similar to: Parabolic cylinder function

Dear R users and experts, I want to fit a shifted parabolic function with the following functional form to my data: f(x)=a0*(x+a1)^2+a2 (a0, a1 and a2 are scaling factors.) What is standard approach to do this in R? I tried the "lm" function in R but I got problems getting the above functional form. Any help is welcome :) . Greetings, Henri

Thanks for the confirmation Jean-Marc. I kind of suspected from the comments that it was the confluent hypergoemetric function, which I was trying to evaluate using Kummer's equation, namely: M(a;b;x) is the sum from n=0 to infinity of (a)n*x^n / (b)n*n! where (a)n = a(a+1)(a+2) ... (a+n-1) But when I use Kummer's equation, I don't get the values in the "hypergeom_gain"

By my reckoning the confluent hypergoemetric functions should have the following values: M(-.25;1;-.5) = 1.11433 M(-.25;1;-1) = 1.21088 M(-.25;1;-1.5) = 1.29385 M(-.25;1;-2) = 1.36627 M(-.25;1;-2.5) = 1.43038 M(-.25;1;-3) = 1.48784 M(-.25;1;-3.5) = 1.53988 M(-.25;1;-4) = 1.58747 M(-.25;1;-4.5) = 1.63134 M(-.25;1;-5) = 1.67206 M(-.25;1;-5.5) = 1.71009 M(-.25;1;-6) = 1.74579 M(-.25;1;-6.5) =

I got the approximation from a Google book: http://books.google.com/books?id=2CAqsF-RebgC&pg=PA385 Page 392, formula (10.33) Using this formula, you're right, hypergeom_gain() would *not* converge to 1 for large x, but would instead be gamma(1.25)/sqrt(sqrt(x)) which would approach zero. Now if the formula for the hypergeometric gain were instead gamma(1.5) * M(-.5;1;-x) / sqrt(x)

Hi, I've been trying to re-create the table in the function "hypergeom_gain" in preprocess.c, and I just simply can't get the same values. I get the same value for the first element, so I know I'm computing gamma(1.25)^2 correctly, but I can't get the same numbers for M(-.25;1;-x), which I assume is Kummer's function. Is it possible that the comment is out of

hello, we start to develop an open source project. This project is a VoIp application with the same features like "Teamspeak" (http://www.goteamspeak.com). Our test application run in peer to peer mode under windows 2000 and above. At the moment we are using the gsm codec (13 kbps bandwidth). Later there will be a port for *unix systems. As aforementioned the project will be released

Hello, I need to use the confluent function of second kind, also known as Tricomi function. It is implemented as kummerU() function in fAsianOptions package, but I've found very inaccurate values, comparing with those provided by Mathematica. I think Mathematica values are OK because kummerU values leads to negative probabilities. For example, if you try the kummerU() function example, you

Dear all, Does any know how to solve PDE with R? The archive list refers to the use of ODE if PDE are parabolic. I am not a mathematician and this does not mean anything for me! help would be very appreciated. Many thanks -- ___________________________________________________ Christophe NGUYEN UMR 1220 INRA-ENITAB Transfert sol-plante et cycle des ?l?ments min?raux dans les ?cosyst?mes

Quoting asterisk-users-request@lists.digium.com: > > Would it be considered trolling to start a thread on Cleaning Maple > > Syrup off of Dial Pads, or Wiring your Moose for Wi-Fi? > > Let's not forget the weekly "tooques and telephony" segment, and a > review of > the best block heaters for your wi-fi fones. > Oh, we're gonna have a good time next

I have an array of floating-point measurements on a square (5 by 5) 2d grid. The data are nominally constant, and somewhat noisy. I need to find any significant spatial trend, e.g. bigger on the left, bigger in the middle, etc. I have many thousands of these data sets that need to be scanned for 'interesting' spatial variations, selecting the datasets that are beyond some criterion of

Hi, I've got a matrix, Z, of values representing (as it happens) optical power at each pixel location. Since I know in advance I've got a single, convex peak, I would like to do a 2D parabolic fit of the form Z = poly((x+y),2) where x and y are the x,y coordinates of each pixel (or equivalently, the row, column numbers). Is there an R function that lets me easily implement that?

Hi everybody, I am using the L-BFGS-B method of the mle2() function to estimate the values of 6 parameters. mle2 uses the methods implemented in optim. As I got it from the descriptions available online, one can use the parscale parameter to tell R somehow what the values of the estimated parameters should be . . . Could somebody please help me understand what one has to do actually with the

Hi, In order to use the mle2-function, one has to define the likelihood function itself. As we know, the likelihood function is a sum of the logarithm of probability density functions (pdf). I have implemented myself the pdfs that I am using. My problem is, that the pdfs values are negative and I cann't take the logarithm of them in the log-likelihood function. So how can one take the

Rers: I have some ecological data (stream velocity vs. % cover of submerged weeds) that shows strong evidence of a thresholding step-function, e.g. below some velocity, % cover ranges from 0% to 100% (with no apparent relationship to velocity within this range of velocities), but above a certain "threshold" velocity, the % cover does not appear to exceed, say, 10%. There are good

Hello, I'm hoping for some help implementing a general optimization problem in R. I'd like to write a program that for a vector of non-negative input values, x, returns a non-negative "normalized" vector y such that sum(y)==1, and y <= maxx (vector of maximum values), and for which sum((x-y)^2) is minimized. Additionally, I'd like to remove (0,minx) from the domain of each

Hi, I'm using R to find esplicit functions fitting set of data. The data contains about 30 points, which have different weights (number of cases represented from the point). I plot the points, choose "by eye" a function made with exp or arctg or polinomial and use nlm to minimaze the root mean error with correct the weights. For Example: Err <- function(p) sum((weight*(y

Dear all, I'm trying to produce a log-likelihood profile for a GPD estimate with the ismev package. When following the examples, everything is working fine, i.e. I get a nice parabolic log-likelihood curve. When using my own data (intensity of hurricanes at landfall), the log-likelihood curve looks like a step function. Am I doing something wrong here? Is my data not suited for the GPD fit?

Hello, I am trying to call a FORTRAN subroutine within R and something really strange happens: I have a dll-library, that I load with dyn.load('mpbvv.dll'). I have checked the [Ordinal/Name Pointer] Table for the function within the library that I want to call - it is there (objdump - p mpbvv.dll). Then, I have written an R-wrapper to call the FORTRAN subroutine, which works fine. SInce

Dear List: I have a data frame prepared in the couting process style for including a binary time-dependent covariate. The first few rows look like this. PtNo Start End Status Imp 1 1 0 608.0 0 0 2 2 0 513.0 0 0 3 2 513 887.0 0 1 4 3 0 57.0 0 0 5 3 57 604.0 0 1 6 4 0 150.0 1 0 The outcome

Sorry, For me a vector is a matrix with mx1 dimmensions. But it is true that it is not the way I correctly must talk in R. Can you guide me in what I?m trying to do? I?m trying to find what I want in Excel using something like a parabolic function but dind?t get yet (then to try to replicate in R). 2017-06-02 7:01 GMT+02:00 Bert Gunter <bgunter.4567 at gmail.com>: > Bert Gunter >