Set a template for where to record result during a simulation
Source:R/punchcard.R
punchcard-set.RdUsing a sparse result matrix can save a lot of memory if the model contains many nodes and time-points, but where only a few of the data points are of interest for post-processing.
Arguments
- model
The
modelto set a template for where to record result.- value
A
data.framethat specify the nodes, time-points and compartments to record the number of individuals attspan. UseNULLto reset the model to record the number of inidividuals in each compartment in every node at each time-point in tspan.
Details
Using a sparse result matrix can save a lot of memory if the model
contains many nodes and time-points, but where only a few of the
data points are of interest for post-processing. To use this
feature, a template has to be defined for which data points to
record. This is done using a data.frame that specifies the
time-points (column ‘time’) and nodes (column
‘node’) to record the state of the compartments, see
‘Examples’. The specified time-points, nodes and
compartments must exist in the model, or an error is raised. Note
that specifying a template only affects which data-points are
recorded for post-processing, it does not affect how the solver
simulates the trajectory.
Examples
## For reproducibility, call the set.seed() function and specify
## the number of threads to use. To use all available threads,
## remove the set_num_threads() call.
set.seed(123)
set_num_threads(1)
## Create an 'SIR' model with 6 nodes and initialize it to run over 10 days.
u0 <- data.frame(S = 100:105, I = 1:6, R = rep(0, 6))
model <- SIR(u0 = u0, tspan = 1:10, beta = 0.16, gamma = 0.077)
## Run the model.
result <- run(model)
## Display the trajectory with data for every node at each
## time-point in tspan.
trajectory(result)
#> node time S I R
#> 1 1 1 100 1 0
#> 2 2 1 101 2 0
#> 3 3 1 102 3 0
#> 4 4 1 102 5 0
#> 5 5 1 103 6 0
#> 6 6 1 105 6 0
#> 7 1 2 100 1 0
#> 8 2 2 101 2 0
#> 9 3 2 101 4 0
#> 10 4 2 101 5 1
#> 11 5 2 103 6 0
#> 12 6 2 105 6 0
#> 13 1 3 99 2 0
#> 14 2 3 101 2 0
#> 15 3 3 101 4 0
#> 16 4 3 99 6 2
#> 17 5 3 101 8 0
#> 18 6 3 103 7 1
#> 19 1 4 98 3 0
#> 20 2 4 101 2 0
#> 21 3 4 101 4 0
#> 22 4 4 98 6 3
#> 23 5 4 99 10 0
#> 24 6 4 101 8 2
#> 25 1 5 98 3 0
#> 26 2 5 101 2 0
#> 27 3 5 100 5 0
#> 28 4 5 97 6 4
#> 29 5 5 98 9 2
#> 30 6 5 101 6 4
#> 31 1 6 98 2 1
#> 32 2 6 101 2 0
#> 33 3 6 100 5 0
#> 34 4 6 97 5 5
#> 35 5 6 98 8 3
#> 36 6 6 100 7 4
#> 37 1 7 98 2 1
#> 38 2 7 98 5 0
#> 39 3 7 100 5 0
#> 40 4 7 92 10 5
#> 41 5 7 98 7 4
#> 42 6 7 99 8 4
#> 43 1 8 97 3 1
#> 44 2 8 98 5 0
#> 45 3 8 98 6 1
#> 46 4 8 92 8 7
#> 47 5 8 95 10 4
#> 48 6 8 99 8 4
#> 49 1 9 97 3 1
#> 50 2 9 97 6 0
#> 51 3 9 98 4 3
#> 52 4 9 91 9 7
#> 53 5 9 94 10 5
#> 54 6 9 99 7 5
#> 55 1 10 97 3 1
#> 56 2 10 96 6 1
#> 57 3 10 98 4 3
#> 58 4 10 89 11 7
#> 59 5 10 93 9 7
#> 60 6 10 98 8 5
## Assume we are only interested in nodes '2' and '4' at the
## time-points '3' and '5'
df <- data.frame(time = c(3, 5, 3, 5),
node = c(2, 2, 4, 4),
S = c(TRUE, TRUE, TRUE, TRUE),
I = c(TRUE, TRUE, TRUE, TRUE),
R = c(TRUE, TRUE, TRUE, TRUE))
punchcard(model) <- df
result <- run(model)
trajectory(result)
