Displaying 5 results from an estimated 5 matches for "twotailed".
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2003 Jan 24
0
new function: twotailed.colors {base}
...two colors.
This function is similar to `cm.colors' but the colors
can be choosen by hsv values.
This function could be used e.g. as alternative to
the default ``col.regions'' in `levelplot'.
Perhaps the arguments in the following code could be
simplified.
Wolfram Fischer
#--- twotailed.colors.R
twotailed.colors <-
function(
n = 7 # number of colors to be in the palette
, n5 = n %% 2 # number of colors between the two tails
, h1 = 0.02 # 0|1 = rot
, h2 = 0.15 # 0.7 = blau, 0.15 = samtgelb, 0.35 = grün
, s0 = 1 # saturation: begin and end
, s5 = 1/n*1.4...
2023 Mar 22
1
How to test the difference between paired correlations?
...d into 1.08; is this correct?)
FisherZ(lm(v2~v1)$coefficients[2])
> v1
> 1.081667
lm(v2~v1)$coefficients[2]
> v1
> 0.7938164
# apply test
v1_v2 = FisherZ(lm(v2~v1)$coefficients[2])
v1_v3 = FisherZ(lm(v3~v1)$coefficients[2])
paired.r(v1_v2, v1_v3, yz=NULL, length(v1), n2=NULL, twotailed=TRUE)
> Call: paired.r(xy = v1_v2, xz = v1_v3, yz = NULL, n = length(v1), n2 = NULL,
> twotailed = TRUE)
> [1] "test of difference between two independent correlations"
> z = NaN With probability = NaNWarning messages:
> 1: In log((1 + xy)/(1 - xy)) : NaNs produced
>...
2023 Mar 23
1
How to test the difference between paired correlations?
...v1)$coefficients[2])
> > v1
> > 1.081667
> lm(v2~v1)$coefficients[2]
> > v1
> > 0.7938164
> # apply test
> v1_v2 = FisherZ(lm(v2~v1)$coefficients[2])
> v1_v3 = FisherZ(lm(v3~v1)$coefficients[2])
> paired.r(v1_v2, v1_v3, yz=NULL, length(v1), n2=NULL, twotailed=TRUE)
> > Call: paired.r(xy = v1_v2, xz = v1_v3, yz = NULL, n = length(v1), n2 = NULL,
> > twotailed = TRUE)
> > [1] "test of difference between two independent correlations"
> > z = NaN With probability = NaNWarning messages:
> > 1: In log((1 + xy)/(1 -...
2006 Mar 30
1
warning message in hand-made function
...pr = 0.975, x = c*
(n^0.5), df = dl)
CI.U <- pnorm(deltaL$root/(n^0.5)) * 100 #upper bound of the
confidence interval
CI.L <- pnorm(deltaU$root/(n^0.5)) * 100 #lower bound of the
confidence interval
#output
output <- list(statistic=t.obs, p.value=c
(one.tailed=proba.onetailed, twotailed=2*proba.onetailed), rarity=c
(rarity=rar, lower.boud=CI.L, upper.bound=CI.U), df=dl, method=paste
("Crawford modified t test with", dl, "degrees of freedom", sep=" "))
class(output)<-"htest"
return(output)
}
Matthieu Dubois, PH.D. Student
Cognitive...
2006 Jan 01
20
A comment about R:
Readers of this list might be interested in the following commenta about R.
In a recent report, by Michael N. Mitchell
http://www.ats.ucla.edu/stat/technicalreports/
says about R:
"Perhaps the most notable exception to this discussion is R, a language for
statistical computing and graphics.
R is free to download under the terms of the GNU General Public License (see
http://www.r-project.