mem (version 2.5)

optimum.by.inspection: Inspection calcultation of the optimum

Description

Function optimum.by.inspection perform an analysis of mem parameters to find the one that fits better a panel of experts inspection criterium.

Usage

optimum.by.inspection(i.data, i.detection.values = seq(1.5, 4.5, 0.1))

Arguments

i.data

Data frame of input data.

i.detection.values

values to use in the i.param value of memtiming.

Value

optimum.by.inspection returns a list. An object of class mem is a list containing at least the following components:

optimum

optimum value.

optimum.data

Data related to the optimum value.

summary.data

Data for all values tested.

inspection.data

Detailed results of each iteration.

Details

To be written

References

Vega Alonso, Tomas, Jose E Lozano Alonso, Raul Ortiz de Lejarazu, and Marisol Gutierrez Perez. 2004. Modelling Influenza Epidemic: Can We Detect the Beginning and Predict the Intensity and Duration? International Congress Series, Options for the Control of Influenza V. Proceedings of the International Conference on Options for the Control of Influenza V, 1263 (June): 281-83. doi:10.1016/j.ics.2004.02.121. Vega, Tomas, Jose Eugenio Lozano, Tamara Meerhoff, Rene Snacken, Joshua Mott, Raul Ortiz de Lejarazu, and Baltazar Nunes. 2013. Influenza Surveillance in Europe: Establishing Epidemic Thresholds by the Moving Epidemic Method. Influenza and Other Respiratory Viruses 7 (4): 546-58. doi:10.1111/j.1750-2659.2012.00422.x. Vega, Tomas, Jose E. Lozano, Tamara Meerhoff, Rene Snacken, Julien Beaute, Pernille Jorgensen, Raul Ortiz de Lejarazu, et al. 2015. Influenza Surveillance in Europe: Comparing Intensity Levels Calculated Using the Moving Epidemic Method. Influenza and Other Respiratory Viruses 9 (5): 234-46. doi:10.1111/irv.12330.

Examples

# Castilla y Leon Influenza Rates data
data(flucyl)
# Inspection. It runs interactively (uncomment to run)
#opt.ins<-optimum.by.inspection(flucyl,i.detection.values=seq(2.0,3.0,0.1))
#opt.ins$optimum.data