roc.analysis function

Analysis of different indicators to find the optimum value of the window parameter

Function roc.analysis perform a ROC analysis

roc.analysis(
  i.data,
  i.param.values = seq(1, 5, 0.1),
  i.min.seasons = 6,
  i.graph = F,
  i.graph.file = F,
  i.graph.file.name = "",
  i.graph.title = "",
  i.graph.subtitle = "",
  i.output = ".",
  i.mem.info = T,
  ...
)

Arguments

• i.data: Data frame of input data.
• i.param.values: range of i.param values to test.
• i.min.seasons: minimum number of seasons to perform the analysis, default=6.
• i.graph: create a graph with the outputs (T/F).
• i.graph.file: write the graph to a file.
• i.graph.file.name: name of the output file.
• i.graph.title: title of the graph.
• i.graph.subtitle: subtitle of the graph.
• i.output: output directory.
• i.mem.info: include information about the package in the graph.
• ...: other paramaters to be used by memgoodness function.

Returns

roc.analysis returns a list. An object of class mem is a list containing at least the following components:

• optimum optimum value.
• results Detailed results of each iteration.

Details

Optimize is an iterative process that calculates goodness indicators using different window parameters for the fixed criterium and compares all estimators in order to find the optimum window parameter.

The output shows the different window parameters and their respective indicators to decide which one is better for your data.

Examples

# Castilla y Leon Influenza Rates data
data(flucyl)
# ROC analysis
epi.roc <- roc.analysis(flucyl,
  i.param.values = seq(2.6, 2.8, 0.1),
  i.detection.values = seq(2.6, 2.8, 0.1)
)
epi.roc$results

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

Author(s)

Jose E. Lozano lozalojo@gmail.com