similar to: Regressions with monotonicity constraints

Hi, I'm trying to find an implementation of monotonic regression in R and I haven't been able to find anything that's really related to this. isoMDS in the MASS package uses monotonic regression, however, I was wondering if there is any standalone function for monotonic regression? Basically what I'm trying to do is implement monotonic regression where I can see not just the

Hi all, I'm looking for smooth monotone estimation packages, preferably using splines. I downloaded the 'cobs' package and intend to use it, but since it offers only quadratic splines based on L1 minimization, I'd like to compare its performance to that of a more 'mainstream' cubic-spline, L2-norm minimizing spline. Preferably a smoothing spline. Does anyone know of such

Before re-inventing the wheel I would like to ask: does anyone know about an optimizer in R which can reliably identify which value of X (Xopt) leads to Y (Yopt) closest to Ytarget in Y <- MonotoneStepFun(X) optionally with the restriction that Yopt <= Ytarget (at least if any Y <= Ytarget, otherwise any Yopt > Ytarget would be the preferred answer) If none is known, I will write

>I am trying to use the Hmisc function transace to transform predictors > > test<-cbind(flowstress,pressres,alloy) > xtrans<-transace(x,binary=pressres',monotonic='flowstress', categorical='alloy') > > >and I am getting the following message?? >Error in ace(x[, -i], x[, i], monotone = im, categorical = ic) : > unused argument(s) (monotone ...)

Here is a problem that perhaps someone out here has an idea about. It vaguely reminds me of something I've seen before, but can't place. Can anyone help? For multiple imputation, there are simpler methods available if the patterns of missing data are 'monotone' --- if Vj is missing then all variables Vk, k>j are also missing, vs. more complex methods required when the

Dear R help list, This is a long post so apologies in advance. I am estimating a model with the mgcv package, which has several covariates both linear and smooth terms. For 1 or 2 of these smooth terms, I "know" that the truth is monotonic and downward sloping. I am aware that a new package "scam" exists for this kind of thing, but I am in the unfortunate situation that I am

Hi, Has anyone written code for isotonic regression on ordered rectangular grids? Nathan Nathan Leon Pace, MD, MStat University of Utah Salt Lake City, UT 84132 Office: 801.581.6393 Fax: 801.581.4367 Cell: 801.558.3987 Pager: 801.291.9019 Home: 801.467.2925 [[alternative text/enriched version deleted]]

It seems that VCPU system time isn''t monotonic (using 3.0.4). It seems it might be correlated to when a VCPU is switched across real CPUs but I haven''t conclusively proved that. But e.g.: { old = { time = { version = 0x4ec pad0 = 0xe8e0 tsc_timestamp = 0x22cc8398b7194 system_time =

Hello I am looking for a function to create an Integrated (I) spline basis, somehting similar to the likes of 'bs' and 'ns'. I have come across the funcitons, fda::eval.monfd Values of a Monotone Functional Data Object fda::/.fd FDA internal functions fda::monfn Evaluates a monotone function fda::smooth.monotone Monotone

Could anyone help me to find the mathematical expression to calculate the monotonicity correlation coefficient between two variables? Thanks in advance. Luis Rivera. Universidad de Alcal?. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Well, I could argue that it's not *completely* OT since my question is motivated by an enquiry that I received in respect of a CRAN package "Iso" that I wrote and maintain. The question is this: Given observations y_1, ..., y_n, what is the solution to the problem: minimise \sum_{i=1}^n (y_i - y_i^*)^2 with respect to y_1^*, ..., y_n^* subject to the "isotonic"

Hi, In my version of R, the stats package splinefun code for fitting a Fritsch and Carlson monotonic spline does not appear to guarantee a monotonic result. If two adjoining sections both have over/undershoot the way the resulting adjustment of alpha and beta is performed can give modified values which still do not satisfy the required constraints. I do not think this is due to finite precision

Hello, i have a little problem with R and i hope you can help me. I want to use splines to estimate a function but i want to force the interpolation to be monotone. Is this possible with R ? Thank you, Rémi. --------------------------------- [[alternative HTML version deleted]]

I am wondering if there is any package in R that can fit a nonparametric regression model with monotone constraints on the fitted results. --------------------------------- [[alternative HTML version deleted]]

Dear R users, I am currently attempting to fit logistic regression models in R, where the slopes should be restricted to positive values. Although I am aware of the package nnls (which does the trick for linear regression models), I did not find any solution for logistic regression. If there is any package available for this purpose, I would be interested to know them. Alternatively, I realize

Nadav, Arnold, et al., I have a number of loops that I would like us to be able to autovectorize (common, for example, in n-body inter-particle force kernels), and the problem is that they look like this: for (int i = 0; i < N; ++i) { if (r[i] > 0) v += m[i]*...; } where, as written, m[i] is not accessed unless the condition is true. The general problem (as is noted by the loop

Has anyone got a function for smooth monotonic interpolation of a univariate function? I'm after something like the NAG function PCHIM which does monotonic Hermite interpolation. Alternatively, montononic cubic spline interpolation. Please reply directly. Rob Hyndman ___________________________________________________ Rob J Hyndman Associate Professor & Director of Consulting

Hi, I have written the following function to check whether a vector has elements satisfying monotonicity. is.monotone <- function(vec, increase=T){ # check for monotonicity in time-stamp data for cortisol collection ans <- TRUE vec.nomis <- vec[!is.na(vec)] if (increase & any(diff(vec.nomis,1) < 0, na.rm=T)) ans <- FALSE if (!increase & any(diff(vec.nomis,1) > 0,

Hi Hal, Yes, I agree that this is a problem that prevents vectorization in many loops. Another problem that we have is that sunk loads don’t preserve their control dependence properties. For example in the code below, if we sink the load into the branch then we can't vectorize the loop. x = A[i] if (cond) { sum += x; } I agree with you that checking the first and last element for each

Dear All, Which package/function could i use to solve following linear least square problem? A over determined system of linear equations is given. The nnls-function may would be a possibility BUT: The solving is constrained with a inequality that all unknowns are >= 0 and a equality that the sum of all unknowns is 1 The influence of the equations according to the solving process is