trajectory {SimInf} | R Documentation |
Extract the number of individuals in each compartment in every
node after generating a single stochastic trajectory with
run
.
trajectory(model, compartments = NULL, node = NULL, as.is = FALSE)
model |
the |
compartments |
specify the names of the compartments to
extract data from. The compartments can be specified as a
character vector e.g. |
node |
indices specifying the subset of nodes to include when
extracting data. Default ( |
as.is |
the default ( |
A data.frame
if as.is = FALSE
, else a
matrix.
Description of the layout of the internal matrix (U
)
that is returned if as.is = TRUE
. 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.
Description of the layout of the matrix that is returned if
as.is = TRUE
. 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_ndim(ldata)[1]
\times
length(tspan)
.
## 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, threads = 1) ## Extract the number of individuals in each compartment at the ## time-points in 'tspan'. trajectory(result) ## Extract the number of recovered individuals in the first node ## at the time-points in 'tspan'. trajectory(result, compartments = "R", node = 1) ## Extract the number of recovered individuals in the first and ## third node at the time-points in 'tspan'. trajectory(result, compartments = "R", node = c(1, 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, threads = 1) ## Extract the continuous state variable 'phi' which represents ## the environmental infectious pressure. trajectory(result, "phi")