SPOT (version 2.4.2)

tuneRegionModel: tuneRegionModel

Description

Perform a spot run on funMarkovChain with region data. Results can be postprocessed with the function parseTunedRegionModel to extract model and parameter information.

Usage

tuneRegionModel(
  regionData,
  pops = NULL,
  lower = NULL,
  upper = NULL,
  control = list()
)

Arguments

regionData

is a data.frame with observations of 3 variables:

data

Date, format: "2020-01-22" "2020-01-23" "2020-01-24" "2020-01-25" ...

confirmed

num 0 0 0 0 0 0 0 0 0 0 ..

fatalities

fatalities: num 0 0 0 0 0 0 0 0 0 0 ...

and attributes - attr(*, "regionName")= chr "Afghanistan/" - attr(*, "regionPopulation")= int 38041754

pops

evaluated populations

lower

lower bounds for spot optimization, @seealso Link{spot}

upper

upper bounds for spot optimization, @seealso Link{spot}

control

spot control list, see controlSpot

Value

This function returns a list with:

regionName

e.g., "Afghanistan/": List of 7

xbest

Parameters of the best found solution (matrix).

ybest

Objective function value of the best found solution (matrix).

x

Archive of all evaluation parameters (matrix).

y

Archive of the respective objective function values (matrix).

count

Number of performed objective function evaluations.

msg

Message specifying the reason of termination.

modelFit

The fit of the last build model, i.e., an object returned by the last call to the function specified by control$model.

Details

Note: the default number of function evaluations is very low.

Examples

# NOT RUN {
 
# require(SimInf)
# data <- preprocessInputData(regionTrain, regionPopulation)
# a <- c(0.01,  0.001, 0.001,   0.001)
# b <- c(0.1,  0.01, 0.01,   0.01)
# lapply(data[1], tuneRegionModel, pops=NULL, lower = a, upper = b,
# control=list(funEvals=6, 
# designControl=list(size=5), model = buildLM))

# }