[R] is that possible to graph 4 dimention plot
Petr PIKAL
petr.pikal at precheza.cz
Tue Oct 13 10:00:21 CEST 2009
Hi
I tried to do similar thing and did get great answer from Alberto Monteiro
http://tolstoy.newcastle.edu.au/R/e4/help/08/01/0682.html
However I finally managed to do it by slicing space by combination of
image and contour together with putting numbers on contour. Then I used
scizors and glue to put everything into 3 dimensions with colour and
numbers indicating tha values.
Regards
Petr
r-help-bounces at r-project.org napsal dne 12.10.2009 14:40:29:
> I'm basically put off by the question itself. Plotting a 4-dimensional
> graph is rather "complicated" if the world has only 3 dimensions. A
> 4-dimensional representation is typically a movie (with time as the
> 4th dimension). You could try to project a heatmap on a 3D surface
> graph, but I doubt this will make things much more clear.
>
> So the standard (and correct) way of solving this problem, is to think
> about a clever way to represent the needed information in less
> dimensions. Ryan gives some nice examples and tips of how to do that,
> but those are dismissed as "not helpful".
>
> People on the list answer voluntarily. They do not like to be told
> that "you don't think it will really help". Did you actually try it
> out? You certainly don't give that impression. Maybe you should have
> another look at it, and question your own approach to the problem as
> well.
>
> Keep it in mind for next time, you're not making yourself popular this
way.
>
> Kind regards
> Joris
>
>
>
> On Sat, Oct 10, 2009 at 10:01 PM, Duncan Murdoch <murdoch at stats.uwo.ca>
wrote:
> > On 07/10/2009 5:50 PM, gcheer3 wrote:
> >>
> >> Thanks for your reply.
> >>
> >> But I don't think it will really help. My problem is as follows:
> >>
> >> I have 20 observations
> >> y <- rnorm(N,mean= rep(th[1:2],N/2),sd=th[3])
> >>
> >> I have a loglikelihood function for 3 variables mu<-(mu1,mu2) and sig
> >> loglike <- function(mu,sig){
> >> temp<-rep(0,length(y))
> >> for (i in 1:(length(y)))
> >> {
> >>
> >>
temp[i]<-log((1/2)*dnorm(y[i],mu[1],sig)+(1/2)*dnorm(y[i],mu[2],sig))}
> >> return(sum(temp))
> >> }
> >>
> >> for example
> >>>
> >>> mu<-c(1,1.5)
> >>> sig<-2
> >>> loglike(mu,sig)
> >>
> >> [1] -34.1811
> >>
> >> I am interested how mu[1], mu[2], and sig changes, will effect the
> >> loglikelihood surface. At what values of mu and sig will make
> >> loglikelihood
> >> the maximum and at what values of mu and sig will make loglikelihood
has
> >> local max (smaller hills) and at what values of mu and sig the
> >> loglikelihood
> >> is flat , etc.
> >> I tried contour3d also, seems doesn't work
> >
> > I haven't seen any replies to this. One explanation would be that
everyone
> > was turned off (as I was) by the rude remark above.
> >
> > On this list, before saying that something "doesn't work", it's polite
to
> > give a simple, nicely formatted, self-contained reproducible example
of what
> > went wrong, and to ask whether it is your error or an error in the
package.
> > Taking that approach will usually result in someone pointing out your
error
> > (and fixing your code); sometimes it will result in a package author
> > agreeing it's a bug, and fixing it.
> >
> > Duncan Murdoch
> >
> >>
> >> Thanks for any advice
> >>
> >>
> >> Ryan-50 wrote:
> >>>>
> >>>> Suppose there are 4 variables
> >>>> d is a function of a , b and c
> >>>> I want to know how a, b and c change will make d change
> >>>> It will be straightforward to see it if we can graph the d surface
> >>>>
> >>>> if d is only a function of a and b, I can use 'persp' to see the
surface
> >>>> of
> >>>> d. I can easily see at what values of a and b, d will get the
maxium or
> >>>> minium or multiple modes, etc
> >>>>
> >>>> But for 4 dimention graph, is there a way to show the surface of d
> >>>> Will use color help
> >>>>
> >>>> Thanks a lot
> >>>
> >>> Not sure what your data looks like, but you might also consider
looking
> >>> at a 2 dimensional version. See ?coplot
> >>> for example:
> >>>
> >>> coplot(lat ~ long | depth * mag, data = quakes)
> >>>
> >>> Or you can make 2 or 3-dimensional plots using the lattice package
> >>> conditioning on some of the variables - e.g. d ~ a | b * c,
> >>> etc.
> >>> If a, b, and c are "continuous", you can use equal.count. Here is
> >>> an uninteresting example, considering a, b, and c as points along
> >>> a grid:
> >>>
> >>> a <- b <- c <- seq(1:10)
> >>> dat <- data.frame(expand.grid(a, b, c))
> >>> names(dat) <- letters[1:3]
> >>>
> >>> dat$d <- with(dat, -(a-5)^2 - (b-5)^2 - (c-5)^2)
> >>>
> >>> library(lattice)
> >>> # 2-d:
> >>> xyplot(d ~ a | equal.count(b)*equal.count(c), data=dat, type="l")
> >>> # etc.
> >>>
> >>> # 3-d:
> >>> contourplot(d ~ a * b | equal.count(c), data=dat)
> >>> wireframe(d ~ a * b | equal.count(c), data=dat)
> >>>
> >>> ______________________________________________
> >>> R-help at r-project.org mailing list
> >>> https://stat.ethz.ch/mailman/listinfo/r-help
> >>> PLEASE do read the posting guide
> >>> http://www.R-project.org/posting-guide.html
> >>> and provide commented, minimal, self-contained, reproducible code.
> >>>
> >>>
> >>
> >
> > ______________________________________________
> > R-help at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
> ______________________________________________
> R-help at r-project.org mailing list
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> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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