search for: unconstrained

Congrats to the team for 0.10.0. It sounds really good in the tests I've done so far. I'm really looking forward to the 1.0 release. One question though: could you explain briefly the difference between VBR and unconstrained VBR? And, in my case, the $64K question: is there any situation where CELT could produce more data than specified by nbCompressedBytes when encoding? Thanks much, John RIdges

...0_CS25 + pcnmEYR_TH_2_CS25 + mydata$Site_No + mydata$Landscape + Condition(pcnmCS_NULL + mydata$LAT.x + mydata$LONG.x), na.action = "na.omit") Inertia Proportion Rank Total 1.8110 1.0000 Conditional 0.8681 0.4793 32 Constrained 0.0000 0.0000 0 Unconstrained 0.9429 0.5207 29 Inertia is variance Some constraints were aliased because they were collinear (redundant) Eigenvalues for unconstrained axes: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 0.16008 0.14733 0.12183 0.09054 0.07380 0.06971 0.05578 0.04215 (Showed only 8 of all...

Apologies if this is this too obscure for R-help. In package splines, ns(x,,knots,intercept=TRUE) produces an n by K+2 matrix N, the values of K+2 basis functions for the natural splines with K (internal) knots, evaluated at x. It does this by first generating an n by K+4 matrix B of unconstrained splines, then postmultiplying B by H, a K+4 by K+2 representation of the nullspace of C (2 by K+4), which contains the 2nd derivatives of the unconstrained splines evaluated at the boundary knots. E.g. see Hastie and Tibshirani, Generalized Additive Models, exercise 2.5, p36. The QR decomposition...

...memory, and the user can provide box constraints. 1) Why would you ever want to use BFGS, if L-BFGS-B does the same thing but use less memory? 2) If i'm optimizing with respect to a variable x that must be non-negative, a common approach is to do a change of variables x = exp(y), and optimize unconstrained with respect to y. Is optimization using box constraints on x, likely to produce as good a result as unconstrained optimization on y? - Brian. [[alternative HTML version deleted]]

Using the maptools function "map2poly" I have created a map/polylist object - a map of the Great Lakes. My goal is to passively plot additional data on this map. Unfortunately I am not able to change (distort) the relative scale of the axes for the map, and so the sample points do not line up correctly on the map. In the code below, notice that I have set the same axis ranges on each

I have used Mike Powell's optimization routine (VA05AD) from the Harwell Subroutine Library (HSL) for more than 20 years. It is no exaggeration to say that it has helped make my career (thanks Mike). I recently learned that I am not alone in this respect - apparently it still has a loyal following in all sorts of fields! It is an exceedingly fine piece of software - fast, reliable and easy to

R-experts: I would like to explore the partial proportional odds models of Peterson and Harrell (Applied Statistics 1990, 39(2): 205-217) for a dataset that I am analyzing. I have not been able to locate a R package that implements these models. Is anyone aware of existing R functions, packages, etc... that might be used to implement the partial proportional odds models? Brant Inman

...function > BTW, nb.80 is a negative binomial glm model created using the MASS library with the call at the bottom of the message In the hopes of trying to figure this out I tried the following workaround but it did not work either since AIC = (Deviance(Mu) + 2C) / N where: Mu is deviance of unconstrained model C is number of coefficients N is sample size and BIC = Deviance(Mu) - (df1 *ln(N)) where: Mu is deviance of unconstrained model df1 is the sample size minus the number of coeffcients N is the sample size thus by substitution BIC = (AIC*N - 2C) - (df1 * ln(N)) In R the code I tried was: AIC...

...d gives a warning that L-BFGS-B should be used, and from the output uses this. This is a bit of an edge case. My own preference would be for optim() to simply fail if bounds of any type are specified without L-BFGS-B as the method. I believe that gives clarity, even though infinite bounds imply an unconstrained problem. The behaviour where a scalar infinite bound is treated as unconstrained but a vector is not is inconsistent, however, and I think that at some point should be fixed. Possibly the easiest way is to treat infinite bounds specified as a vector the same as those specified as a scalar. That is...

I'm looking for an example of a simple R script that impliments a contrained nonlinear function using nlm or optim. I'm not exactly sure how to impliment the constraints within the objective function that is passed to nlm/optim. obj.func <- function( p ) { x(p) <- unconstrained obj function value if( constraint1 > something ) { obj.func <- x(p) } else { obj.func <- some super huge number } } p <- c(0.1,2.4, 5) nlm( obj.func, p, and a bunch of other arguments ) Any suggestions would be very helpful... Jeff. -- Jeff D. Hamann Forest...

...+ pcnmEYR_TH_2_CS25 + mydata$Site_No + mydata$Landscape + Condition(pcnmCS_NULL + mydata$LAT.x + mydata$LONG.x), na.action = "na.omit") Inertia Proportion Rank Total 1.8110 1.0000 Conditional 0.8681 0.4793 32 Constrained 0.0000 0.0000 0 Unconstrained 0.9429 0.5207 29 Inertia is variance Some constraints were aliased because they were collinear (redundant) Eigenvalues for unconstrained axes: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 0.16008 0.14733 0.12183 0.09054 0.07380 0.06971 0.05578 0.04215 (Showed only 8 of...

