrix
(Matriz_train <- do.call(rbind, train_lst_padded))
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 995 935 1110 994
#> [2,] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 995 935 1110 994
#> [3,] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 995 935 1110 994
#> [4,] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 995 935 1110 994
#> [5,] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 995 935 1110 994
#> [6,] 1120 1160 963 1210 1160 1160 813 1230 1370 1140 995 935 1110 994
#> [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,] 1020 960 1180 799 958 1140 1100 1210 1150 1250 1260 1220
#> [2,] 1020 960 1180 799 958 1140 1100 1210 1150 1250 1260 1220
#> [3,] 1020 960 1180 799 958 1140 1100 1210 1150 1250 1260 1220
#> [4,] 1020 960 1180 799 958 1140 1100 1210 1150 1250 1260 1220
#> [5,] 1020 960 1180 799 958 1140 1100 1210 1150 1250 1260 1220
#> [6,] 1020 960 1180 799 958 1140 1100 1210 1150 1250 1260 1220
#> [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
#> [1,] 1030 1100 774 840 874 694 940 833 701 916 692 1020
#> [2,] 1030 1100 774 840 874 694 940 833 701 916 692 1020
#> [3,] 1030 1100 774 840 874 694 940 833 701 916 692 1020
#> [4,] 1030 1100 774 840 874 694 940 833 701 916 692 1020
#> [5,] 1030 1100 774 840 874 694 940 833 701 916 692 1020
#> [6,] 1030 1100 774 840 874 694 940 833 701 916 692 1020
#> [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
#> [1,] 1050 969 831 726 456 824 702 1120 1100 832 764 821
#> [2,] 1050 969 831 726 456 824 702 1120 1100 832 764 821
#> [3,] 1050 969 831 726 456 824 702 1120 1100 832 764 821
#> [4,] 1050 969 831 726 456 824 702 1120 1100 832 764 821
#> [5,] 1050 969 831 726 456 824 702 1120 1100 832 764 821
#> [6,] 1050 969 831 726 456 824 702 1120 1100 832 764 821
#> [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62]
#> [1,] 768 845 864 862 698 845 744 796 1040 759 781 865
#> [2,] 768 845 864 862 698 845 744 796 1040 759 781 865
#> [3,] 768 845 864 862 698 845 744 796 1040 759 781 865
#> [4,] 768 845 864 862 698 845 744 796 1040 759 781 865
#> [5,] 768 845 864 862 698 845 744 796 1040 759 781 865
#> [6,] 768 845 864 862 698 845 744 796 1040 759 781 865
#> [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74]
#> [1,] 845 944 984 897 822 1010 771 676 NA NA NA NA
#> [2,] 845 944 984 897 822 1010 771 676 649 846 812 742
#> [3,] 845 944 984 897 822 1010 771 676 649 846 812 742
#> [4,] 845 944 984 897 822 1010 771 676 649 846 812 742
#> [5,] 845 944 984 897 822 1010 771 676 649 846 812 742
#> [6,] 845 944 984 897 822 1010 771 676 649 846 812 742
#> [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86]
#> [1,] NA NA NA NA NA NA NA NA NA NA NA NA
#> [2,] 801 NA NA NA NA NA NA NA NA NA NA NA
#> [3,] 801 1040 860 874 848 890 NA NA NA NA NA NA
#> [4,] 801 1040 860 874 848 890 744 749 838 1050 918 NA
#> [5,] 801 1040 860 874 848 890 744 749 838 1050 918 986
#> [6,] 801 1040 860 874 848 890 744 749 838 1050 918 986
#> [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95]
#> [1,] NA NA NA NA NA NA NA NA NA
#> [2,] NA NA NA NA NA NA NA NA NA
#> [3,] NA NA NA NA NA NA NA NA NA
#> [4,] NA NA NA NA NA NA NA NA NA
#> [5,] 797 923 975 815 NA NA NA NA NA
#> [6,] 797 923 975 815 1020 906 901 1170 912
Created on 2023-11-21 with reprex v2.0.2