cliff.delta(d, ... )
"cliff.delta"(formula, data=list() ,conf.level=.95, use.unbiased=TRUE, use.normal=FALSE, return.dm=FALSE, ...)
"cliff.delta"(d, f, conf.level=.95, use.unbiased=TRUE, use.normal=FALSE, return.dm=FALSE, ...)
f
is a factor) or the treatment group values (if f
is a numeric vector)
y ~ f
, where y
is a numeric variable giving the data values and f
a factor with two levels giving the corresponding group
formula
. By default the variables are taken from environment(formula)
.
effsize
containing the following components:
containing the following components:The magnitude is assessed using the thresholds provided in (Romano 2006), i.e. |d|<0.147 "negligible", |d|<0.33 "small", |d|<0.474 "medium", otherwise "large"
If the dominance matrix is required i.e. return.dm=TRUE
) the full matrix is computed thus using the naive algorithm.
Otherwise, if treatment
and control
are factor
s then the optimized linear complexity algorithm is used, otherwise the RLE algorithm (with complexity n log n) is used.
J. Romano, J. D. Kromrey, J. Coraggio, J. Skowronek, Appropriate statistics for ordinal level data: Should we really be using t-test and cohen's d for evaluating group differences on the NSSE and other surveys?, in: Annual meeting of the Florida Association of Institutional Research, 2006.
K.Y. Hogarty and J.D.Kromrey (1999). Using SAS to Calculate Tests of Cliff's Delta. Proceedings of the Twenty-Foursth Annual SAS User Group International Conference, Miami Beach, Florida, p 238. Available at: http://www2.sas.com/proceedings/sugi24/Posters/p238-24.pdf
cohen.d
, print.effsize
## Example data from Hogarty and Kromrey (1999)
treatment <- c(10,10,20,20,20,30,30,30,40,50)
control <- c(10,20,30,40,40,50)
res = cliff.delta(treatment,control,return.dm=TRUE)
print(res)
print(res$dm)
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