Extract the number of individuals in each compartment in every
node after generating a single stochastic trajectory with
run.
Usage
# S4 method for class 'SimInf_model'
trajectory(model, compartments, index, format = c("data.frame", "matrix"))Arguments
- model
the
SimInf_modelobject to extract the result from.- compartments
specify the names of the compartments to extract data from. The compartments can be specified as a character vector e.g.
compartments = c('S', 'I', 'R'), or as a formula e.g.compartments = ~S+I+R(see ‘Examples’). Default (compartments=NULL) is to extract the number of individuals in each compartment i.e. the data from all discrete state compartments in the model. In models that also have continuous state variables e.g. theSISemodel, they are also included.- index
indices specifying the subset of nodes to include when extracting data. Default (
index = NULL) is to extract data from all nodes.- format
the default (
format = "data.frame") is to generate adata.framewith one row per node and time-step with the number of individuals in each compartment. Usingformat = "matrix"returns the result as a matrix, which is the internal format (see ‘Details’).
Internal format of the discrete state variables
Description of the layout of the internal matrix (U)
that is returned if format = "matrix". U[, j]
contains the number of individuals in each compartment at
tspan[j]. U[1:Nc, j] contains the number of
individuals in node 1 at tspan[j]. U[(Nc + 1):(2
* Nc), j] contains the number of individuals in node 2 at
tspan[j] etc, where Nc is the number of
compartments in the model. The dimension of the matrix is
\(N_n N_c \times\) length(tspan) where \(N_n\) is
the number of nodes.
Internal format of the continuous state variables
Description of the layout of the matrix that is returned if
format = "matrix". The result matrix for the
real-valued continuous state. V[, j] contains the
real-valued state of the system at tspan[j]. The
dimension of the matrix is \(N_n\)dim(ldata)[1]
\(\times\) length(tspan).
Examples
## 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 to generate a single stochastic trajectory.
result <- run(model)
## Extract the number of individuals in each compartment at the
## time-points in 'tspan'.
trajectory(result)
