SimInfDescribe your model in a logical way in R. mparse creates a
SimInf_model object with your model
definition that is ready to run.
mparse(
transitions = NULL,
compartments = NULL,
ldata = NULL,
gdata = NULL,
u0 = NULL,
v0 = NULL,
tspan = NULL,
events = NULL,
E = NULL,
N = NULL,
pts_fun = NULL,
use_enum = FALSE
)a SimInf_model object
character vector containing transitions on the
form "X -> ... -> Y". The left (right) side is the
initial (final) state and the propensity is written in between
the ->-signs. The special symbol @ is reserved
for the empty set. For example, transitions =
c("S -> beta*S*I/(S+I+R) -> I", "I -> gamma*I -> R")
expresses the SIR model. It is also possible to define
variables which can then be used in calculations of
propensities or in calculations of other variables. A variable
is defined by the operator <-. Using a variable for the
size of the population, the SIR model can instead be written
transitions = c("S -> beta*S*I/N -> I",
"I -> gamma*I -> R", "N <- S+I+R"). By default, the type of a
variable is defined as a double in the generated C code, but
it is possible to also define it as an integer by writing
(int) before the variable name. For example, for the
SIR model, the population size can be defined as
"(int)N <- S+I+R". It is also possible to explicitly
use (double) in front of the variable name, but it is not
needed because it is the default. Note that the order of
propensities and variables does not matter.
contains the names of the involved
compartments, for example, compartments = c("S", "I",
"R").
optional data for the nodes. Can be specified as a
data.frame with one row per node, as a numeric matrix
where column ldata[, j] contains the local data vector
for the node j, or as a as a named vector when the
model only contains one node. If ldata is specified as
a data.frame, each column is one parameter. If
v0 is specified as a matrix, it must have row names to
identify the parameters in the transitions. If v0 is
specified as a named vector, the names identify the
parameters. The local data vector is passed as an argument to
the transition rate functions and the post time step function.
optional data that are common to all nodes in the model. Can be specified either as a named numeric vector or as as a one-row data.frame. The names are used to identify the parameters in the transitions. The global data vector is passed as an argument to the transition rate functions and the post time step function.
A data.frame with the initial state in each node,
i.e., the number of individuals in each compartment in each
node when the simulation starts (see ‘Details’). The
parameter u0 can also be an object that can be coerced
to a data.frame, e.g., a named numeric vector will be
coerced to a one row data.frame.
optional data with the initial continuous state in each
node. v0 can be specified as a data.frame with
one row per node, as a numeric matrix where column v0[,
j] contains the initial state vector for the node j,
or as a named vector when the model only contains one node. If
v0 is specified as a data.frame, each column is
one parameter. If v0 is specified as a matrix, the row
names identify the parameters. If v0 is specified as a
named vector, the names identify the parameters. The
‘v’ vector is passed as an argument to the transition
rate functions and the post time step function. The continuous
state can be updated in the post time step function.
A vector (length >= 1) of increasing time points
where the state of each node is to be returned. Can be either
an integer or a Date vector. A Date
vector is coerced to a numeric vector as days, where
tspan[1] becomes the day of the year of the first year
of tspan. The dates are added as names to the numeric
vector.
A data.frame with the scheduled
events. Default is NULL i.e. no scheduled events in the
model.
matrix to handle scheduled events, see
SimInf_events. Default is NULL
i.e. no scheduled events in the model.
matrix to handle scheduled events, see
SimInf_events. Default is NULL
i.e. no scheduled events in the model.
optional character vector with C code for the post time step function. The C code should contain only the body of the function i.e. the code between the opening and closing curly brackets.
generate enumeration constants for the indices to
each parameter in the 'u', 'v', 'ldata', and 'gdata' vectors
in the generated C code. The name of each enumeration constant
will be transformed to the upper-case name of the
corresponding parameter, for example, a parameter 'beta' will
become 'BETA'. Using enumeration constants can make it easier
to modify the C code afterwards, or when writing C code for
the pts_fun parameter. Default is FALSE, i.e.,
the parameters are specified by using integer indices for the
parameters.
if (FALSE) {
## Use the model parser to create a 'SimInf_model' object that
## expresses the SIR model, where 'beta' is the transmission rate
## and 'gamma' is the recovery rate.
model <- mparse(transitions = c("S -> beta*S*I/N -> I",
"I -> gamma*I -> R",
"N <- S+I+R"),
compartments = c("S", "I", "R"),
gdata = c(beta = 0.16, gamma = 0.077),
u0 = data.frame(S = 100, I = 1, R = 0),
tspan = 1:100)
## Run and plot the result
set.seed(22)
result <- run(model)
plot(result)
}
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