similar to: Monotone Smoothing specifically I splines

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

I get the following error: Error in backSpline.npolySpline(sp) : spline must be monotone Has anyone had this error before? any ideas on a workaround? > > vols=read.csv(file="C:/Documents and Settings/Hugh/My > Documents/PhD/Swaption vols.csv" + , header=TRUE, sep=",") > X<-ts(vols[,2]) > #X > > > dcOU<-function(x,t,x0,theta,log=FALSE){ +

dear R-help: one of my students is struggling to test an ordered alternative hypothesis on a set of groups (e.g., mu_a <= mu_b <= mu_c). There has been some literature on this topic -- a lot of this goes back to Bartholomew (1961); Gaines and Rice (see refs below) are the ones who've popularized it in the ecology community. The topic is closely related to isotonic regression, but

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

Hello, We would like to apply the smooth monotone function to our data which correspond to a non-linear function. We follow the example posted on the web, but in our case it did not apply. We always get a straight line in response. Which parameters we should change. ind.basis = create.bspline.basis(c(min(time),max(time)),nbasis=38,norder=4) Wfdob =

Hi, I would like to use constrained splines in a GLM model (Poisson link) to take into account the nonlinear effect of some covariate. The constraints I need are described below. I have several variables that I need concurrently in the same model. I looked at package mgcv but I do not know if/how I can use it in GLM (not GAM) : I could not manage to adapt the mono.con(mgcv) example to GLM. The

Hi, I am trying to use R to mimic what I did in SAS. proc transreg data=x ; model identity(GSI)=monotone(group1); output out=d2 pprefix=M; run; Accroding to SAS documentation, the MONOTONE transfomation algorithm comes from (Kruskal 1964, secondary approach to ties). I have tried ace. it does provide some kind of monotone transformation, but it is not what I expected. Here is how

I have developed a GAM model in order to predict Y using 4 X variables. 2 of these X's are factors, and 1 is a spline. Part of the data looks like: Days WRM variety PWM O_EC 31 75 1 90 234 31 79 1 78 283 31 82 1 92 281 31 84 1 96 213 31 99 2 69 247 31 100 2 77 324 31 104 2 74 259 31 118 2 81 282 31 61 3 58 478 31 98 3 83 429 31 98 3 70 379 31 156 3 87 467 31 78 4 56 283 31 97 4 67 282 31

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'm sure I'm doing something completely boneheaded here, but I've used this idiom (constructing an interpolation spline and using prediction from a backSpline to find an approximation profile confidence interval) many times before and haven't hit this particular problem: r2 <- c(1.04409027570601, 1.09953936543359, 1.15498845516117, 1.21043754488875,

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"

Dear list, As can be seen below, adding a NA to the y values in a call to isoreg results in a segfault. ir4 <- isoreg(1:10, y4 <- c(5, 9, 1:2, 5:8, NA, 8)) Adding missing values to the x values, on the contrary, gives an error, but maybe the error message could be tailored to this particular situation. y <- c(5, 9, 1:2, 5:8, 3, 8) x <- c(1:9, NA) isoreg(x, y) ## error message:

Full_Name: Petr Klasterecky Version: 1.8.1 OS: Windows XP, Linux Submission from: (NULL) (195.113.27.212) The isoreg() function causes R to crash when called repeatedly. Consider the following simple script: { library(modreg) N <- 10 x <- rnorm(N) print("Original x values:") print(x) for(n in (1:N)){print(y <- isoreg(x[1:n])$yf)} } I am able to run (call) it several

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]]

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

This seems to be a recurrent topic, but I don't remember hearing a definitive answer. I also apologies for cross-posting. Say I have a numerical response variable and a bunch of multi-level factors I want to use for modeling. I don't expect factor interaction to be important so there will be no interactions in the model. All this would be a perfect job for ANOVA except for one additional

I believe there is a memory leak in isoreg in the current version of R, as I believe the following shows > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 120405 3.3 350000 9.4 350000 9.4 Vcells 78639 0.6 786432 6.0 392463 3.0 > for(k in 1:100) { + + y <- runif(10000) + isoreg(x,y) + } > rm(x) > rm(y) > gc() used (Mb) gc

Dear R- User,   I have a dataset that looks like the following:   jh<-data.frame(  'id'=seq(1,10,1),   'time0'=c(8,5,8,8,9,NA,NA,2,4,5),   'time4'=c(NA,NA,9,8,NA,2,3,2,4,5),  'time8'=c(NA,2,8,NA,5,NA,2,3,NA,4),  'time12'=c(NA,2,NA,NA,NA,3,3,2,3,NA),  

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]]

I'm using the monoreg function (with weights) from the fdrtool package. How can I calculate the R square for this type of regression? Thanks for your help, Thierry -- View this message in context: http://www.nabble.com/R-square-for-Monotone-regression-tp15580803p15580803.html Sent from the R help mailing list archive at Nabble.com.