#> node time S I R
#> 1 1 1 100 1 0
#> 2 2 1 100 3 0
#> 3 3 1 102 3 0
#> 4 4 1 102 5 0
#> 5 5 1 102 7 0
#> 6 6 1 103 8 0
#> 7 1 2 100 1 0
#> 8 2 2 100 3 0
#> 9 3 2 102 3 0
#> 10 4 2 101 5 1
#> 11 5 2 100 8 1
#> 12 6 2 103 8 0
#> 13 1 3 100 1 0
#> 14 2 3 99 4 0
#> 15 3 3 102 2 1
#> 16 4 3 99 7 1
#> 17 5 3 100 7 2
#> 18 6 3 101 10 0
#> 19 1 4 100 1 0
#> 20 2 4 98 5 0
#> 21 3 4 102 1 2
#> 22 4 4 99 5 3
#> 23 5 4 100 5 4
#> 24 6 4 100 9 2
#> 25 1 5 100 1 0
#> 26 2 5 98 4 1
#> 27 3 5 102 0 3
#> 28 4 5 99 5 3
#> 29 5 5 99 6 4
#> 30 6 5 100 8 3
#> 31 1 6 100 1 0
#> 32 2 6 97 5 1
#> 33 3 6 102 0 3
#> 34 4 6 99 5 3
#> 35 5 6 99 6 4
#> 36 6 6 99 9 3
#> 37 1 7 100 1 0
#> 38 2 7 97 5 1
#> 39 3 7 102 0 3
#> 40 4 7 95 8 4
#> 41 5 7 99 6 4
#> 42 6 7 96 12 3
#> 43 1 8 100 1 0
#> 44 2 8 96 5 2
#> 45 3 8 102 0 3
#> 46 4 8 95 8 4
#> 47 5 8 98 7 4
#> 48 6 8 95 11 5
#> 49 1 9 100 1 0
#> 50 2 9 95 6 2
#> 51 3 9 102 0 3
#> 52 4 9 94 7 6
#> 53 5 9 97 8 4
#> 54 6 9 91 15 5
#> 55 1 10 100 1 0
#> 56 2 10 94 7 2
#> 57 3 10 102 0 3
#> 58 4 10 94 6 7
#> 59 5 10 95 10 4
#> 60 6 10 88 18 5
## Extract the number of recovered individuals in the first node
## at the time-points in 'tspan'.
trajectory(result, compartments = "R", index = 1)
#> node time R
#> 1 1 1 0
#> 2 1 2 0
#> 3 1 3 0
#> 4 1 4 0
#> 5 1 5 0
#> 6 1 6 0
#> 7 1 7 0
#> 8 1 8 0
#> 9 1 9 0
#> 10 1 10 0
## Extract the number of recovered individuals in the first and
## third node at the time-points in 'tspan'.
trajectory(result, compartments = "R", index = c(1, 3))
#> node time R
#> 1 1 1 0
#> 2 3 1 0
#> 3 1 2 0
#> 4 3 2 0
#> 5 1 3 0
#> 6 3 3 1
#> 7 1 4 0
#> 8 3 4 2
#> 9 1 5 0
#> 10 3 5 3
#> 11 1 6 0
#> 12 3 6 3
#> 13 1 7 0
#> 14 3 7 3
#> 15 1 8 0
#> 16 3 8 3
#> 17 1 9 0
#> 18 3 9 3
#> 19 1 10 0
#> 20 3 10 3
## Create an 'SISe' model with 6 nodes and initialize
## it to run over 10 days.
u0 <- data.frame(S = 100:105, I = 1:6)
model <- SISe(u0 = u0, tspan = 1:10, phi = rep(0, 6),
upsilon = 0.02, gamma = 0.1, alpha = 1, epsilon = 1.1e-5,
beta_t1 = 0.15, beta_t2 = 0.15, beta_t3 = 0.15, beta_t4 = 0.15,
end_t1 = 91, end_t2 = 182, end_t3 = 273, end_t4 = 365)
## Run the model
result <- run(model)
## Extract the continuous state variable 'phi' which represents
## the environmental infectious pressure.
trajectory(result, "phi")
#> node time phi
#> 1 1 1 0.009911990
#> 2 2 1 0.009719738
#> 3 3 1 0.028582429
#> 4 4 1 0.028048383
#> 5 5 1 0.036708248
#> 6 6 1 0.054065054
#> 7 1 2 0.008436192
#> 8 2 2 0.017981515
#> 9 3 2 0.052877493
#> 10 4 2 0.051889509
#> 11 5 2 0.067910258
#> 12 6 2 0.100020350
#> 13 1 3 0.007181763
#> 14 2 3 0.025004026
#> 15 3 3 0.064004488
#> 16 4 3 0.062808671
#> 17 5 3 0.094431967
#> 18 6 3 0.130073343
#> 19 1 4 0.006115498
#> 20 2 4 0.030973160
#> 21 3 4 0.073462434
#> 22 4 4 0.072089959
#> 23 5 4 0.116975420
#> 24 6 4 0.155618386
#> 25 1 5 0.005209174
#> 26 2 5 0.036046924
#> 27 3 5 0.081501688
#> 28 4 5 0.089324849
#> 29 5 5 0.126963043
#> 30 6 5 0.177331673
#> 31 1 6 0.004438798
#> 32 2 6 0.040359623
#> 33 3 6 0.088335054
#> 34 4 6 0.094628710
#> 35 5 6 0.144626834
#> 36 6 6 0.195787967
#> 37 1 7 0.003783978
#> 38 2 7 0.044025417
#> 39 3 7 0.094143415
#> 40 4 7 0.108482787
#> 41 5 7 0.159641057
#> 42 6 7 0.220484826
#> 43 1 8 0.003227381
#> 44 2 8 0.047141343
#> 45 3 8 0.099080522
#> 46 4 8 0.120258752
#> 47 5 8 0.172403146
#> 48 6 8 0.241477156
#> 49 1 9 0.002754274
#> 50 2 9 0.049789879
#> 51 3 9 0.103277062
#> 52 4 9 0.130268322
#> 53 5 9 0.174076610
#> 54 6 9 0.277338655
#> 55 1 10 0.002352133
#> 56 2 10 0.052041135
#> 57 3 10 0.106844122
#> 58 4 10 0.138776457
#> 59 5 10 0.184673366
#> 60 6 10 0.298811920