similar to: solve equations

Hello, I have a system of five equations to solve with five 'unknows'(V, W, X, Y and Z) and constraints. The equations are: 0.007= 2VZ 0.03= W(Y+Z) 0.034= X(y+Z) 0.013 = (X+W)Y +(X-W)Z X = W+V Constraints: 0<V<W<X 0<Y<Z<1 Does anyone know a R-package to solve this system? Thanks, E-mail: sebastien.puechmaille at ucd.ie

Dear all, I just know how to solve an eaquation by using optim() or nlm(). But, now, I have three nonlinear equations, how could we use optim() or nlm() to solve  a system of nonlinear equations in R?  Thank you so much. Sincerely, Joe ___________________________________________________ 您的生活即時通 － 溝通、娛樂、生活、工作一次搞定！ [[alternative HTML version deleted]]

Dear R-list members, I've had a hard time trying to solve a non-linear system (nls) of equations which structure for the equation i, i=1,...,4, is as follows: f_i(d_1,d_2,d_3,d_4)-k_i(l,m,s) = 0 (1) In the expression above, both f_i and k_i are known functions and l, m and s are known constants. I would like to estimate the vector d=(d_1,d_2,d_3,d_4) which is solution

Hi, I am trying to find a simple way to numerically solve a system of two equations equal to zero with two unknowns (x_loc and y_loc). Here is a mock data set and below it, the equations I need to solve. theta<-c(180,135,90)/(2*pi) x<-c(0,0,15) y<-c(20,0,0) 0 = -sum((y_loc-y)*(sin(theta)*(x_loc-x)-cos(theta)*(y_loc-y))/(((x_loc-x)^2+(y_loc-y)^2)^0.5)^3) 0 =

Didn't get any useful response to the following question. Trying again. -------------------------------------------------------------------------------- I can't seem to get computationally stable estimates for the following system: Y=a+bX+cX^2+dX^3, where X~N(0,1). (Y is expressed as a linear combination of the first three powers of a standard normal variable.) Assuming that E(Y)=0 and

Dear: I have a paired equation below. Can I solve (x,y) using R. Thanks! Xin A=327.727 B=9517.336 p=0.114^10 (1-p)*y*(1-x)/x/(1-x^y)=A A(1+(1-x)*(1+y)/x-A))=B [[alternative HTML version deleted]]

To R-helpers, R offers the polyroot function for solving mentioned equations iteratively. However, Dr Math and Mathworld (and other places) show in detail how to solve mentioned equations non-iteratively. Do implementations for R that are non-iterative and that solve mentioned equations exists? Regards, Mads Jeppe

I have a dataset X Y1 1200 1.375 4000 0.464 1333.33 0.148 444.44 0.047 148.148 0.014 49.383 0.005 16.461 0.004 I have to find a curve fit for the above dataset based on a 4-parameter logistic equation viz. Y1 = d + ((a-d)/(1+(X/cc)^b)), where X and Y1 are the values above. I need to know how to solve the above equation for values a, b, c, d. -- View this message in context:

Hi, I am trying to solve this equation prob = exp(-3.33 + 0.0102*x)/(1+exp(-3.33 + 0.0102*x)). I want to write a function where I call the function and enter the 'prob' value and the output should be the 'x'. Im not sure how to write this. I have a basic structure but im not sure if its correct. calc <- function(prob){ prob <- exp(-3.33+0.0102*x)/(1+exp(-3.33 + 0.0102*x))

Any recommendations on how I can get mathematical equations into my resulting html? (Besides using x^2^ type markup.) I figure I could parse out equation text from my main text before RedCloth ever sees it... and then maybe create png''s somehow from it, putting the links to them back into the main text before handing it over to RC. But I don''t know of a util that will generate

Dear all, I would like to estimate a system of regression equations of the following form: y1 = a1 + b1 x1 + b2x2 + e1 y2 = a2 + c1 y1 + c2 x2 + c3 x3 + e2 Specifically the dependent variable in Equation 1 appears as an independent variable in Equation 2. Additionally some independent variables that appear in Equation 1 are also included in Equation 2. I assume that I cannot estimate these two

Hi Frances, I have not touched the system.fit package for quite some time, but to solve your problem the following two pointers might be helpful: 1) Recast your model in the revised form, i.e., include your identity directly into your reaction functions, if possible. 2) For solving your model, you can employ the Gau?-Seidel method (see https://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method).

Who was speaking about non-linear models in the first place??? The Klein-Model(s) and pretty much all simultaneous equation models encountered in macro-econometrics are linear and/or can contain linear approximations to non-linear relationships, e.g., production functions of the Cobb-Douglas type. Best, Bernhard -----Urspr?ngliche Nachricht----- Von: Berend Hasselman [mailto:bhh at xs4all.nl]

> On 13 Jul 2017, at 12:55, Pfaff, Bernhard Dr. <Bernhard_Pfaff at fra.invesco.com> wrote: > > Who was speaking about non-linear models in the first place??? > The Klein-Model(s) and pretty much all simultaneous equation models encountered in macro-econometrics are linear That's really not true. Klein model is linear but Oseibonsu did not say that explicitly. "Klein

Frances, I would not advise Gauss-Seidel for non linear models. Can be quite tricky, slow and diverge. You can write your model as a non linear system of equations and use one of the nonlinear solvers. See the section "Root Finding" in the task view NumericalMathematics suggesting three packages (BB, nleqslv and ktsolve). These package are certainly able to handle medium sized models.

Hello everyone, I am trying to solve 2D differential equations using finite difference scheme in R. I have been able to work with the equations with only one spatial dimensions but I want to extend it to the two dimensional problem. For example i can simulate one dimensional diffusion using a code like the following. But I want to write a similar code for,say, a two dimensional diffusion

Hello there Can anybody please tell me if there is any package in R to solve the following 4 nonlinear equations with 4 unknowns: alpha*exp(20/sigma)+ beta*exp(21/tau) = 2 alpha*exp(22/sigma)+ beta*exp(9/tau) = 4 alpha*exp(10/sigma)+ beta*exp(30/tau) = 6 alpha*exp(40/sigma)+ beta*exp(39/tau) = 5 where alpha = exp(lambda/sigma) beta= exp(delta/tau) I need to estimate lambda, sigma, delta, tau

I'm currently working on writing up some documentation for some of my code, but am having the darndest time coding in equations. For example, the equation in the following: \details{ Calculated the R Squared for observed endogenous variables in a structural equation model, as well as several other useful summary statistics about the error in thoe variables. R Squared values are

This below is not solvable with uniroot to find "a": fn=function(a){ b=(0.7/a)-a (1/(a+b+1))-0.0025 } uniroot(fn,c(-500,500)) gives "Error in uniroot(fn, c(-500, 500)) : f() values at end points not of opposite sign" I read R-help posts and someone wrote a function: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/92407.html but it is not very precise. Is there any

Dear R-gurus, being very new to R, (as well as lazy and not too smart !) I have some problems (and get lost in the docs) trying to write something to find the 9 values (A1,B1,C1,A2,B2,C2....C3) which are solutions of a 12 equations system of the form : > x1-(A1/(A1+B1+C1)) = 0 > y1-(B1/(A1+B1+C1))= 0 > z1-(C1/(A1+B1+C1)) = 0 > 3 same equations with subscript 2 > 3 same