Hi Gavin,
Here's a sort of brute force way to nudge points. Might be helpful.
nudge<-function(x,y,away=0.1,tol=0.01) {
dimx<-dim(x)
if(missing(y) && !is.null(dimx)) {
y<-x[,2]
x<-x[,1]
}
xlen<-length(x)
if(xlen != length(y)) stop("x and y must be the same length.")
for(i in 1:xlen) {
f
That (the need for base graphics) is false. It certainly **can** be done in
base graphics -- see ?layout for a perhaps more straightforward way to do
it along the lines you suggest.
However both lattice and ggplot are based on grid graphics, which has a
similar but slightly more flexible ?grid.lay
Thank you for your example. I have successfully modified it with regard to most
fields. However, I think that am not going to be putting this out for my
students. The class I am currently teaching is in multivariate statistics (both
methods and applications, with R used for computing) and I use
For this kind of control you will probably need to move to base graphics
and utilize the `fig` argument in par(), in which case you would want to
run the plot() command twice: once with your first outcome and once with
your second, changing the par() settings before each one to control the
size
You probably ought to read the CRAN Optimization Task View. [1]
You should also read the Posting Guide mentioned at the bottom of every R-help
email (e.g. no homework, use plain text email). You should also read some
guides on asking questions online (e.g. [2][3][4]).
[1] https://cran.r-project
> On 20 Jan 2018, at 07:53 , Suharto Anggono Suharto Anggono via R-help
> wrote:
>
> Extremes.Rd, that documents 'max' and 'pmax', has this in "Details" section,
> in the paragraph before the last.
> By definition the min/max of a numeric vector containing an NaN is NaN,
> except that the
On 20/01/2018 6:53 AM, akshay kulkarni wrote:
dear members,
I have a function by name "ygcudf" and a list of stock names by
name"snl" ( of class "list"). ygcudf acts on snl and returns a list of the most
favourable stocks. I don't wish to divulge the code of the
--- Version 2 of my problem improving the definition of what
the optimal solution would be.
Dear all,
I'm working on the following problem:
Assume two datasets: Y, Y that represent the same physical quantity Q. Dataset
X contains values of Q after an event A while dataset Y cont
Dear all,
I'm working on the following problem:
Assume two datasets: Y, Y that represent the same physical quantity Q. Dataset
X contains values of Q after an event A while dataset Y contains values of Q
after an event B.
In R X, Y are vectors of the same length, containing effectivelly a numb
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