full.series.graph: Creates the historical series graph of the datasets

Description Usage Arguments Details Value Author(s) References Examples

View source: R/full.series.graph.R

Description

Function full.series.graph creates a graph with the whole dataset.

Usage

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full.series.graph(
  i.data,
  i.range.x = NA,
  i.range.y = NA,
  i.output = ".",
  i.graph.title = "",
  i.graph.subtitle = "",
  i.graph.file = T,
  i.graph.file.name = "",
  i.plot.timing = F,
  i.plot.intensity = F,
  i.alternative.thresholds = NA,
  i.color.pattern = c("#C0C0C0", "#606060", "#000000", "#808080", "#000000", "#001933",
    "#00C000", "#800080", "#FFB401", "#8c6bb1", "#88419d", "#810f7c", "#4d004b"),
  i.mem.info = T,
  ...
)

Arguments

i.data

Historical data series.

i.range.x

Range x (surveillance weeks) of graph.

i.range.y

Range y of graph.

i.output

Directory where graph is saved.

i.graph.title

Title of the graph.

i.graph.subtitle

Subtitle of the graph.

i.graph.file

Graph to a file.

i.graph.file.name

Name of the graph.

i.plot.timing

Plot the timing of epidemics.

i.plot.intensity

Plot the intensity levels.

i.alternative.thresholds

Use alternative thresholds, instead of the ones modelled by the input data (epidemic + 3 intensity thresholds)

i.color.pattern

colors to use in the graph.

i.mem.info

include information about the package in the graph.

...

other parameters passed to memmodel.

Details

Input data must be a data.frame with each column a surveillance season and each row a week.

The resulting graph is a time series-like plot showing all the columns in the original dataset one after another.

Color codes:

  1. Axis.

  2. Tickmarks.

  3. Axis labels.

  4. Series line.

  5. Series dots (default).

  6. Title and subtitle.

  7. Series dots (pre-epidemic).

  8. Series dots (epidemic).

  9. Series dots (post-epidemic).

  10. Epidemic threshold.

  11. Medium threshold.

  12. High threshold.

  13. Very high threshold.

Value

full.series.graph writes a tiff graph of the full series of the dataset.

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)
# Data of the last season
# uncomment to execute
# full.series.graph(flucyl)

lozalojo/mem documentation built on April 1, 2021, 1:37 a.m.