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
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@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.
>
>
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