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param = c(lower,upper,length)
,
create.grid
creates a grid of initial values for algo.hhh.grid
.
The resulting matrix contains all combinations of the supplied parameters
which each are a sequence of length length
from lower
to
upper
.
Note that the autoregressive parameters $\lambda, \phi$ and the
overdispersion parameter $\psi$ must be positive.
Only one sequence of initial values is considered for the autregressive,
endemic and overdispersion parameters to create the grid,
e.g. initial values are the same
for each one of the seasonal and trend parameters.create.grid(disProgObj, control, params = list(epidemic = c(0.1, 0.9, 5),
endemic=c(-0.5,0.5,3), negbin = c(0.3, 12, 10)))
disProg
param=c(lower,upper,length)
epidemic
autoregressive parameters$\lambda$and$\phi$.endemic
trend and seasonal parameters$\beta, \gamma_j$.negbin
overdispersion gridSize
starting values as rowsalgo.hhh.grid
# simulate data
set.seed(123)
disProgObj <- simHHH(control = list(coefs = list(alpha =-0.5, gamma = 0.4,
delta = 0.6)),length=300)$data
# consider the model specified in a control object for algo.hhh.grid
cntrl1 <- list(lambda=TRUE, neighbours=TRUE,
linear=TRUE, nseason=1)
cntrl2 <- list(lambda=TRUE, negbin="single")
# create a grid of initial values for respective parameters
grid1 <- create.grid(disProgObj, cntrl1,
params = list(epidemic=c(0.1,0.9,3),
endemic=c(-1,1,3)))
grid2 <- create.grid(disProgObj, cntrl2,
params = list(epidemic=c(0.1,0.9,5),
negbin=c(0.3,12,10)))
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