....results<-capscale(as.dist(qiime.data$distmat)~ N+rem+N*rem+Condition(dateFac), factor.frame) capscale.Nrem.results Inertia Proportion Rank Total 1.454538 Real Total 1.459802 1.000000 Conditional 0.117117 0.080228 1 Constrained 0.386228 0.264576 3 Unconstrained 0.956457 0.655197 22 Imaginary -0.005264 2 Inertia is squared Unknown distance Eigenvalues for constrained axes: CAP1 CAP2 CAP3 0.29869 0.05395 0.03359 Eigenvalues for unconstrained axes: MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 0.27719 0.137...

...in.data ~ Ph+TotalN+Organicmatter+Ca+K+Na+P+Cu+Mn, data=env.data) > strain.cca Call: cca(formula = strain.data ~ Ph + TotalN + Organicmatter + Ca + K + Na + P + Cu + Mn, data = env.data) Inertia Proportion Rank Total 5 1 Constrained 5 1 5 Unconstrained 0 0 0 Inertia is mean squared contingency coefficient Some constraints were aliased because they were collinear (redundant) Eigenvalues for constrained axes: CCA1 CCA2 CCA3 CCA4 CCA5 1 1 1 1 1 > plot(strain.cca) > summary (strain.cca) Call: cca(formula...

...based on the simplex method. The provided algorithms are direct search algorithms, i.e. algorithms which do not use the derivative of the cost function. They are based on the update of a simplex. The following algorithms are available: the fixed size simplex method of Spendley, Hext and Himsworth (unconstrained optimization with a fixed sized simplex), the variable size simplex method of Nelder and Mead (unconstrained optimization with a variable sized simplex), Box's complex method (constrained optimization with a variable sized simplex). This package includes an R-port of the fminsearch function (av...

...based on the simplex method. The provided algorithms are direct search algorithms, i.e. algorithms which do not use the derivative of the cost function. They are based on the update of a simplex. The following algorithms are available: the fixed size simplex method of Spendley, Hext and Himsworth (unconstrained optimization with a fixed sized simplex), the variable size simplex method of Nelder and Mead (unconstrained optimization with a variable sized simplex), Box's complex method (constrained optimization with a variable sized simplex). This package includes an R-port of the fminsearch function (av...

...lways thought these out-of-range instructions did produce an "undef" rather than allowing fully-general undefined behaviour (otherwise we couldn't speculate them, for a start). If so, I think the code ought to be valid: %1 is *some* i16 bitpattern, which means %2 cannot be completely unconstrained and should never be equal to %0. Cheers. Tim.

...as the sign of the first difference is correct? I am assuming that if I generate data for this purpose the dimension should be the same so the dimension of the matrices from finite differencing are the same? A couple of (perhaps quite basic) specific questions to the example code: ## Preliminary unconstrained gam fit... G <- gam(y~s(x)+s(z)+s(v,k=20),fit=FALSE) So first create G which is going to be the input to pcls() to fit the constrained model. Then fit the unconstrained version: b <- gam(G=G) (skipping this part of the example that calculates finite differences contained in Xx and Xz where...

I wrote a simple log likelihood (for the ordinary least squares (OLS) model), in two ways. The first works out the likelihood. The second merely calls the first, but after transforming the variance parameter, so as to allow an unconstrained maximisation. So the second suffers a slight cost for one exp() and then it pays the cost of calling the first. I did performance measurement. One would expect the second version to be slightly (very slightly) slower than the first. But I am finding this is not the case! The second version is slig...

...c. for monotonic spline.... sm<-smoothCon(s(x,k=10,bs="cr"),dat,knots=NULL)[[1]] F<-mono.con(sm$xp); # get constraints G<-list(X=sm$X,C=matrix(0,0,0),sp=f.ug$sp,p=sm$xp,y=y,w=y*0+1) G$Ain<-F$A;G$bin<-F$b;G$S<-sm$S;G$off<-0 p<-pcls(G); # fit spline (using s.p. from unconstrained fit) fv<-Predict.matrix(sm,data.frame(x=x))%*%p lines(x,fv,col=2) lines(x,f,col="blue")            Thanks a lot!!             Victor [[alternative HTML version deleted]]

...d Producer), similar on the example above. I found some examples to optimize equations in R and some tips, but I not be able to define the sintaxe to use with that functions. Among the functions that could be used to resolve the problem presented, I found the function optim() that it is used for unconstrained optimization and the nlm() which is used for solving nonlinear unconstrained minimization problems. May I wrong, but the nlm() function would be appropriate to solve this problem, is it right? Can I get a pointer to solve this problem using the nlm() function or where could I get some tips/exampl...