Fitting an inverse function

Question

I have a function which looks like: g(x) = f(x) - a^b / f(x)^b

g(x) - known function, data vector provided.
f(x) - hidden process.
a,b - parameters of this function.

From the above we get the relation:
f(x) = inverse(g(x))

My goal is to optimize parameters a and b such that f(x) would be as close as possible
to a normal distribution. If we look on a f(x) Q-Q normal plot (attached), my purpose is to minimize the distance between f(x) to the straight line which represents the normal distribution, by optimizing parameters a and b.

I wrote the below code:

g_fun <- function(x) {x - a^b/x^b}

inverse = function (f, lower = 0, upper = 2000) {
      function (y) uniroot((function (x) f(x) - y), lower = lower, upper = upper)[1]
}


f_func = inverse(function(x) g_fun(x))
enter code here

# let's made up an example 
# g(x) values are known 
g <- c(-0.016339, 0.029646, -0.0255258, 0.003352, -0.053258, -0.018971, 0.005172,  
       0.067114, 0.026415, 0.051062)  

# Calculate f(x) by using the inverse of g(x), when a=a0 and b=b0
for (i in 1:10) {  
  f[i] <- f_fun(g[i])  
}

I have two question:

1. How to pass parameters a and b to the functions?
2. How to perform this optimization task, meaning find a and b such that f(x) would approximate normal distribution.

Answer 1

Not sure how you were able to produce the Q-Q plot since your provided examples do not work. You are not specifying the values of a and b and you are defining f_func but calling f_fun. Anyway here is my answer to your questions:

1. How to pass parameters a and b to the functions? - Just pass them as arguments to the functions.
2. How to perform this optimization task, meaning find a and b such that f(x) would approximate normal distribution? - The same way any optimization task is done. Define a cost function, then minimize it.

Here is the revised code: I have added a and b as parameters, removed the inverse function and incorporated it inside f_func, which can now take vector input so no need for a for loop.

g_fun <- function(x,a,b) {x - a^b/x^b}

f_func = function(y,a,b,lower = 0, upper = 2000){
  sapply(y,function(z) { uniroot(function(x) g_fun(x,a,b) - z, lower = lower, upper = upper)$root})
} 

# g(x) values are known 
g <- c(-0.016339, 0.029646, -0.0255258, 0.003352, -0.053258, -0.018971, 0.005172,  
       0.067114, 0.026415, 0.051062)  
f <- f_func(g,1,1) # using a = 1 and b = 1
#[1] 0.9918427 1.0149329 0.9873386 1.0016774 0.9737270 0.9905320 1.0025893
#[8] 1.0341199 1.0132947 1.0258569

f_func(g,2,10)
 [1] 1.876408 1.880554 1.875578 1.878138 1.873094 1.876170 1.878304 1.884049
 [9] 1.880256 1.882544

Now for the optimization part, it depends on what you mean by f(x) would approximate normal distribution. You can compare mean square error from the qq-line if you want. Also since you say approximate, how close is good enough? You can go with shapiro.test and keep searching till you find p-value below 0.05 (be ware that there may not be a solution)

shapiro.test(f_func(g,1,2))$p
[1] 0.9484821

cost <- function(x,y) shapiro.test(f_func(g,x,y))$p

Now that we have a cost function how do we go about minimizing it. There are many many different ways to do numerical optimization. Take a look at optim function http://stat.ethz.ch/R-manual/R-patched/library/stats/html/optim.html.

optim(c(1,1),cost)

This final line does not work, but without proper data and context this is as far as I can go. Hope this helps.