I'm using the function nls.lm {package: minpack.lm} to optimize a parameteristion for a hydrological model. The function is working quite well, but I want to use an other objective function (OF). Normally, the obective function "fn" in the nls.lm is defined as
A function that returns a vector of residuals, the sum square of which
is to be minimized. The first argument of fn must be par.
Now I want to use the Nash-Sutcliff-Efficiency, which is defined as
NSE <- 1 - (sum((obs - sim)^2) / sum((obs - mean(obs))^2))
or other OF. The problem is that nls.lm minimizes the expression sum(x)^2 and only the x is modifiable. I know that the best fit NSE = 1. Thus 1 - NSE creates a real minimization problem.
BTW: Example 1 from a nls.lm help page is a good example; there
observed - getPred(p,xx)
is minimized, what actually means that
sum ( observed - getPred(p,xx) )^2
is minimized by the nls.lm function, whereas getPred(p,xx) returns sim.
Any suggestion would be helpful. Thanks in advance.
Micha
nls.lm (and the related functions nls, and nlsLM) are designed to minimize the sum square of the residuals. For the problem you seek to solve, I would try application of a general-purpose minimizer.
If the problem is 'not too hard' (that is, has a single global minimum, is smooth), you could try to apply 'optim' to it (I would try the 'Nelder-Mead' and 'BFGS' options first), or the 'bobyqa' function from the package 'minqa', among other functions.
If the problem requires a global optimizer, you could try the 'GenSA' function from package 'GenSA', the 'genoud' function from the package 'rgenoud', or the 'DEoptim' function from package 'DEoptim', among other options. A review on 'Global Optimization in R' is forthcoming in the Journal of Statistical Software, and that will give a more complete overview of applicable functions.
Related
I've tried the below code on RStudio and was expecting 0, 1 and 0.5 to show up. However, it showed a very small number instead of 0 and I thought it must be using some algorithm to approximate the sin function.
sin(c(pi, pi/2, pi/6))
This was the result
1.224606e-16 1.000000e+00 5.000000e-01
I wanted to know how they approximated the sin function in this case.
Though your question may seem simple at first, the reality is quite the opposite. Whenever you want to know what a function is doing, you just have to access the function as it were an object (it literally is an object in R):
sin # function (x) .Primitive("sin")
.Primitive is one of the ways R can call C. If you want to see the C-code, then you can use the pryr library as in:
pryr::show_c_source(.Primitive(sin(x)))
# do_math1 with op = 21
It also opens a Github page with the code of arithmetic.c, the arithmetic heart of R. R computes sin with the do_math1 function with option 21. If you want to go any further, you will need to understand how the sin function is estimated in C. For that, I recommend the following post:
How does C compute sin() and other math functions?
I have a 3D parametric curve defined as P(t) = [x(t), y(t), z(t)].
I'm looking for a function to reparametrize this curve in terms of arc-length. I'm using OpenSCAD, which is a declarative language with no variables (constants only), so the solution needs to work recursively (and with no variables aside from global constants and function arguments).
More precisely, I need to write a function Q(s) that gives the point on P that is (approximately) distance s along the arc from the point where t=0. I already have functions for numeric integration and derivation that can be incorporated into the answer.
Any suggestions would be greatly appreciated!
p.s It's not possible to pass functions as a parameter in OpenSCAD, I usually get around this by just using global declarations.
The length of an arc sigma between parameter values t=0 and t=T can be computed by solving the following integral:
sigma(T) = Integral[ sqrt[ x'(t)^2 + y'(t)^2 + z'(t)^2 ],{t,0,T}]
If you want to parametrize your curve with the arc-length, you have to invert this formula. This is unfortunately rather difficult from a mathematics point of view. The simplest method is to implement a simple bisection method as a numeric solver. The computation method quickly becomes heavy so reusing previous results is ideal. The secant method is also useful as the derivative of sigma(t) is already known and equals
sigma'(t) = sqrt[ x'(t)^2 + y'(t)^2 + z'(t)^2]
Maybe not really the most helpful answer, but I hope it gives you some ideas. I cannot help you with the OpenSCad implementation.
