Coerce arrays to data frames
array2data.frame.RdThis helper function creates a data.frame from a given array.
Arguments
- x
Matrix or array.
- rows, cols
integer vectors. These two vectors define a partition of the "dimensions" (1,...,n), where n is the number of dimensions of x (i.e. length(dim(x))). If either of the two is missing, then the complement is used. At least one of the arguments "rows" and "cols" has to be given.
See also
The helper function array2data.frame is used
internally for the plot methods.
The function bmatrix (which coerces arrays to matrices) is a simplified
version of array2data.frame.
Examples
# test array
x = test_array(dim = c(2,3,2), dimnames = TRUE)
array2data.frame(x, cols = c(1,3,2))
#> A=1.C=1.B=1 A=2.C=1.B=1 A=1.C=2.B=1 A=2.C=2.B=1 A=1.C=1.B=2 A=2.C=1.B=2
#> 1 111 211 112 212 121 221
#> A=1.C=2.B=2 A=2.C=2.B=2 A=1.C=1.B=3 A=2.C=1.B=3 A=1.C=2.B=3 A=2.C=2.B=3
#> 1 122 222 131 231 132 232
array2data.frame(x, rows = 1)
#> A B=1.C=1 B=2.C=1 B=3.C=1 B=1.C=2 B=2.C=2 B=3.C=2
#> 1 A=1 111 121 131 112 122 132
#> 2 A=2 211 221 231 212 222 232
array2data.frame(x, rows = 2:1)
#> B A C=1 C=2
#> 1 B=1 A=1 111 112
#> 2 B=2 A=1 121 122
#> 3 B=3 A=1 131 132
#> 4 B=1 A=2 211 212
#> 5 B=2 A=2 221 222
#> 6 B=3 A=2 231 232
array2data.frame(x, rows = c(2,1,3))
#> B A C value
#> 1 B=1 A=1 C=1 111
#> 2 B=2 A=1 C=1 121
#> 3 B=3 A=1 C=1 131
#> 4 B=1 A=2 C=1 211
#> 5 B=2 A=2 C=1 221
#> 6 B=3 A=2 C=1 231
#> 7 B=1 A=1 C=2 112
#> 8 B=2 A=1 C=2 122
#> 9 B=3 A=1 C=2 132
#> 10 B=1 A=2 C=2 212
#> 11 B=2 A=2 C=2 222
#> 12 B=3 A=2 C=2 232
# consider a pseudo socio economic data set
x = test_array(dim = c(2,4,5), random = TRUE)
dimnames(x) = list(sex=c('female','male'),
education = c('none','primary','high','university'),
age = c('<20','30-40','40-50','50-60','>60'))
array2data.frame(x, cols = 1)
#> education age female male
#> 1 none <20 0.2912870 -0.11639284
#> 2 primary <20 0.2286832 -0.21508529
#> 3 high <20 -1.9375715 1.57313868
#> 4 university <20 0.7390487 -0.05978496
#> 5 none 30-40 -1.5768686 -0.03036814
#> 6 primary 30-40 -0.1696321 1.75181919
#> 7 high 30-40 -0.7677395 -0.66016417
#> 8 university 30-40 1.4187800 -0.35884375
#> 9 none 40-50 -0.2217680 -1.18800769
#> 10 primary 40-50 1.5089138 -0.64586635
#> 11 high 40-50 -1.0834977 -0.73025679
#> 12 university 40-50 0.2551248 -1.35018112
#> 13 none 50-60 -0.7411637 0.97666206
#> 14 primary 50-60 -0.7880937 0.58949420
#> 15 high 50-60 0.2074870 -2.26713309
#> 16 university 50-60 0.4030332 -1.91325645
#> 17 none >60 1.1638880 -0.76630448
#> 18 primary >60 -0.4986021 -1.12765643
#> 19 high >60 1.0491673 -1.48171641
#> 20 university >60 -0.3608579 1.07850521
array2data.frame(x, cols = c(1,2))
#> age female.none male.none female.primary male.primary female.high
#> 1 <20 0.2912870 -0.11639284 0.2286832 -0.2150853 -1.9375715
#> 2 30-40 -1.5768686 -0.03036814 -0.1696321 1.7518192 -0.7677395
#> 3 40-50 -0.2217680 -1.18800769 1.5089138 -0.6458664 -1.0834977
#> 4 50-60 -0.7411637 0.97666206 -0.7880937 0.5894942 0.2074870
#> 5 >60 1.1638880 -0.76630448 -0.4986021 -1.1276564 1.0491673
#> male.high female.university male.university
#> 1 1.5731387 0.7390487 -0.05978496
#> 2 -0.6601642 1.4187800 -0.35884375
#> 3 -0.7302568 0.2551248 -1.35018112
#> 4 -2.2671331 0.4030332 -1.91325645
#> 5 -1.4817164 -0.3608579 1.07850521
# convert an impulse response function into a data.frame
# generate a random statespace model with m=3 outputs, n=2 inputs and s=4 states.
model = stsp(A = matrix(stats::rnorm(16), nrow = 4, ncol = 4),
B = matrix(stats::rnorm(8), nrow = 4, ncol = 2),
C = matrix(stats::rnorm(12), nrow = 3, ncol = 4),
D = matrix(stats::rnorm(6), nrow = 3, ncol = 2))
k = unclass(pseries(model, lag.max = 25))
dimnames(k) = list(y = paste('y[',1:3,']',sep=''),
x = paste('x[',1:2,']',sep=''),
lag = 0:25)
head(array2data.frame(k, rows = c(1,3), cols=2))
#> y lag x[1] x[2]
#> 1 y[1] 0 -1.288669115 -0.9719267
#> 2 y[2] 0 0.009571166 -0.4121729
#> 3 y[3] 0 -0.802024722 0.8045345
#> 4 y[1] 1 -2.177071105 0.1324587
#> 5 y[2] 1 -0.366330235 0.2056882
#> 6 y[3] 1 3.433641161 4.1514886