Hi Abou,
Sure:
library(plotrix)
pdf("circles.pdf")
plot(0:10,type="n",axes=FALSE,xlab="",ylab="")
ymult=getYmult()
draw.circle(4,5,radius=3,border="#ffaa",lwd=10)
for(angle in seq(0,1.95*pi,by=0.05*pi))
draw.circle(4+3*cos(angle),5+3*sin(angle)*ymult,
radius=runif(1,0.05,0.1),col="#00ff00aa
I'm trying to perform a mantel test that ignores specific pairs in my
distance matrices. The reasoning is that some geographic distances
below a certain threshold suffer from spatial autocorrelation, or
perhaps ecological relationships become less relevant that stochastic
processes above a certain
The results are very sensitive in some cases to configuration (tolerances,
etc.) and problem.
Are you using the "follow-on" option? That will definitely be order dependent.
optimx is currently under review by Ravi Varadhan and I. Updating optimx proved
very difficult
because of interactions bet
Dear All:
Thank you very much for all of you.
I just have one more thing. Is there a way to fill the borders with small
dots, may be different sizes.
I tried to do it, but it looks ugly.
Here what I tried:
library(plotrix)
plot(0:10, 0:10, type="n",axes=FALSE,xlab="",ylab="") 0:5,
Dear R-er,
For a non-linear optimisation, I used optim() with BFGS method but it
stopped regularly before to reach a true mimimum. It was not a problem
with limit of iterations, just a local minimum. I was able sometimes to
reach better minimum using several rounds of optim().
Then I moved t
Another solution:
library("HelpersMG") plot(0:10,type="n",axes=FALSE,xlab="",ylab="",
asp=1) ellipse(center.x = 3, center.y = 5, radius.x = 5, radius.y = 5,
lwd=10, col=NA, border=rgb(red = 1, green = 0, blue=0, alpha = 0.5))
ellipse(center.x = 8, center.y = 5, radius.x = 5, radius.y = 5, lwd=1
That code nees the plotrix package:
library(plotrix)
pdf("circles.pdf")
plot(0:10,type="n",axes=FALSE,xlab="",ylab="")
draw.circle(4,5,radius=3,border="#ffaa",lwd=10)
draw.circle(6,5,radius=3,border="#ffaa",lwd=10)
dev.off()
On Friday, December 29, 2017, 6:06:32 PM EST, Jim Lemon
Dear all,
I am doing a one way between subjects anova in an unbalanced data set.
Suppose we have "a" levels of the one factor. I want to merge the not so
significantly different levels into the same cluster.
Can I do a Tukey Kramer HSD and then use the following algorithm:
For i in 2 : "a"
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