search for: epsmch

...PTIM, I've got the following message. --------------------------------------------------------------------- $par [1] 0.176166426835580 $value [1] 1322.17600079332 $counts function gradient 8 8 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" $hessian [,1] [1,] 46300.3853279247 --------------------------------------------------------------------- First, what does that message, "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH", mean? and I am wondering if the estimates are reliable. Any comments...

hi all i dont understand the error message that is produced by the optim function. can anybody help??? ie: [[1]]$message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" can anyone help? ########################################################################### SK.FIT(XDATA=a,XDATAname="a",PHI1=1,v=5,vlo=2,vhi=300,phi2lo=.01) [[1]] [[1]]$par [1] -0.01377906 0.83859445 0.34675230 300.00000000 [[1]]$value [1] 90.59185 [[1]]$counts functio...

...ot;L-BFGS-B", lower=rep(0, 3), upper=rep(1, 3)) D1.unbound $par c1 c2 c3 0.004387706 0.203562156 0.300825550 $value [1] 0.07811152 $counts function gradient 8 8 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" Any suggestion on how to fix the error "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"? [[alternative HTML version deleted]]

...e minimisation. It stops and gives a (-log(likelihood))=6104.45, with the messages: "there are 50 or more warnings" ( warnings() = "multi-arguments returns are deprecated" in a function used by the program) $convergence=0 $message= CONVERGENCE:REL_REDUCTION_OF_F <= FACTR*EPSMCH What does it mean? Is the convergence reached? What can it be concluded from these two steps? Thank you very much for your advices and help.

...,upper = rep(Inf, 2), hessian=TRUE, control=list(trace=1)) iter 0 value 3.011784 final value 2.802694 converged $par [1] 4.6597779 0.3860387 $value [1] 2.802694 $counts function gradient 17 17 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" WHich gives an estimate of stDev = 0.38 while the empirical stDev = 1.94 Is there anything wrong above in the code? Thanks in advance

...nf k t0 b sigma [1] 24.27206813 0.04679844 0.00100000 1.61760492 0.01000000 $value [1] -11.69524 $counts function gradient 143 143 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" $hessian [,1] [,2] [,3] [,4] [,5] [1,] 1.867150e+00 1.262763e+03 -7.857719 -5.153276e+01 -1.492850e-05 [2,] 1.262763e+03 8.608461e+05 -5512.469266 -3.562137e+04 9.693180e-05 [3,] -7.857719e+00 -5.512469e+03 41.670222 2.473167e+02 -...

...sing these as starting values in the next function call. The final call to optim() returns the following: $par [1] 0.2272361 0.8037642 26.8591998 3.0631280 0.2224566 $value [1] -46.13906 $counts function gradient 4 4 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" $hessian [,1] [,2] [,3] [,4] [,5] [1,] 1.267070e+17 1.012691e+17 1.348054e+15 625551.58724 9.359559e+07 [2,] 1.012691e+17 8.189877e+16 1.144248e+15 569562.44945 8.699072e+07 [3,] 1.348054e+15 1.144248e+15 2.457323e+05 3426.60293 -2.297009e+03 [4,] 6.255516e+05 5.695624e+05 3.426603e+03 99.068...

...= lambda) The output is: $par [1] 0.56350964 0.56350964 0.56350964 0.56350964 0.00000000 -0.29515957 [7] 0.00569937 0.32543297 0.18615880 $value [1] 0.2529198 $counts function gradient 31 31 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" Warning messages: 1: bounds can only be used with method L-BFGS-B in: optim(par, errorFunction, gr = NULL, method = "Nelder-Mead", 2: NAs introduced by coercion If I change my "error-function" to errorFunction <- function(localShifts,globalShift,fileName,experime...

...5 --- Val = 42.70603 ... Eval fn at 0.7425713 21.12820 0.001 --- Val = 64.99 Eval fn at 0.7425713 21.12920 0.002 --- Val = 60.20449 Eval fn at 0.7425713 21.12920 0.001 --- Val = 64.99 > o$val [1] 64.99 > o$convergence [1] 0 > o$message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" So optim thinks it has found an optimum (i.e. minimum). But my initial guess is better than optim's answer; and optim has visited many points which are better than its final answer. If I choose a different initial guess, like c(.7,10.3,1), optim reaches the answer I expect. What is goi...

