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

 NNS.CDF R Documentation

## NNS CDF

### Description

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

### Usage

```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

```## Not run:
set.seed(123)
x <- rnorm(100)
NNS.CDF(x)

## 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 Nov. 4, 2022, 1:06 a.m.