search for: arctan

I have a set of data that is distributed normally. How can I transform this data to fit an ArcTan distribution? [[alternative HTML version deleted]]

I need to do a non-linear regression in the form of Y = a0 + a1 * arctan(a2 * x) + error. A data sample (X,Y) is available, but I can't remember how to run this sort of regression through R so that I get a value for a0, a1 and a2. Can someone please give me a hint? Thank you in advance.

The following has been committed to the Vorbis I spec as of r19445: Errata 20150226: Single entry codebooks A ?single-entry codebook? is a codebook with one active codeword entry. A single-entry codebook may be either a fully populated codebook with only one declared entry, or a sparse codebook with only one entry marked used. The Vorbis I spec provides no means to specify a codeword length of

Hi, In the course of some work which I describe below, I have found a very significant difference between the BARK function described in the Vorbis specification and its implementation in libvorbis. In the specificationhttp://xiph.org/vorbis/doc/Vorbis_I_spec.pdf bark(x) = 13.1arctan(.00074x) + 2.24arctan(.0000000185x**2 + .0001x) In the libvorbis code http://svn.xiph.org/trunk/vorbis/lib/scales.h #define toBARK(n) (13.1f*atan(.00074f*(n))+2.24f*atan((n)*(n)*1.85e-8f)+1e-4f*(n)) You will note that the last term is inside the parentheses of the second arctan function in the...

...= three methods are used to calculate E(s2|X) for different sample sizes: Method 1 ========= (integral(s2*L(X|s2)*p(s2)))/(integral(L(X|s2)*p(s2))) where the integrals are calculated using the integrate function Method 2 ========= transform s2 to tau (from (0,Inf) to (-1,1)) f(s2) = tau = 4*arctan(s2)/pi-1 so E(s2|X) = (integral(jac*finv(s2)*L(X|finv(s2))*p(finv(s2))))/(integral(L(X|finv(s2))*p(finv(s2)))) . the bounds on both integrals is (-1,1) jac = the jacobian and finv is the inverse function of f(s2) once again the integrals are calculated using the intergrate function Meth...

Dear all, I have the following dataset: each row corresponds to count of forest floor small mammal captured in a plot and vegetation characteristics measured at that plot > sotr plot cnt herbc herbht 1 1A1 0 37.08 53.54 2 1A3 1 36.27 26.67 3 1A5 0 32.50 30.62 4 1A7 0 56.54 45.63 5 1B2 0 41.66 38.13 6 1B4 0 32.08 37.79 7 1B6 0 33.71 30.62

Hi Apart from nlm() with the stepmax= argument, is there any other >1 dimensional minimisation function where I can limit the step size dynamically. With the NAG routine E04NBF (I think) I used to bound the size of the next step using a*arctan(x/a) tricks where a function was particularly difficult near a boundary and thus 'steer' the result within legal limits. The function I am trying to minimise is (negative) log likelihood for the GEV but with a *lot* of right-censoring. I am not currently sure whether it is a numerical pro...

Dear R-users, I'd like to fit a sine function to my data. The result should have a format (and thus the formula, too) y ~ a + sin(x+b) where y and x are vectors, and a and b are (yet) unknown values. The data sets (vectors x and y) are OK, and I can do a simple lm fitting lm(y~x), or lm(y~I(sin(2*pi*x/360))), succesfully My issue is that I'm not able to do the optional linear shift in