NNS.ANOVA: NNS ANOVA

Description Usage Arguments Value Author(s) References Examples

View source: R/ANOVA.R

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

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NNS.ANOVA(
  control,
  treatment,
  confidence.interval = 0.95,
  tails = "Both",
  pairwise = FALSE,
  plot = 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, 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 (default) Returns pairwise certainty tests when set to pairwise = TRUE.

plot

logical; TRUE (default) Returns the boxplot of all variables along with grand mean identification and confidence interval thereof.

Value

Returns the following:

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

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### 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 June 26, 2021, 1:07 a.m.