convergEU (version 0.7.3.2)

upDo_CoDi: Upward-downward convergence declaration

Description

Convergence and divergence may be strict or weak, upward or downward. The interpretation depends on the type of indicator, that is "highBest" or "lowBest".

Usage

upDo_CoDi(
  myTB,
  timeName = "time",
  indiType = "highBest",
  time_0 = NA,
  time_t = NA,
  heter_fun = "pop_var"
)

Value

list of declarations.

Arguments

myTB

time by member states dataset. No other variables can be in the dataset.

timeName

name of the variable that contains time.

indiType

a string, "lowBest" or "highBest".

time_0

reference time.

time_t

target time strictly larger than time_0.

heter_fun

function to summarize dispersion, like var(), sd(); user-developed function are allowed; pop_var is the variance with denominator n.

Details

Note that if the argument heter_fun is set to sd or var, then those statistics use a denominator which is n-1, i.e. the number of observations decreased by 1. This is not typically what one wants here, thus the function pop_var may be used instead, because it adopts n as denominator. It is also possible to map a summary of dispersion with a monotonic function, like sqrt (see examples).

All the Member states contributing to the mean must be columns of the dataset given as input.

References

https://www.eurofound.europa.eu/system/files/2022-04/introduction-to-the-convergeu-package-0.6.4-tutorial-v2-apr2022.pdf

Examples

# using the standard deviation
upDo_CoDi(emp_20_64_MS,
         timeName = "time",
         indiType = "highBest",
         time_0 = 2010,
         time_t = 2015,
         heter_fun = "var" # watchout the denominator here is n-1
         )


# using the standard pop_var function
upDo_CoDi(emp_20_64_MS,
         timeName = "time",
         indiType = "highBest",
         time_0 = 2010,
         time_t = 2015,
         heter_fun = "pop_var" # the denominator here is n
         )



# using personalized summary of dispersion
diffQQmu <-  function(vettore){
   (quantile(vettore,0.75)-quantile(vettore,0.25))/mean(vettore)
   }

upDo_CoDi(emp_20_64_MS,
         timeName = "time",
         indiType = "highBest",
         time_0 = 2010,
         time_t = 2015,
         heter_fun = "diffQQmu"
         )