memevolution: Evolution of estimators

Description Usage Arguments Details Value Author(s) References Examples

View source: R/memevolution.R

Description

Function memevolution analyzes the evolution of mem estimators

Usage

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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.

Details

Shows the evolution of some indicators across time. The indicators are: duration of the epidemic, start of the epidemic, epidemic percentage, pre-epidemic, post-epidemic and intensity thresholds.

Values per season are the estimates of the indicator for this specific season calculated according the memmodel options: cross or sequential (validation option), maximum number of seasons to use in calculations. In memmodel is it also possible to select the method used to calculate indicators and the level of confidence of the confidence intervals.

To clarify how it is calculated, the evolution.seasons showss for each x-axis point the seasons selected to calculate thresholds. Each row is a different season and in each column it is shown if that specific season has been used to calculate the indicators. If TRUE, it has been used, if NO, it hasn't. In the last row, called Next, the indicators are calculated for the upcoming next season.

Note that if you select sequential validation, first points in the graphs calculate thresholds using less data (there are less seasons before the current data than the max. seasons value), and this will affect the range of confidence intervals.

There are four indicators:

The confidence intervals for these indicators are calculated using the other CI and other CI. level parameters of memmodel.

Value

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

Author(s)

Jose E. Lozano lozalojo@gmail.com

References

Vega T, Lozano JE, Ortiz de Lejarazu R, Gutierrez Perez M. Modelling influenza epidemic - can we detect the beginning and predict the intensity and duration? Int Congr Ser. 2004 Jun;1263:281-3.

Vega T, Lozano JE, Meerhoff T, Snacken R, Mott J, Ortiz de Lejarazu R, et al. Influenza surveillance in Europe: establishing epidemic thresholds by the moving epidemic method. Influenza Other Respir Viruses. 2013 Jul;7(4):546-58. DOI:10.1111/j.1750-2659.2012.00422.x.

Vega T, Lozano JE, Meerhoff T, Snacken R, Beaute J, Jorgensen P, et al. Influenza surveillance in Europe: comparing intensity levels calculated using the moving epidemic method. Influenza Other Respir Viruses. 2015 Sep;9(5):234-46. DOI:10.1111/irv.12330.

Lozano JE. lozalojo/mem: Second release of the MEM R library. Zenodo [Internet]. [cited 2017 Feb 1]; Available from: https://zenodo.org/record/165983. DOI:10.5281/zenodo.165983

Examples

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# Castilla y Leon Influenza Rates data
data(flucyl)
# evolution of estimators
evolution <- memevolution(flucyl)
evolution$evolution.data

lozalojo/mem documentation built on Sept. 15, 2020, 1:12 a.m.