search for: ndeps

...aking the ... arguments that one user can pass in my function for fitting a model. More precisely, my objective function is (really) problematic to optimize using the "optim" function. Consequently, I'd like to add in the "control" argument of the latter function a "ndeps = rep(something, #par)" and/or "parscale = something" if the user has not specified it already. Do you know a way to deal with this point? In advance, thanks. Mathieu -- Institute of Mathematics Ecole Polytechnique F?d?rale de Lausanne STAT-IMA-FSB-EPFL, Station 8 CH-1015 Lausan...

hi! I would like to ask help how to generate numbers from g-and-h distribution. This distribution is like normal distribution but span more of the kurtosis and skewness plane. Has R any package on how to generate them? Any help will be greatly appreciated. Thank you so much! Form, Filame Uyaco --------------------------------- [[alternative HTML version deleted]]

...17, 13, 12, 20, 5, 9, 8, 5, 4, 8) ll <- function(ymax=15, xhalf=6) -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE)) (fit <- mle(ll)) plot(profile(fit), absVal=FALSE) everything works fine. Now run (fit <- mle(ll, method="BFGS", control=list(ndeps=c(1e-3, 1e-3)))) plot(profile(fit), absVal=FALSE) and you will get Error in interpSpline.default(obj[[i]]$par.vals[, i], obj[[i]]$z, na.action = na.omit) : only 0's may mix with negative subscripts This happens because optim gets the old ndeps but just one variable to optimize....

...at, suppose I think that f(x)-f(x1) approx equals f(x)-f(x2) where x1[1] = x[1] + 10 and x2[2] = x[2] + 1, and x, x1, and x2 are otherwise equal. Does this mean I should have parscale = c(10, 1) or parscale= (1/10, 1)? Since I'm not sure about parscale, I'm really not sure about 'ndeps' A vector of step sizes for the finite-difference approximation to the gradient, on 'par/parscale' scale. Defaults to '1e-3'. So, if I don't do any other rescaling, I might say ndeps=c(1e-2, 1e3) in the previous example (response to x[1] is 10 times flatt...

...Error in optim(c(10, 1), fn = LLNorm, method = "L-BFGS-B", lower = c(-Inf, : non-finite finite-difference value [2] The only solution/workaround I found is to do a two steps procedure: use optim() without hessian, then use optimHess, specifying the length of the numerical variations (arg ndeps) as smaller as the distance between the parameter and the bound, i.e.: op<-optim(c(10,1), fn=LLNorm, method="L-BFGS-B", lower=c(-Inf, 0.0000001), dat=rn2,hessian=FALSE) H<-optimHess(op$par, LLNorm, dat=rn2, control=list(ndeps=rep((op$par[2]-0)/1000,2))) While this solution "w...

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

...t least that's the way it works now -- one solution to the problem I'm about to state is to insist that they specify the parameters in the same order as they are given in the arguments of the objective function). However, there are other arguments (lower, upper, control$parscale, control$ndeps) that should all be in the same order as the objective function definition by the time they get to optim()). I can think of a few solutions: (1) make the user specify them all in the right order (ugh) (2) add all of them as explicit parameters to my function so that I can rearrange them appro...

Hi, When using method L-BFGS-B along with a parscale argument, should the lower and upper bounds provided be on the scaled or unscaled values? Thanks. Cheers, -- Seb

..., double factr, double pgtol, int *fncount, int *grcount, int maxit, char *msg, int trace, int nREPORT); What do I put for parameter ex (11th parameter)? I looked at optim.c codes at R sites and it's a structure that has bunch of objects such as SEXP R_fcall, SEXP R_gcall, SEXP R_env, double* ndeps, etc. I cannot figure out what it is about. How about fncount, rcount? R-ext.pdf or R help does not explain it in detail. Any comments would help. Thank you. [[alternative HTML version deleted]]

...the continuous variables and dumps back the initial values for the discrete variables. I presume that the numerical approximation to the gradients/hessian is thrown off by the `flat spots' or step-like-nature of the objective function with respect to the discrete variables. I have played with ndeps, parscale etc. but nothing really works. I realize this is a mixed combinatorial optimization/continuous optimization problem and ideally would love pointers to any related literature or ideally an R package that implements such a beast. However, if anyone has attempted to use optimization routine...

