The goal of long2lstmarray is to transform 2D
longitudinal data into 3D arrays suitable for neural networks training
that require longitudinal data (e.g. Long short-term memory). The array
output can be used by the R ‘keras’ or other similar packages as a
X/label set.
You can install the long2lstmarray from GitHub with:
We will follow a step-by-step approach, starting with the most basic function and advancing to the most advanced function. Note that the most advanced functions rely on the most basic ones to function properly.
The alsfrs_data dataset will be used to guide you
through the package functionality. This data is invented.
library(long2lstmarray)
head(alsfrs_data, n = 10)
#> # A tibble: 10 × 15
#> subjid visdy p1 p2 p3 p4 p5 p6 p7 p8 p9 p10
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 3 3 0 0 2 3 3 4 0 4
#> 2 1 151 0 0 2 4 3 0 3 1 3 3
#> 3 1 223 3 4 0 3 2 3 0 2 2 0
#> 4 1 372 1 3 1 3 3 3 4 3 1 1
#> 5 1 459 0 4 0 1 1 4 0 0 4 2
#> 6 1 535 2 2 4 1 1 0 2 3 0 1
#> 7 1 644 4 2 2 3 2 1 0 2 0 0
#> 8 1 759 4 0 4 1 2 3 0 2 1 3
#> 9 2 0 4 0 3 3 0 0 1 2 3 1
#> 10 2 244 3 4 0 4 0 2 1 4 2 4
#> # ℹ 3 more variables: x1r <dbl>, x2r <dbl>, x3r <dbl>get_var_sequence functionThe most basic function has the goal to retrieve the variable values from a subject/variable name pair, like this:
slice_var_sequence functionThen, the package has the ability to generate a matrix with various lags from a sequence. For example, take a simple numeric sequence:
slice_var_sequence(sequence = 1:10, lags = 3,
label_length = 1, label_output = TRUE)
#> $x
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 2 3 4
#> [3,] 3 4 5
#> [4,] 4 5 6
#> [5,] 5 6 7
#> [6,] 6 7 8
#> [7,] 7 8 9
#>
#> $y
#> [1] 4 5 6 7 8 9 10The result is a list with x representing the lags from
the sequence, and y represents the value that follows each
lag, and that will be used as label. If
label_output = FALSE, only x is returned. The
lags argument represents the number of columns of
x, and label_length represents how many values
after the lag is considered to be the label. If
label_length = 1, the label value is always the value
following the sliced sequence.
get_var_array functionThis function has the ability to generate a matrix with various lags
from a variable in a dataframe. This function is analogous to
slice_var_sequence but its scope is larger, because it
takes an data.frame as an argument, and so the
var to be sequenced has to stated. The
time_var is the time variable which is important to be
stated because it orders the lags correctly.
get_var_array(data = alsfrs_data, subj_var = "subjid",
var = "p3", time_var = "visdy",
lags = 5, label_length = 1,
label_output = TRUE)
#> $x
#> time1 time2 time3 time4 time5
#> seq1 0 2 0 1 0
#> seq2 2 0 1 0 4
#> seq3 0 1 0 4 2
#> seq4 3 0 2 3 4
#> seq5 0 2 3 4 3
#> seq6 2 3 4 3 1
#> seq7 3 4 3 1 3
#> seq8 4 3 1 3 3
#> seq9 3 1 3 3 0
#> seq10 1 3 3 0 2
#> seq11 3 3 0 2 4
#> seq12 3 0 2 4 1
#> seq13 0 2 4 1 0
#> seq14 2 4 1 0 1
#> seq15 0 1 1 3 2
#> seq16 1 1 3 2 4
#> seq17 1 3 2 4 1
#> seq18 3 2 4 1 0
#> seq19 2 4 1 0 3
#> seq20 1 0 3 0 2
#> seq21 0 3 0 2 1
#> seq22 3 0 2 1 4
#> seq23 0 2 1 4 4
#> seq24 2 1 4 4 4
#> seq25 1 4 4 4 2
#> seq26 4 4 4 2 4
#> seq27 4 4 2 4 4
#> seq28 4 2 1 3 0
#> seq29 2 1 3 0 1
#> seq30 1 3 0 1 0
#> seq31 3 0 1 0 4
#> seq32 0 1 0 4 1
#> seq33 4 4 4 1 0
#> seq34 4 4 1 0 2
#> seq35 4 1 0 2 2
#> seq36 1 0 2 2 3
#> seq37 0 2 2 3 0
#>
#> $y
#> [1] 4 2 4 3 1 3 3 0 2 4 1 0 1 2 4 1 0 3 4 1 4 4 4 2 4 4 0 1 0 4 1 0 2 2 3 0 2longitudinal_array functionThis function is analogous to the previous get_var_array function.