...$par [1] 0.007369536 0.032623958 1.025064715 0.315420992 0.288083186 0.008728551 [7] 1.016895527 0.978822785 0.552299864 1.016390800 0.000100000 $value [1] -1697.267 $counts function gradient 50 50 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" ########### Here ``likeli.time.log'' and ``gradient.time.log'' are functions, ``temp.censor'' is a list with input variables for the two functions (data, fixed parameters, etc.), and the parameter space is of dimension 11. Note that value returned is clearly not th...

...440892e-16 [7,] -1.332268e-15 -2.220446e-15 > abs(D - t(s$u) %*% X %*% s$v) - Eps [,1] [,2] [1,] 3.108624e-15 -8.881784e-16 [2,] -1.165734e-15 -2.220446e-15 4.440892e-16 and 3.108624e-15 Eps was: > Eps [1] 2.220446e-15 This seems to be ok, I tried in Fortran EPSMCH=DLAMCH('E') WRITE(*,*)"eps:",EPSMCH and got eps: 2.22044605E-16 I compiled --whithout-dxml, so I'm not using any special numeric library. When I comment out the few lines for svd the tests from base-Ex.R complete successfully. All other tests succeed (other librar...

...> optRes <- optim(c(0,0), method="L-BFGS-B", fn=IRT.llZetaLambdaCorrNan, + gr=IRT.gradZL, + lower=c(-Inf, -Inf), upper=c(Inf, Inf), t=st, X=sx) > optRes $par [1] -0.6975157 0.7944972 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" Does anyone have an idea what this could be, and what I could try to avoid this error? I tried bounding the parameters, with lower=c(-10, -10) and upper=... but that made no difference. Thanks, Diederik Roijers Utrecht University MSc student. ------ PS: the other functions I am using are...

...0.77012134 4.34425988 0.14248754 0.30722383 0.12748400 [6] 0.48420116 0.00000000 0.02095689 0.00000000 0.61156935 [11] 0.77179635 0.00000000 $value: [1] 5106.049 $counts: function gradient 21 21 $convergence: [1] 0 $message: [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" I tried the 'bad' parameters in S-plus: > likelihood(par=rep(0,12),data=data03) #It is NA. [1] NA > likelihood(data=data03) #It is normal again. [1] 5834.421 Thank you very much for your time! ########################################### This message has been sca...

...0.49205978 Examination 0.26976286 Education 0.00050000 Catholic 0.06973751 Infant.Mortality 0.96007318 $value [1] 0.5008949 $counts function gradient 22 22 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" I have never seen that particular failure message from `optim` before, "NEW_X", and I cannot locate what it means. It is possible, if not probably, that it has to do with one of the functions internal to `factanal.fit.mle`, possibly the call to `eigen` as I presume that calls Rblas...

...800328223 1.448902455 2.280837645 [15] 1.594648898 0.011581676 0.040651369 0.000000000 0.000000000 0.000000000 0.002717246 $value [1] 14535187 $counts function gradient 54 54 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" $hessian [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.000000e+00 0.000000e+00 0.000000e+00 -0.0023283064 0.0000000000 2.328306e-03 [2,] 0.000000e+00 0.000000e+00 0.000000e+00 -0.0023283064 0.0000000000 2.328306e-03 [3,] 0.000000e+...

...+sum(log(par[2]*par[2]+(c-par[1])*(c-par[1])))+ }> optim(v,fn1,NULL,method="BFGS",X)$par[1] 0.3857697 -1.3984869 2.0925084 3.2370430 -1.6269258$value[1] -6.132011$countsfunction gradient 15 15 $convergence[1] 0$message[1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"There were 50 or more warnings (use warnings() to see the first 50)> #=====================================================================================> > c<-rn! orm(10)> v<-rnorm(10)> w<-rexp(10)> fn1<-function(v) {+ for(i ! in 1:10) {+ par[1]<-v[i]+ pa...

...okay (larger range of variation for x2 and x3) # but c1 is a bit out of whack (residual=3, we were unlucky...) $value [1] 12.87678 # keep in mind for future comparison $counts function gradient       9        9 $convergence [1] 0 $message [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH" > > # Another rainchech : does bound optimization reach the same objective > # when the "true" value lies in the bound region ? > > system.time(D2.unbound<-optim(par=c(c1=0.5, c2=0.5, c3=0.5), +                              fn=objfun, +                         ...