Calculate Frequency/Period from Output of a Maximum Entropy Spectrum MATLAB function When I plot said function output, "p," it looks like a typical spectrum with an easily identifiable peak at a given ix on the x axis. The help rubric from the function is

SYNTAX: p = mesa(x,m,nfreq);
For a vector x, this function calculates a maximum-entropy spectrum
of order m. The spectral estimate is returned in the vector p, which
has nfreq points linearly spaced in the Nyquist frequency interval 0-.5.
The psd is normalized such that the mean square value of x equals the
integral of p from -.5 to .5, so sum(x.^2)/N ~= sum(p)/nfreq.
Mesa is based on the Burg algorithm, as described in Numerical Recipes
and implemented in their memcof and evlmem subroutines.

Written by Eric Breitenberger        Version 5/24/95


My problem is that the x axis is unitless, i.e. if nfreq = 200 then the x axis is 1 to 200 long. If the peak is at x axis ix = 22, for example, how do I calculate the measured frequency/period of the input signal "x"?

To give a concrete example: 

the length of the input vector "x" is 20 because I believe (or know due to a controlled input experiment) this length vector contains one full cycle (noisy sinusoidal) of interest 

order m is set to 10, for example, although this is incidental to my question as I can see from experiment that changing the value of m only affects the sharpness of the peak 

nfreq is set to 200

and with these values the peak occurs at x axis ix == 22.

From this, what is the mathematics to get the measured frequency/period from the output of the function?

Plot of the output

for a known input, e.g.

input_x = awgn( sinewave( 20 , 20 ) , 10 ) ;
From the code on p.$7$ of this document it can be seen that the $N$ (nfreq in the code) frequency points are chosen equidistantly between zero and Nyquist (i.e., $f_s/2$, with $f_s$ being the sampling frequency) using the command linspace(). So the individual frequencies are given by

$$f_k=\frac{kf_s}{2(N-1)},\qquad k=0,1,\ldots, N-1\tag{1}$$

[Note that the indices $k$ in $(1)$ start at zero.]