Incomplete gamma functions can be calculated in R with pgamma, or with gamma_inc_Q from library(gsl), or with gammainc from library(expint). However, all of these functions take only real input.
I need an implementation of the incomplete gamma function which will take complex input. Specifically, I have an integer for the first argument, and a complex number for the second argument (the limit in the integral).
This function is well-defined for complex inputs (see Wikipedia), and I've been calculating it in Mathematica. It doesn't seem to be built into R though, and I don't see it in any libraries.
So, can anyone suggest a shorter path to doing these calculations, than looking up an algorithm, implementing it in C, and writing an R interface?
(If I do have to implement it myself, here's the only algorithm for complex inputs that I've found: Kostlan & Gokhman 1987)
Here is an implementation, assuming you want the lower incomplete gamma function. I've compared a couple of values with Wolfram and they match.
library(CharFun)
incgamma <- function(s,z){
z^s * exp(-z) * hypergeom1F1(z, 1, s+1) / s
}
Perhaps the evaluation fails for a large s.
EDIT
Looks like CharFun has been removed from CRAN. You can use IncGamma in HypergeoMat:
> library(HypergeoMat)
> IncGamma(m=50, 2+2i, 5-6i)
[1] 0.3841221+0.3348439i
The result is the same on Wolfram.
This question already has answers here:
modify glm function to adopt user-specified link function in R
(2 answers)
Closed 7 years ago.
This question has already been somewhat addressed already in the past on this site, but the answers provided are not fully helpful to me. Here are the details of my questions that are actually somewhat different from what has already been discussed here:
After working hard on this, I remained unable to understand how I can define my own user-specified link function in R for glm. I have several questions on this.
First of all, I understand I have to write my own function (likely modifying one that already exists), and - in it - I need to define the following elements:
linkfun: the link function.
linkinv: the inverse of the link function, as a function of "eta".
mu.eta: the first derivative of the invlink respect to eta.
valideta: that must return TRUE if the value of eta are in the correct interval
And return all of this in a list element.
So far, so good.
Here is the first set of my questions:
The link function is sometimes defined as a function of "y" and sometimes as a function of "mu". What must be done in this respect?
Let's take an example, and type make.link("sqrt"). We then indeed discover that linkfun is sqrt(mu), linkinv is eta^2, mu.eta is 2*eta. So far, so good. However, if you look at make.link("log"), mu.eta is not simply exp(eta) as it should, but pmax(exp(eta), .Machine$double.eps) (i.e., the maximal values of the first derivative for all the eta vector). Why? I remained unable to understand this.
Just for my curiosity, why the algorithm needs the first derivative of the invlink respect to eta? This is not fully clear to me.
In my specific case, I need a quasi-logistic regression for binomial data. Instead of having a standard logit function log(p/(1-p)), I need to have the slightly modified link function (if p is defined as Y/N): log((Y+0.5)/(N-Y+0.5)).
My other question in this case is:
I remained unable to built this.. Can someone give me some hints?
Where can I find a detailed explanation of all of this? I have looked at the good old Chambers & Hastie book (1992), but the explanation is not sufficient. Are there any detailed courses available on the web, etc.?
Not sure whether I can answer all of your questions, but I give it a try:
Can you specify a linkfun which takes mu and y? Up to my knowledge, the link function should only tkae mu as the GLM (as opposed to the LM) models a function of the expecetd value mu (aka linkfunction) instead of the expecetd value itself. Hence, there should be only mu as an argument.
