NNS.ANOVA {NNS} | R Documentation |
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.
NNS.ANOVA( control, treatment, confidence.interval = 0.95, tails = "Both", pairwise = FALSE, plot = TRUE, robust = FALSE )
control |
a numeric vector, matrix or data frame. |
treatment |
|
confidence.interval |
numeric [0, 1]; The confidence interval surrounding the |
tails |
options: ("Left", "Right", "Both"). |
pairwise |
logical; |
plot |
logical; |
robust |
logical; |
Returns the following:
"Control Mean"
control
mean.
"Treatment Mean"
treatment
mean.
"Grand Mean"
mean of means.
"Control CDF"
CDF of the control
from the grand mean.
"Treatment CDF"
CDF of the treatment
from the grand mean.
"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.
"Robust Certainty Estimate"
and "95 CI"
are the robust certainty estimate and its 95 percent confidence interval after permutations if robust = TRUE
.
Fred Viole, OVVO Financial Systems
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
### 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)