This function has the ability to generate a matrix with various lags
from various variables in a dataframe. The returned object is a 3D
array. The array dimensions are respectively, subject, time and
variable. If label_output is TRUE, a list with
the 3D array and vector with the labels is returned.
array3d <- longitudinal_array(alsfrs_data, "subjid",
vars = c("p1", "p2", "p3"),
time_var = "visdy", lags = 3,
label_output = FALSE)First dimension, representing the subjects (e.g. subjid
= 1):
Second dimension, representing time (e.g. first visit):
array3d[,1,]
#> p1 p2 p3
#> seq1 3 3 0
#> seq2 0 0 2
#> seq3 3 4 0
#> seq4 1 3 1
#> seq5 0 4 0
#> seq6 4 0 3
#> seq7 3 4 0
#> seq8 0 3 2
#> seq9 1 4 3
#> seq10 3 3 4
#> seq11 3 3 3
#> seq12 4 0 1
#> seq13 3 0 3
#> seq14 2 4 3
#> seq15 3 1 0
#> seq16 1 3 2
#> seq17 4 4 4
#> seq18 2 0 1
#> seq19 1 0 1
#> seq20 3 3 3
#> seq21 2 4 0
#> seq22 4 1 1
#> seq23 4 4 1
#> seq24 1 3 3
#> seq25 1 4 2
#> seq26 3 3 4
#> seq27 1 2 1
#> seq28 2 1 1
#> seq29 3 4 0
#> seq30 0 1 3
#> seq31 3 0 0
#> seq32 1 3 2
#> seq33 4 3 1
#> seq34 1 2 4
#> seq35 3 2 4
#> seq36 1 0 4
#> seq37 1 4 2
#> seq38 0 3 4
#> seq39 4 0 1
#> seq40 2 0 0
#> seq41 0 4 4
#> seq42 2 4 2
#> seq43 2 3 1
#> seq44 4 1 3
#> seq45 0 3 0
#> seq46 1 4 1
#> seq47 0 3 0
#> seq48 0 3 4
#> seq49 1 0 4
#> seq50 1 4 4
#> seq51 1 1 1
#> seq52 1 3 0
#> seq53 3 0 2
#> seq54 1 1 2
#> seq55 4 3 4Third dimension, representing the variables
(e.g. p1):
array3d[,,1]
#> time1 time2 time3
#> seq1 3 0 3
#> seq2 0 3 1
#> seq3 3 1 0
#> seq4 1 0 2
#> seq5 0 2 4
#> seq6 4 3 0
#> seq7 3 0 1
#> seq8 0 1 3
#> seq9 1 3 3
#> seq10 3 3 4
#> seq11 3 4 3
#> seq12 4 3 2
#> seq13 3 2 3
#> seq14 2 3 1
#> seq15 3 1 4
#> seq16 1 4 2
#> seq17 4 2 1
#> seq18 2 1 1
#> seq19 1 3 0
#> seq20 3 0 3
#> seq21 2 4 4
#> seq22 4 4 1
#> seq23 4 1 1
#> seq24 1 1 3
#> seq25 1 3 1
#> seq26 3 1 4
#> seq27 1 4 1
#> seq28 2 3 0
#> seq29 3 0 3
#> seq30 0 3 1
#> seq31 3 1 4
#> seq32 1 4 1
#> seq33 4 1 3
#> seq34 1 3 1
#> seq35 3 1 1
#> seq36 1 1 4
#> seq37 1 4 1
#> seq38 0 3 0
#> seq39 4 2 1
#> seq40 2 1 4
#> seq41 0 2 2
#> seq42 2 2 4
#> seq43 2 4 0
#> seq44 4 0 1
#> seq45 0 1 0
#> seq46 1 0 3
#> seq47 0 3 0
#> seq48 0 1 1
#> seq49 1 1 1
#> seq50 1 1 1
#> seq51 1 1 3
#> seq52 1 3 1
#> seq53 3 1 0
#> seq54 1 0 0
#> seq55 4 0 0