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Dear Sir, I am a new user of R and I am doing a tast, which is: find the maximum likelihood estimate of the parameter of Gaussian distribution for generated 100 numbers by using >x=rnorm(100, mean=3, sd=1). I tried to use following Maximum Likelihood function >fn<-function(x) (-50*log((sd(x))^2))-50*log(sqrt(2*pi))-(1/2*((mean(x))^2))*(sum((x-(mean(x))^2)), but it did not work. I am

Dear WizaRds, A run of nls produces the following concise summary: > summary(cs.wt) Formula: 0 ~ wt.MM(conc, time, A1, a1, A2, a2) Parameters: Estimate Std. Error t value Pr(>|t|) A1 4.814e+02 2.240e+01 21.495 0.0296 * a1 7.401e-01 7.435e-02 9.956 0.0637 . A2 1.613e+02 1.738e+01 9.280 0.0683 . a2 1.770e-02 7.324e-03 2.417 0.2497

Dear Colleagues, Our group is also working on implementing the use of R for pharmacokinetic compartmental analysis. Perhaps I have missed something, but > fit <- nls(noisy ~ lsoda(xstart, time, one.compartment.model, c(K1=0.5, k2=0.5)), + data=C1.lsoda, + start=list(K1=0.3, k2=0.7), + trace=T + ) Error in eval(as.name(varName), data) : Object

Ladies and Gentlemen, In MATLAB, I can write: for J=1:M Y(J+1)=Y(J)+ h * feval(f,T(J),Y(J)); ... In R, I can write above as: for (J in 2:M) { y = y + h * f(t,y) ... } ---- In MATLAB, I can write: for J=1:M k1 = feval(f,T(J),Y(J)); k2 = feval(f,T(J+1),Y(J)+ h * k1 How do I write k2 in R? k1 = f(t,y) k2 = ? Thanks, /oal

Dear WizaRds, Given the experimental data, csdata<-data.frame( time=c(0,1,3,9,20), conc=c(638.697,395.69,199.00,141.58,112.16) ) weighted nls is applied, wt.MM<- function(resp, time,A1,a1,A2,a2) { pred <- A1*exp(-a1*time)+A2*exp(-a2*time) (resp - pred) / sqrt(pred) } # cs.wt <- nls( ~ wt.MM(conc, time,A1,a1,A2,a2), data=csdata,

Dear Colleague: I am glad to hear from you. I was going to contact you and ask if you have notes on the steps involved on how you implemented LSODA. I will gladly take the project on. In addition to dassl, I want to implement ODESSA also. In other words, my project is a "suite" of ode solvers for R. The application of these solvers will be to the study of the distribution of