search for: typsize

Dear R list members, I have a question about the proper use of the typsize and fscale arguments to nlm. I use nlm in my sem package to fit general structural-equation models, which entails maximizing a multinormal likelihood with respect to parameters that represent regression coefficients and covariances of variables. The magnitudes of these parameters can be very d...

...9;s a hack in R code that works for me (basically copying the R code of nlm() but defining a temporary function that passes the optional arguments to the objective function), but I presume this would be cleaner and faster if implemented at a lower level ... nlm2 <- function(f, p, hessian=FALSE, typsize=rep(1,length(p)), fscale=1, print.level=0, ndigit=12, gradtol=1e-6, stepmax=max(1000 * sqrt(sum((p/typsize)^2)), 1000), steptol=1e-6, iterlim=100, check.analyticals=TRUE, ...) { tmpf <- function(x) { f(x,...) } nlm(tmpf,p,hessian=hessian,...

...ps with the following error message: > *** caught segfault *** > address (nil), cause 'unknown' > > Traceback: > 1: nlm(if (analytic.gradient) objective.2 else objective.1, > start, hessian = TRUE, iterlim = maxiter, print.level = if > (debug) 2 else 0, typsize = typsize, . > ..) > 2: sem.default(ram = ram, S = S, N = N, param.names = pars, > var.names = vars, fixed.x = fixed.x, debug = debug, ...) > 3: sem(ram = ram, S = S, N = N, param.names = pars, var.names = > vars, fixed.x = fixed.x, debug = debug, ...) > 4: sem.mod(mod...

I am trying to view the source code of the function nlm in the stats package of R 2.4.1. I downloaded the source from CRAN and opened nlm.R, and it calls a .Internal function: .Internal(nlm(function(x) f(x, ...), p, hessian, typsize, fscale, msg, ndigit, gradtol, stepmax, steptol, iterlim)) This is the same thing I saw when entering the function name at the R command prompt. Where will I find the actual code? Thanks.

...use "gamma" mixture distribution to estimate the parameters. Here is what I am using for the "mix" function. Test<-mix(x, mixparam(mu=c(1,125),sigma=c(1,11.18)),"gamma") However, one error message shows up as: "Error in nlm(function(x) f(x, ...), p, hessian, typsize, fscale, msg, : missing value in parameter" Does anybody know what that means and how to fix it to get the estimation? Your help will be much appreciated. Yun ============================================== Yun Wang Research Assistant Department of Family Medicine and Public Health Science...

...ive.1, start, : type 31 is unimplemented in 'type2char' 3) *** caught segfault *** address 0xc019c87b, cause 'memory not mapped' Traceback: 1: nlm(if (analytic.gradient) objective.2 else objective.1, start, hessian = TRUE, iterlim = maxiter, print.level = if (debug) 2 else 0, typsize = typsize, ...) 2: sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, fixed.x = fixed.x, debug = debug, ...) 3: sem(ram = ram, S = S, N = N, param.names = pars, var.names = vars, fixed.x = fixed.x, debug = debug, ...) 4: sem.mod(ram = ice.ram, S = ice.S, N = ice.N)...

Hi Everyone, Is there a simple way to force nlm to take larger initial steps? Setting print.level = 2 allows me to inspect the step size at each iteration, but I appear not to have made any appreciable impact on it by changing values of typsize, fscale, steptol or stepmax. The steps repeatedly come out tiny, 1e-9 typically, and the algorithm is terminating not because the gradient is zero (it is not, according to the numerical values) but because the function value is not changing between iterations. Am I missing something obvious? Man...

...n 'type2char' > 3) =A0*** caught segfault *** > address 0xc019c87b, cause 'memory not mapped' > > Traceback: > =A01: nlm(if (analytic.gradient) objective.2 else objective.1, start, > hessian =3D TRUE, iterlim =3D maxiter, print.level =3D if (debug) 2 else = 0, > typsize =3D typsize, ...) > =A02: sem.default(ram =3D ram, S =3D S, N =3D N, param.names =3D pars, va= r.names =3D > vars, =A0 =A0 fixed.x =3D fixed.x, debug =3D debug, ...) > =A03: sem(ram =3D ram, S =3D S, N =3D N, param.names =3D pars, var.names = =3D vars, > fixed.x =3D fixed.x, debug =3D d...

...t; 3) =A0*** caught segfault *** >> address 0xc019c87b, cause 'memory not mapped' >> >> Traceback: >> =A01: nlm(if (analytic.gradient) objective.2 else objective.1, start, >> hessian =3D TRUE, iterlim =3D maxiter, print.level =3D if (debug) 2 else= 0, >> typsize =3D typsize, ...) >> =A02: sem.default(ram =3D ram, S =3D S, N =3D N, param.names =3D pars, v= ar.names =3D >> vars, =A0 =A0 fixed.x =3D fixed.x, debug =3D debug, ...) >> =A03: sem(ram =3D ram, S =3D S, N =3D N, param.names =3D pars, var.names= =3D vars, >> fixed.x =3D fixe...

Dear R list members, Since today seems to be the day for optimization questions, I have one that has been puzzling me: I've been doing some work on sem, my structural-equation modelling package. The models that the sem function in this package fits are essentially parametrizations of the multinormal distribution. The function uses optim and nlm sequentially to maximize a multinormal