#> node time S I R
#> 1 2 3 100 3 0
#> 2 4 3 102 5 0
#> 3 2 5 100 3 0
#> 4 4 5 100 6 1
## We can also specify to record only some of the compartments in
## each time-step.
df <- data.frame(time = c(3, 5, 3, 5),
node = c(2, 2, 4, 4),
S = c(FALSE, TRUE, TRUE, TRUE),
I = c(TRUE, FALSE, TRUE, FALSE),
R = c(TRUE, FALSE, TRUE, TRUE))
punchcard(model) <- df
result <- run(model)
trajectory(result)
#> node time S I R
#> 1 2 3 NA 4 0
#> 2 4 3 99 7 1
#> 3 2 5 98 NA NA
#> 4 4 5 99 NA 3
## A shortcut to specify to record all of the compartments in
## each time-step is to only inlude node and time.
df <- data.frame(time = c(3, 5, 3, 5),
node = c(2, 2, 4, 4))
punchcard(model) <- df
result <- run(model)
trajectory(result)
#> node time S I R
#> 1 2 3 97 6 0
#> 2 4 3 103 4 0
#> 3 2 5 96 6 1
#> 4 4 5 101 6 0
## It is possible to use an empty 'data.frame' to specify
## that no data-points should be recorded for the trajectory.
punchcard(model) <- data.frame()
result <- run(model)
trajectory(result)
#> [1] node time S I R
#> <0 rows> (or 0-length row.names)
## Use 'NULL' to reset the model to record data for every node at
## each time-point in tspan.
punchcard(model) <- NULL
result <- run(model)
trajectory(result)
#> node time S I R
#> 1 1 1 100 1 0
#> 2 2 1 101 2 0
#> 3 3 1 99 6 0
#> 4 4 1 103 4 0
#> 5 5 1 104 5 0
#> 6 6 1 105 6 0
#> 7 1 2 100 1 0
#> 8 2 2 100 3 0
#> 9 3 2 98 7 0
#> 10 4 2 102 4 1
#> 11 5 2 104 5 0
#> 12 6 2 104 7 0
#> 13 1 3 100 1 0
#> 14 2 3 100 2 1
#> 15 3 3 98 7 0
#> 16 4 3 101 5 1
#> 17 5 3 101 8 0
#> 18 6 3 103 6 2
#> 19 1 4 100 1 0
#> 20 2 4 100 2 1
#> 21 3 4 96 9 0
#> 22 4 4 99 7 1
#> 23 5 4 101 6 2
#> 24 6 4 103 5 3
#> 25 1 5 100 1 0
#> 26 2 5 100 2 1
#> 27 3 5 96 7 2
#> 28 4 5 99 6 2
#> 29 5 5 96 11 2
#> 30 6 5 103 5 3
#> 31 1 6 100 1 0
#> 32 2 6 99 3 1
#> 33 3 6 95 8 2
#> 34 4 6 98 7 2
#> 35 5 6 95 11 3
#> 36 6 6 102 5 4
#> 37 1 7 100 1 0
#> 38 2 7 99 3 1
#> 39 3 7 94 9 2
#> 40 4 7 98 7 2
#> 41 5 7 94 12 3
#> 42 6 7 102 4 5
#> 43 1 8 100 1 0
#> 44 2 8 99 3 1
#> 45 3 8 94 8 3
#> 46 4 8 97 7 3
#> 47 5 8 93 13 3
#> 48 6 8 101 5 5
#> 49 1 9 100 1 0
#> 50 2 9 99 3 1
#> 51 3 9 90 11 4
#> 52 4 9 97 7 3
#> 53 5 9 91 13 5
#> 54 6 9 101 5 5
#> 55 1 10 100 1 0
#> 56 2 10 99 3 1
#> 57 3 10 87 14 4
#> 58 4 10 97 5 5
#> 59 5 10 89 14 6
#> 60 6 10 99 7 5