search for: multiresponse

Hello! I have dataset where one variable is 'multiresponse', like this: [1] "1 2" "1 2 3" "4" "1 4" "4 3" etc. 'responses' separated by space. observations in different 'rows' of data.frame. I can do strsplit(data$var,' ') and make a list, where multiple responses are elements...

I am looking for a way to do multiresponse parameter estimation for a problem that is quite similar in form to the alpha-pinene example in Bates and Watts (1988), p. 147, where the model is a set of ODEs. I am specifically interested in obtaining the parameter covariance matrix, as well. Which package(s) in R would I use to do such a...

I'm trying to fit a model to solve a biological problem. There are multiple independent variables, and also there are multiple responses. Each response is a function of all the independent variables, plus a set of parameters. All the responses depend on the same variables and parameters - just the form of the function changes to define each seperate response. Any ideas how I can fit

...symbol name "foo2" not in load table. foo.f90: Module foo contains Subroutine foo2(x) real(kind=8),intent(inout) :: x x = x + 2 end subroutine foo2 end module foo c:\mingw64\bin\gfortran --shared -Wall -pedantic -g -o foo.dll foo.f90 ff = "d:/Fortran folders/Fortran Multiresponse/foo.dll" x= dyn.load(ff) .Fortran('foo2',as.double(1)) Error in .Fortran("foo", as.double(1)) : Fortran symbol name "foo" not in load table Can someone point me in the direction of a solution? Regards David -- David K Stevens, P.E., Ph.D., Professor Civil a...

I am perfectly aware that this question is not an R question, at least not yet, but I have not succeeded in finding what I want in other ways, so ... What I am looking for are two algorithms, preferabley in Pascal, but other languages may do. For (a) systematic (complete) permutations for grouped data with unequal group sizes, and (b) random permutations for the same kind of data. I know

Dear Colleagues, Our group is also working on implementing the use of R for pharmacokinetic compartmental analysis. Perhaps I have missed something, but > fit <- nls(noisy ~ lsoda(xstart, time, one.compartment.model, c(K1=0.5, k2=0.5)), + data=C1.lsoda, + start=list(K1=0.3, k2=0.7), + trace=T + ) Error in eval(as.name(varName), data) : Object

Dear list, this is not a particular R question but perhaps someone can help. I am running a maximum likelihood estimation (competing risk duration model with unobserved heterogeneity) on 30 different datasets. The problem is that on 2 datasets the model does not converge. I am interested if there are any methods, based on the gradients or (an approximation of) the hessian which helps to

Dear colleagues, has anyone an idea how to carry out a nonparametric manova for comparing K groups? has anyone a good package of non-parametric stats? Thank you for your help. -- José David Gómez Químico Farmacéutico Universidad Nacional [[alternative HTML version deleted]]

This is a major revision, with two additional models included. 1) Multiresponse regression - family="mgaussian" Here we have a matrix of M responses, and we fit a series of linear models in parallel. We use a group-lasso penalty on the set of M coefficients for each variable. This means they are all in or out together 2) family="multinomial, type.multinomial=&...

Dear all, Is there a package in R that has implemented the MANN (Multiresponse Artificial Neural Network) presented in (Olden, 2006 - Ecological Applications)? Best Wishes, -- Prof. Gilson Correia de Carvalho, M.Sc. ---------------------------------------------------- Pesquisador Associado Laboratório de Ecologia Bentônica - LEB Instituto de Biologia - UFBA Universidade Fe...

Dear R-experts! I have y(x) data, dim(y) == dim(x) == c(2000, 2) I'd like to fit them with nls: fit.result <- nls ( y ~ f(x, p1, p2, p3), start = list(p1 = ... , p2 = .. , p3 = ..) ) Actually I want to fit y[,1] ~ x[,1] and y[,2] ~ x[,2] *simulaneously*, with the same parameters set {p1, p2, p3}. I tried to feed R tha above formula, R errors with:

...djustments to the right-hand boundary crossed a DST transition time. o On Windows, the code in as.POSIXct() to handle incorrectly specified isdst fields might have resulted in NA being returned. o aov() and manova() gave spurious warning about singular error model in the multiresponse case. o In ns() and bs(), specifying knots = NULL is now equivalent to omitting it, also when df is specified. (PR#14970) o sprintf() did not accept numbered arguments ending in zero. (PR#14975) o rWishart() could overflow the C stack and maybe crash the R process...

...djustments to the right-hand boundary crossed a DST transition time. o On Windows, the code in as.POSIXct() to handle incorrectly specified isdst fields might have resulted in NA being returned. o aov() and manova() gave spurious warning about singular error model in the multiresponse case. o In ns() and bs(), specifying knots = NULL is now equivalent to omitting it, also when df is specified. (PR#14970) o sprintf() did not accept numbered arguments ending in zero. (PR#14975) o rWishart() could overflow the C stack and maybe crash the R process...

...djustments to the right-hand boundary crossed a DST transition time. o On Windows, the code in as.POSIXct() to handle incorrectly specified isdst fields might have resulted in NA being returned. o aov() and manova() gave spurious warning about singular error model in the multiresponse case. o In ns() and bs(), specifying knots = NULL is now equivalent to omitting it, also when df is specified. (PR#14970) o sprintf() did not accept numbered arguments ending in zero. (PR#14975) o rWishart() could overflow the C stack and maybe crash the R process...