mem (version 2.5)

memgoodness: Goodness of fit of the mem

Description

Function memgoodness perform the goodness of fit of the mem

Usage

memgoodness(i.data, i.seasons = 10, i.type.threshold = 5,
  i.level.threshold = 0.95, i.tails.threshold = 1, i.type.intensity = 6,
  i.level.intensity = c(0.4, 0.9, 0.975), i.tails.intensity = 1,
  i.type.curve = 2, i.level.curve = 0.95, i.type.other = 2,
  i.level.other = 0.95, i.method = 2, i.param = 2.8, i.n.max = -1,
  i.type.boot = "norm", i.iter.boot = 10000,
  i.calculation.method = "default", i.goodness.method = "cross",
  i.detection.values = seq(2, 3, 0.1), i.weeks.above = 1, i.output = ".",
  i.graph = F, i.prefix = "", i.min.seasons = 6)

Arguments

i.data

Data frame of input data.

i.seasons

Maximum number of seasons to use.

i.type.threshold

Type of confidence interval to calculate the threshold.

i.level.threshold

Level of confidence interval to calculate the threshold.

i.tails.threshold

Tails for the confidence interval to calculate the threshold.

i.type.intensity

Type of confidence interval to calculate the intensity thresholds.

i.level.intensity

Levels of confidence interval to calculate the intensity thresholds.

i.tails.intensity

Tails for the confidence interval to calculate the threshold.

i.type.curve

Type of confidence interval to calculate the modelled curve.

i.level.curve

Level of confidence interval to calculate the modelled curve.

i.type.other

Type of confidence interval to calculate length, start and percentages.

i.level.other

Level of confidence interval to calculate length, start and percentages.

i.method

Method to calculate the optimal timing of the epidemic.

i.param

Parameter to calculate the optimal timing of the epidemic.

i.n.max

Number of pre-epidemic values used to calculate the threshold.

i.type.boot

Type of bootstrap technique.

i.iter.boot

Number of bootstrap iterations.

i.calculation.method

method of determining true/false positives and true/false negatives.

i.goodness.method

method to calculate goodness.

i.detection.values

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

i.weeks.above

number of weeks over the threshold to give the alert.

i.output

output directory for graphs.

i.graph

whether the graphs must be written or not.

i.prefix

prefix used for naming graphs.

i.min.seasons

minimum number of seasons to perform goodness, default=6.

Value

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

validity.data

data for each value analysed.

results

Total weeks, non-missing weeks, true positives, false positives true negatives, false negatives, sensitivity, specificity .

peaks

distribution of the levels of intensity of the peaks.

peaks.data

Peak value, week of the peak value, epidemic and intensity thresholds and intensity level of each season 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)
# Goodness of fit
epi.good<-memgoodness(flucyl,i.detection.values=seq(2.5,2.8,0.1))
epi.good$results
epi.good$peaks