search for: maxsteps

...the result returns: DLSODA- At current T (=R1), MXSTEP (=I1) steps taken on this call before reaching TOUT In above message, I = [1] 5000 In above message, R = [1] 0.0498949 Warning? 1: In lsoda(y[ii], times, func = bmod, parms = parms, bandup = nspec * : an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxsteps 2: In lsoda(y[ii], times, func = bmod, parms = parms, bandup = nspec * : Returning early. Results are accurate, as far as they go And the diagnostics(NPZ.out) shows: lsoda return code -------------------- return code (idid)...

...t(maxit = 100), : function cannot be evaluated at initial parameters ... Error in BBsolve(par = par, fn = Bo, s = s, outmat = outmat, method = c(1, : object 'ans.best' not found In addition: Warning messages: 1: In lsoda(y, times, func, parms, ...) : an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxsteps 2: In lsoda(y, times, func, parms, ...) : Returning early. Results are accurate, as far as they go For each iteration of the MLE my code gives me the parameters. If I use the parameters from the last iteration before the above er...

Hi I have the following formula (I hope it is clear - if no, I can try to do better the next time) h(x, a, b) = integral(0 to pi/2) ( ( integral(D/sin(alpha) to Inf) ( ( f(x, a, b) ) dx ) dalpha ) and I want to do an mle with it. I know how to use mle() and I also know about integrate(). My problem is to give the parameter values a and b to the

...imagedat Red Green Blue 0_0 5 7 8 1_0 5 5 18 2_0 7 8 49 3_0 22 8 76 4_0 54 10 67 5_0 50 9 28 6_0 18 10 15 7_0 9 7 6 8_0 2 5 7 ... I cluster using > cl <- kmeans(imagedat, nclust, maxsteps) > cl $cluster [1] 1 1 9 8 2 9 1 1 1 1 1 1 1 1 1 8 8 8 8 4 [25] 9 9 8 8 8 2 2 9 1 1 9 9 7 10 10 10 10 10 10 10 10 ... $centers Red Green Blue 1 9.940421 7.744428 11.11652 2 85.198120 18.363348 68.10173 3 109.247072 80...

Hi, I got two questions and would really appreciate any help from here. First, is the penalized maximum likelihood estimation(Firth Type Estimation) only fit for binary response (0,1 or TRUE, FALSE)? Can it be applied to multinomial logistic regression? If yes, what's the formula for LL and U(beta_i)? Can someone point me to the right reference? Second, when I used *logistf *on a dataset with

Dear All, I like to simulate a physiologically based pharmacokinetics model using R but am having a problem with the daspk routine. The same problem has been implemented in Berkeley madonna and Winbugs so that I know that it is working. However, with daspk it is not, and the numbers are everywhere! Please see the following and let me know if I am missing something... Thanks a lot in advance,