Description Usage Arguments Value Author(s) References Examples
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 pvalue.
1 2 
control 
a numeric vector, matrix or data frame. 
treatment 

confidence.interval 
numeric [0, 1]; The confidence interval surrounding the control mean when 
tails 
options: ("Left", "Right", "Both"). 
pairwise 
logical; 
plot 
logical; 
binary 
logical; 
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
Fred Viole, OVVO Financial Systems
Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" http://amzn.com/1490523995
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)

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