NNS.meboot: NNS meboot
In NNS: Nonlinear Nonparametric Statistics

NNS.meboot

R Documentation

NNS meboot

Description

Adapted maximum entropy bootstrap routine from meboot https://cran.r-project.org/package=meboot.

Usage

NNS.meboot(
  x,
  reps = 999,
  rho = NULL,
  type = "spearman",
  drift = TRUE,
  trim = 0.1,
  xmin = NULL,
  xmax = NULL,
  reachbnd = TRUE,
  expand.sd = TRUE,
  force.clt = TRUE,
  scl.adjustment = FALSE,
  sym = FALSE,
  elaps = FALSE,
  digits = 6,
  colsubj,
  coldata,
  coltimes,
  ...
)

Arguments

`x`	vector of data.
`reps`	numeric; number of replicates to generate.
`rho`	numeric [-1,1] (vectorized); A `rho` must be provided, otherwise a blank list will be returned.
`type`	options("spearman", "pearson", "NNScor", "NNSdep"); `type = "spearman"`(default) dependence metric desired.
`drift`	logical; `TRUE` default preserves the drift of the original series.
`trim`	numeric [0,1]; The mean trimming proportion, defaults to `trim=0.1`.
`xmin`	numeric; the lower limit for the left tail.
`xmax`	numeric; the upper limit for the right tail.
`reachbnd`	logical; If `TRUE` potentially reached bounds (xmin = smallest value - trimmed mean and xmax = largest value + trimmed mean) are given when the random draw happens to be equal to 0 and 1, respectively.
`expand.sd`	logical; If `TRUE` the standard deviation in the ensemble is expanded. See `expand.sd` in meboot::meboot.
`force.clt`	logical; If `TRUE` the ensemble is forced to satisfy the central limit theorem. See `force.clt` in meboot::meboot.
`scl.adjustment`	logical; If `TRUE` scale adjustment is performed to ensure that the population variance of the transformed series equals the variance of the data.
`sym`	logical; If `TRUE` an adjustment is performed to ensure that the ME density is symmetric.
`elaps`	logical; If `TRUE` elapsed time during computations is displayed.
`digits`	integer; 6 (default) number of digits to round output to.
`colsubj`	numeric; the column in `x` that contains the individual index. It is ignored if the input data `x` is not a `pdata.frame` object.
`coldata`	numeric; the column in `x` that contains the data of the variable to create the ensemble. It is ignored if the input data `x` is not a `pdata.frame` object.
`coltimes`	numeric; an optional argument indicating the column that contains the times at which the observations for each individual are observed. It is ignored if the input data `x` is not a `pdata.frame` object.
`...`	possible argument `fiv` to be passed to `expand.sd`.

Value

Returns the following row names in a matrix:

x original data provided as input.
replicates maximum entropy bootstrap replicates.
ensemble average observation over all replicates.
xx sorted order stats (xx[1] is minimum value).
z class intervals limits.
dv deviations of consecutive data values.
dvtrim trimmed mean of dv.
xmin data minimum for ensemble=xx[1]-dvtrim.
xmax data x maximum for ensemble=xx[n]+dvtrim.
desintxb desired interval means.
ordxx ordered x values.
kappa scale adjustment to the variance of ME density.
elaps elapsed time.

References

Vinod, H.D. and Viole, F. (2020) Arbitrary Spearman's Rank Correlations in Maximum Entropy Bootstrap and Improved Monte Carlo Simulations. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2139/ssrn.3621614")}
Vinod, H.D. (2013), Maximum Entropy Bootstrap Algorithm Enhancements. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2139/ssrn.2285041")}
Vinod, H.D. (2006), Maximum Entropy Ensembles for Time Series Inference in Economics, Journal of Asian Economics, 17(6), pp. 955-978.
Vinod, H.D. (2004), Ranking mutual funds using unconventional utility theory and stochastic dominance, Journal of Empirical Finance, 11(3), pp. 353-377.

Examples

## Not run: 
# To generate an orthogonal rank correlated time-series to AirPassengers
boots <- NNS.meboot(AirPassengers, reps=100, rho = 0, xmin = 0)

# Verify correlation of replicates ensemble to original
cor(boots["ensemble",], AirPassengers, method = "spearman")

# Plot all replicates
matplot(boots["replicates",] , type = 'l')

# Plot ensemble
lines(boots["ensemble",], lwd = 3)

## End(Not run)

NNS documentation built on Oct. 14, 2024, 5:09 p.m.