Dear all I've got the dataset data:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491; ?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334 I know from other testing that it should be possible to fit the data with the exponentialdistribution. I tried to get parameterestimates for the exponentialdistribution with R, but as the values of the parameter

All: I am getting an error code from the optimization function. The code is Error in optim(p,fun.LLike, lower=low, upper = up, method = "L-BFGS-B", : non-finite finite-difference value [0] If I add a trace=6 option to my control list the last message before this error is: At X0, 0 variables are exactly at the bounds Any ideas on where I should start would be

I am trying to translate a given BMDP 8V problem treatment to something approximately equivalent in nlme package (or base R, if sufficient). (Of course, it is not my goal to end there. I want to get access to the advanced flexibility of a more modern treatment.) Here is the problem statement in BMDP control language: /input title='Augenbewegungen'. variables=4.

[Sorry, In case this is repeating a message already sent to the list]. I am trying to move a project to R (base or nlme), for which I have a partial solution in BMDP 8V. Here is the 8V control language: /input title='Augenbewegungen'. variables=4. file='latm.dat'. format='11x,f7.0,f7.0,f12.4,f10.0'. /variables names= llat,rlat,vg,diff. /design

Dear all, I have a function MYFUN which depends on 3 positive parameters TETA[1], TETA[2], and TETA[3]; x belongs to [0,1]. I integrate the function over [0,0.1], [0.1,0.2] and [0.2,0.3] and want to choose the three parameters so that these three integrals are as close to, resp., 2300, 4600 and 5800 as possible. As I have three equations with three unknowns, I expect the exact fit, i.e., the SS

...nsigned long size_of_struct; /* == sizeof(module) */ - struct module *next; - const char *name; - unsigned long size; - - union - { - atomic_t usecount; - long pad; - } uc; /* Needs to keep its size - so says rth */ - - unsigned long flags; /* AUTOCLEAN et al */ - - unsigned nsyms; - unsigned ndeps; - - struct module_symbol *syms; - struct module_ref *deps; - struct module_ref *refs; - int (*init)(void); - void (*cleanup)(void); - const struct exception_table_entry *ex_table_start; - const struct exception_table_entry *ex_table_end; -#ifdef __alpha__ - unsigned long gp; -#endif - /* Members p...

...nsigned long size_of_struct; /* == sizeof(module) */ - struct module *next; - const char *name; - unsigned long size; - - union - { - atomic_t usecount; - long pad; - } uc; /* Needs to keep its size - so says rth */ - - unsigned long flags; /* AUTOCLEAN et al */ - - unsigned nsyms; - unsigned ndeps; - - struct module_symbol *syms; - struct module_ref *deps; - struct module_ref *refs; - int (*init)(void); - void (*cleanup)(void); - const struct exception_table_entry *ex_table_start; - const struct exception_table_entry *ex_table_end; -#ifdef __alpha__ - unsigned long gp; -#endif - /* Members p...

...as() methods and x at .Data should work better. o hist() and pretty() accept (and ignore) infinite values. (PR#7220) o It is no longer possible to call gzcon() more than once on a connection. o t.test() now detects nearly-constant input data. (PR#7225) o mle() had problems if ndeps or parscale was supplied in the control arguments for optim(). Also, the profiler is now more careful to reevaluate modified mle() calls in its parent environment. o Fix to rendering of accented superscripts and subscripts e.g., expression((b[dot(a)])). (Patch from Uwe Ligges.) o at...

...as() methods and x at .Data should work better. o hist() and pretty() accept (and ignore) infinite values. (PR#7220) o It is no longer possible to call gzcon() more than once on a connection. o t.test() now detects nearly-constant input data. (PR#7225) o mle() had problems if ndeps or parscale was supplied in the control arguments for optim(). Also, the profiler is now more careful to reevaluate modified mle() calls in its parent environment. o Fix to rendering of accented superscripts and subscripts e.g., expression((b[dot(a)])). (Patch from Uwe Ligges.) o at...