similar to: Solving equations in R

Hi there, I would like to solve a simple equation in R a^2 - a = 8.313 There is no real solution to this problem but I would like to get an approximate numerical solution. Can someone suggest how I can set this up? Thanks in advance, Diviya [[alternative HTML version deleted]]

Dear all, I am relatively new to R and have had some difficulty in understanding the user manual for a package that I have downloaded to evaluate non-linear simultaneous equations. The package is called systemfit. Does anyone have any experience of this package? What I am trying to do is solve a non linear simultaneous equations... Could anyone give me an example (please) of the code that

Hi there, I have a complex math equation which does not have a closed form solution. It is - y <- (p*exp(-a*d)*(1-exp((d-p)*(a-x[1]))))/((p-d)*(1-exp(-p*(a-x[1])))) For this equation, I have all the values except for x[1]. So I need to solve this problem numerically. Can anyone suggest an optimization package that I can use to estimate the value for x[1]? Thanks in advance, Diviya

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 =

I have two matrix s1 and s2, each of them is 1000*1. and I have two equations: digamma(p)-digamma(p+q)=s1, digamma(q)-digamma(p+q)=s2, and I want to sovle these two equations to get the value of x and y, which are also two 1000*1 matrices. I write a program like this: f <- function(x) { p<- x[1]; q <- x[2]; ((digamma(p)-digamma(p+q)-s1[2,]) )^2 +((digamma(q)-digamma(p+q)-s2[2,]) )^2

People, I'm researching some Bayesian statistic topics and in the midle of my study i found a very simple problem and i'm trying to find a simple package to solve this type of equations: Lets say that i need to compute beta values for the beta distribution and i now for example: E(teta)=a/(a+b) = 0,5 Var(teta)=ab/((a+b)^2(a+b+1))=0.05 So if i want to solve this to non-linear system to

Hi, I would appreciate if someone could help me on track with this problem. I want to compute some parameters from a system of equations given a number of sample observations. The system looks like this: sum_i( A+b_i>0 & A+b_i>C+d_i) = x sum_i( C+d_i>0 & C+d_i>A+b_i) = y sum_i( exp(E+f_i) * ( A+b_i>0 & A+b_i>C+d_i) = z A, C, E are free variables while the other

Hello there, I wish to solve the following nonlinear System of equations: + u1 - Vmax11*S1/(S1 + Km11 *(1 + S2/Km21)) - Vmax12*S1/( S1 + Km12 *(1+S2/Km22)) == 0 + u2 - Vmax22*S2/(S2 + Km22 *(1 + S1/Km12)) - Vmax21*S2/( S2 + Km21 *(1+S1/Km11)) == 0 + Vmax11*S1/(S1 + Km11 *(1 + S2/Km21)) + Vmax12*S1/( S1 + Km12 *(1+S2/Km22)) - d1*P1 == 0 + Vmax22*S2/(S2 + Km22 *(1 + S1/Km12)) + Vmax21*S2/( S2 +

Hello there, Can anyone point me to the code for logLik of an nls object? I found the code for logLik of an lm but could not find exactly what function is used for calculating the logLik of nls function? I am using the nls to fit the following model to data - Model 1: y ~ Ae^(-mx) + Be^(-nx) +c and want to understand what is the likelihood function used by nls. Presumably it is using -

Hello there, I have a question regarding bar plots. I am trying to plot the data from the following matrix as a barplot - # input data mdat <- matrix(c(0.1,0.9,0.9,0.1,0.5,0.5,0.45,1-0.45,0.6,0.4,0.8,0.2), nrow = 6, ncol=2, byrow=TRUE, + dimnames = list(c("Mon", "Mon", "Tues", "Tues", "Thurs", "Friday"), +

Hello there, I have a matrix with some data and I want to split this matrix based on the values in one column. Is there a quick way of doing this? I have looked at cut but I am not sure how to exactly use it? for example: I would like to split the matrix "a" based on the spending such that the data is binned groups [0..99],[100..199]...and so on. a <- data.frame(patient=1:7,

Hello there, I am new to using regression in R. I wanted to solve a simple regression problem where I have 2 equations and 2 unknowns. So lets say - y1 = alpha1*A + beta1*B y2 = alpha2*A + beta2*B y1 <- runif(100000, 0,1) y2 <- runif(100000,0,1) alpha1 <- 0.6 alpha2 <- 0.75 beta1 <- 1-alpha1 beta2 <- 1-apha2 I now want this equation to estimate the values of A and B. Both A

Hi there, I was wondering if there is any R package that one can use for plotting that has more legend symbols - the standard pch has 18 symbols but I need ~30 for my application- and just using different colors is not an option. Thank you in advance, Diviya [[alternative HTML version deleted]]

Hello there, I recently wrote some code to perform pairwise correlations between all samples in a large dataset. So we are talking about performing pairwise correlations between 400K vectors. Since R has a very rich library of functions, it was very easy to code this in R. However, R was probably not the best choice as it is super slow for this large job. So my plan is to recode it in C. I was

Hi there, I have following equations to be solved for a and b: a/(a+b) = x1 ab/((a+b)^2 (a+b+1)) = x2 Is there any direct function available to solve them without disentangling them manually? Thanks for your help.

Hi, Is this method broken in R? I am using it to find roots of the following function: f(x) = 2.5*exp(-0.5*(2*0.045 - x)) + 2.5*exp(-0.045) + 2.5*exp(-1.5*x) - 100 It is giving an answer of -38.4762403 which is not even close (f(x) = 2.903809e+25 for x=-38.4762403). The answer should be around 0.01-0.1. This function should converge.. Even for a simple function like f(x) = exp(-x) * x, it gives

Hello there, I am using NLS for fitting a complex model to some data to estimate a couple of the missing parameters. The model is - y ~ (C+((log(1-r))*exp(-A*d)*(-1+r+exp(d*(A-B)))/(r*(-A*d+d*B+log(1-r))))) where A, B and C are unknown. In order to test the model, I generate data by setting values for all parameters and add some noise (C). A <- 20 B <- 500 r <- 0.6881 d <-

Dear: I am having trouble solving simultaneous nonlinear equations by R. I have been using BBsolve (BB) to do so. Though the function is very strong, still the program doesn't converge. I have tried all (according to my small knowledge) the options described in the help file. Now I am trying to find something else than BBsolve for solving simultaneous nonlinear equations by R. Any idea or

Hello there, I am trying to fit an exponential fit using Least squares to some data. #data x <- c(1 ,10, 20, 30, 40, 50, 60, 70, 80, 90, 100) y <- c(0.033823, 0.014779, 0.004698, 0.001584, -0.002017, -0.003436, -0.000006, -0.004626, -0.004626, -0.004626, -0.004626) sub <- data.frame(x,y) #If model is y = a*exp(-x) + b then fit <- nls(y ~ a*exp(-x) + b, data = sub, start

I'm solving 4 complex equations simultaneously. Code is below. The code returns only zero's for the solution though there should also be a non-zero result. I'm pretty confident that the equations are correct because they are straight from a published paper and I checked them pretty thoroughly. The parameter values I used are from the published paper as well. Any suggestions for how