mem (version 2.5)

memevolution: evolution of estimators

Description

Function memevolution analyzes the evolution of mem estimators

Usage

memevolution(i.data, i.evolution.seasons = 10,
  i.evolution.method = "sequential", ...)

Arguments

i.data

Data frame of input data.

i.evolution.seasons

Maximum number of seasons to use.

i.evolution.method

method to calculate evolution.

...

other parameters passed to memmodel.

Value

memevolution returns a list. A list containing at least the following components:

evolution.data

data for each value analysed.

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)
# evolution of estimators
evolution<-memevolution(flucyl)
evolution$evolution.data