vv: value varier function

In decisionSupport: Quantitative Support of Decision Making under Uncertainty

value varier function

Description

Many variables vary over time and it may not be desirable to ignore this variation in time series analyses. This function produces time series that contain variation from a specified mean and a desired coefficient of variation. A trend can be added to this time series

Usage

vv(
  var_mean,
  var_CV,
  n,
  distribution = "normal",
  absolute_trend = NA,
  relative_trend = NA,
  lower_limit = NA,
  upper_limit = NA
)

Arguments

var_mean	mean of the variable to be varied
var_CV	desired coefficient of variation (in percent)
n	integer; number of values to produce
distribution	probability distribution for the introducing variation. Currently only implemented for "normal"
absolute_trend	absolute increment in the var_mean in each time step. Defaults to NA, which means no such absolute value trend is present. If both absolute and relative trends are specified, only original means are used
relative_trend	relative trend in the var_mean in each time step (in percent). Defaults to NA, which means no such relative value trend is present. If both absolute and relative trends are specified, only original means are used
lower_limit	lowest possible value for elements of the resulting vector
upper_limit	upper possible value for elements of the resulting vector

Details

Note that only one type of trend can be specified. If neither of the trend parameters are NA, the function uses only the original means

Value

vector of n numeric values, representing a variable time series, which initially has the mean var_mean, and then increases according to the specified trends. Variation is determined by the given coefficient of variation var_CV

Author(s)

Eike Luedeling

Examples

valvar<-vv(100,10,30)
plot(valvar)

valvar<-vv(100,10,30,absolute_trend=5)
plot(valvar)

valvar<-vv(100,10,30,relative_trend=5)
plot(valvar)

decisionSupport documentation built on Oct. 6, 2023, 1:06 a.m.