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

## Description

Analysis of variance (ANOVA) based on lower partial moment CDFs for multiple variables. Returns a degree of certainty the difference in sample means is zero, not a p-value.

## Usage

 ```1 2``` ```NNS.ANOVA(control, treatment, confidence.interval = 0.95, tails = "Both", pairwise = FALSE, plot = TRUE, binary = TRUE) ```

## Arguments

 `control` a numeric vector, matrix or data frame. `treatment` `NULL` (default) a numeric vector, matrix or data frame. `confidence.interval` numeric [0, 1]; The confidence interval surrounding the control mean when `(binary = TRUE)`. Defaults to `(confidence.interval = 0.95)`. `tails` options: ("Left", "Right", "Both"). `tails = "Both"`(Default) Selects the tail of the distribution to determine effect size. `pairwise` logical; `FALSE` (defualt) Returns pairwise certainty tests when set to `pairwise = TRUE`. `plot` logical; `TRUE` (default) Returns the boxplot of all variables along with grand mean identification. When `(binary = TRUE)`, returns the boxplot of both variables along with grand mean identification and confidence interval thereof. `binary` logical; `TRUE` (default) Selects binary analysis between a control and treatment variable.

## Value

For `(binary = FALSE)` returns the degree certainty the difference in sample means is zero [0, 1].

For `(binary = TRUE)` returns:

• `"Control Mean"`

• `"Treatment Mean"`

• `"Grand Mean"`

• `"Control CDF"`

• `"Treatment CDF"`

• `"Certainty"` the certainty of the same population statistic

• `"Lower Bound Effect"` and `"Upper Bound Effect"` the effect size of the treatment for the specified confidence interval

## Author(s)

Fred Viole, OVVO Financial Systems

## References

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" http://amzn.com/1490523995

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```### Binary analysis and effect size set.seed(123) x <- rnorm(100) ; y <- rnorm(100) NNS.ANOVA(control = x, treatment = y) ### Two variable analysis with no control variable A <- cbind(x, y) NNS.ANOVA(A) ### Multiple variable analysis with no control variable set.seed(123) x <- rnorm(100) ; y<- rnorm(100) ; z<- rnorm(100) A <- cbind(x, y, z) NNS.ANOVA(A) ```

NNS documentation built on April 15, 2019, 5:05 p.m.