search for: biexponential

Hi, I want to do a biexponential Fit, i.e. y ~ A1*exp(k1*x) + A2*exp(k2*x) Is this possible? I tried nls() but it stopped with several (different) errors. I'm using y and x as simple vectors and the formula for nls() exactly as mentioned above. Thanks a lot! Jonas

Hello all! I have a problem to use a biexponential regression model: I use this code: n3<-nls(proc~SSbiexp(cls,a,b,c,d),data=bline) and this is the error message: Error in nls(y ~ cbind(exp(-exp(lrc1) * x), exp(-exp(lrc2) * x)), data = xy, : singular gradient My data is like this: structure(list(proc = c(387.177462830167, 508.090511433077,...

...#39;t be angry ...). The authors of nlme give an example [1] on page 28: CO2.fit2 <- update(CO2.fit1,fixed=list(A + C ~ Treatment * Type, lB ~ 1), start=c(32.55,0, 0, 0, 41.56, 0, 0, 0, -4.6)) (The code that has been run before is given below) When running the example as well as own code (biexponential model depending on two crossed factors) I receive the following error message: Error: comparison (2) only possible for vector types No output is produced. I tried to convert the relevant factors into numeric vectors without succes. The error likewise occurs in additive and interactive models....

Dear R-Help list, I have a nonlinear least squares problem, which involves a changepoint; at the beginning, the outcome y is constant, and after a delay, t0, y follows a biexponential decay. I log-transform the data, to stabilize the error variance. At time t < t0, my model is log(y_i)=log(exp(a0)+exp(b0)) at time t >= t0, the model is log(y_i)=log(exp(a0-a1*(t_i - t0))+exp(b0=b1*(t_i - t0))) I thought that I would have identifiability issues, but this model seems to w...

Hi there, I'm quite new to R (and statistics), and I like it (both)! But I'm a bit lost in all these packages, so could someone please give me a hint whether there exists a package for fitting exponential curves (of the type t --> \sum_i a_i \exp( - b_i t)) on a noisy signal? In fact monoexponential decay + polynomial growth is what I'd like to try. Thanks in advance,

Just to add a note to the question asked by Bill Simpson.. I am confronted with a similar problem, but the model I try to fit on my data is nonlinear. I have a four-parameter, biexponential model describing the response times of a group of subjects performing a set of task. What I want to evaluate is whether the parameter values differ between (groups of) tasks. myfunc <-formula(x ~ a*exp(-b*age) + (c*exp(d*age)) ) both x (representing response time) and age are variables in t...

All, I have been able to successfully use the optim( ) function with "L-BFGS-B" to find reasonable parameters for a one-compartment open pharmacokinetic model. My loss function in this case was squared error, and I made no assumptions about the distribution of the plasma values. The model appeared to fit pretty well. Out of curiosity, I decided to try to use nlminb( ) applied to a