This has to do with vectorization. pmax returns the parallel maxima and we want to assure that we do not report values smaller than Machine$double.eps. So the linkfun does not return the maximum of all exp(eta) (that would be max(exp(eta), .Machine$double.eps)). Imagine now that eta is now a vector of all eta for which you want to calculate then linkinv, with pmax(.) you make sure that you return exp(eta) only in these cases where it is indeed larger than .Machine$double.eps. So you return also a vector of maxima. (try pmax(1:6, 4) you will get [1] 4 4 4 4 5 6)
You need the first derivative in order to calcuate the estimator of the score function of dL / dbeta[j] = sum_i^n((y[i] - mu[i])/(a(phi[i] * V(mu[i])x[ij]/g'(mu[i]) = 0. That is the derivative of the likelihood function w.r.t. to beta[j] (i.e. dL/dbeta[j]) depends on:
a(phi[i]) is a (known) function of the dispersion parameter coming from the respective distribution (e.g. a(phi) = phi = sigma^2 for the normal distribution)
V(mu[i]) for distributions of the exponential family (for which the GLM was designed) you can derive that var(Y) can be written as a(phi) * V(mu) indicating that the variance is indeed a function of the mean.
g'(mu[i]) is finally the derivative of the link function. So in order to solve the score function (thus to get estimates for beta[j], you will need the derivative of the link function
So in your case you need to define:
the linkfun
the inverse
the derivative
function to validate eta
I see your problem that you link function would also need to take y as an parameter, however, I am not sure whether the glm can deal with it, because in its fitting mechanism it will call linkfun at some point and looking at the pre-defined linkfuns, all of these require just one parameter. You could get around with that if you twist the code of glm but this will be quite some work to do (all things untested and just as food for thoughts without any guarantee that it will work):
Provide your linkfun/linkinvers etc as something like function(mu, y) [whatever you want to have here]
Create a copy of glm.fit (glm.fit2 say)
Change calls fo linkfun(mu), linkinv(eta) etc to linkfun(mu, y), linkinv(eta, y) and so forth
when you call your glm provide method = "glm.fit2" to tell glm that it should use your own fitting procedure.
The refernce book for that is McCullagh, Nelder: Generalized Linear Models. Where you find all the explanations about the exponential family of distributions, score functions etc.
You can look into function powerVarianceFamily of package EQL which also uses parameterized families for extended quasi likelihood estaimation.
Update
As just learned from the excellent answer in the previous post, no need to redefine the glm.fit as long as you use y in you linkfun, as by the time linkfun is called y should be known in the encapsualting function. So you should define linkfun like this
function(mu) [a function which uses mu and y -
as y is known within the context where this function is called]
I would like to use optim() to optimize a cost function (fn argument), and I will be providing a gradient (gr argument). I can write separate functions for fn and gr. However, they have a lot of code in common and I don't want the optimizer to waste time repeating those calculations. So is it possible to provide one function that computes both the cost and the gradient? If so, what would be the calling syntax to optim()?
As an example, suppose the function I want to minimize is
cost <- function(x) {
x*exp(x)
}
Obviously, this is not the function I'm trying to minimize. That's too complicated to list here, but the example serves to illustrate the issue. Now, the gradient would be
grad <- function(x) {
(x+1)*exp(x)
}
So as you can see, the two functions, if called separately, would repeat some of the work (in this case, the exponential function). However, since optim() takes two separate arguments (fn and gr), it appears there is no way to avoid this inefficiency, unless there is a way to define a function like
costAndGrad <- function(x) {
ex <- exp(x)
list(cost=x*ex, grad=(x+1)*ex)
}
and then pass that function to optim(), which would need to know how to extract the cost and gradient.
Hope that explains the problem. Like I said my function is much more complicated, but the idea is the same: there is considerable code that goes into both calculations (cost and gradient), which I don't want to repeat unnecessarily.
By the way, I am an R novice, so there might be something simple that I'm missing!
Thanks very much
The nlm function does optimization and it expects the gradient information to be returned as an attribute to the value returned as the original function value. That is similar to what you show above. See the examples in the help for nlm.