NNS.CDF: NNS CDF In NNS: Nonlinear Nonparametric Statistics

Description

This function generates an empirical CDF using partial moment ratios LPM.ratio, and resulting survival, hazard and cumulative hazard functions.

Usage

 `1` ```NNS.CDF(variable, degree = 0, target = NULL, type = "CDF", plot = TRUE) ```

Arguments

 `variable` a numeric vector or data.frame of 2 variables for joint CDF. `degree` integer; `(degree = 0)` (default) is frequency, `(degree = 1)` is area. `target` numeric; `NULL` (default) Must lie within support of each variable. `type` options("CDF", "survival", "hazard", "cumulative hazard"); `"CDF"` (default) Selects type of function to return for bi-variate analysis. Multivariate analysis is restricted to `"CDF"`. `plot` logical; plots CDF.

Value

Returns:

• `"Function"` a data.table containing the observations and resulting CDF of the variable.

• `"target.value"` value from the `target` argument.

Author(s)

Fred Viole, OVVO Financial Systems

References

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp

Viole, F. (2017) "Continuous CDFs and ANOVA with NNS" https://www.ssrn.com/abstract=3007373

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```set.seed(123) x <- rnorm(100) NNS.CDF(x) ## Not run: ## Empirical CDF (degree = 0) NNS.CDF(x) ## Continuous CDF (degree = 1) NNS.CDF(x, 1) ## Joint CDF x <- rnorm(5000) ; y <- rnorm(5000) A <- cbind(x,y) NNS.CDF(A, 0) ## Joint CDF with target NNS.CDF(A, 0, target = c(0,0)) ## End(Not run) ```

NNS documentation built on June 26, 2021, 1:07 a.m.