eode_gridSearch {ecode}

Grid Search For Optimal Parameters

Description

Find optimal parameters in the ODE system using grid search method.

Usage

eode_gridSearch(x, pdat, space, step = 0.01)

Arguments

x

the ODE system under consideration. An object of "eode" class.

pdat

observed population dynamics. An object of "pdata" class.

space

space

step

interval of time for running simulations. Parameter of the function "eode_proj()".

Value

a data frame showing attempted parameters along with the corresponding values of loss function.

Examples

dX_Cdt <- function(X_C, Y_C, X_A, Y_A, nu = 0.15, beta = 0.1, mu = 0.15, g = 0.04) {
  nu * (X_A + Y_A) - beta * X_C * (Y_C + Y_A) - (mu + g) * X_C
}

dY_Cdt <- function(X_C, Y_C, Y_A, beta = 0.1, mu = 0.15, g = 0.04, rho = 0.2) {
  beta * X_C * (Y_C + Y_A) - (mu + g + rho) * Y_C
}

dX_Adt <- function(X_C, Y_C, X_A, Y_A, beta = 0.1, g = 0.04) {
  g * X_C - beta * X_A * (Y_C + Y_A)
}

dY_Adt <- function(X_A, Y_C, Y_A, beta = 0.1, g = 0.04, rho = 0.2) {
  beta * X_A * (Y_C + Y_A) + g * Y_C - rho * Y_A
}

x <- eode(
  dX_Cdt = dX_Cdt, dY_Cdt = dY_Cdt, dX_Adt = dX_Adt, dY_Adt = dY_Adt,
  constraint = c("X_C>=0", "Y_C>=0", "X_A>=0", "Y_A>=0")
)
training_data <- pdata(x,
  init = data.frame(
    X_A = c(9, 19, 29, 39),
    Y_A = c(1, 1, 1, 1),
    X_C = c(5, 5, 5, 5),
    Y_C = c(0, 0, 0, 0)
  ),
  t = c(3, 3, 3, 3),
  lambda = data.frame(incidence = c(0.4, 0.8, 0.9, 0.95)),
  formula = "incidence = (Y_A + Y_C)/(X_A + X_C + Y_A + Y_C)"
)
res <- eode_gridSearch(x, pdat = training_data, space = list(beta = seq(0.05, 0.15, 0.01)))
res