similar to: multidimensional (smoothing) splines

Hello everone,            Anyone who knows how to force a cubic smoothing spline to pass through a particular point?            I found on website  someone said that we can use "cobs package" to force the spline pass through certain points or impose shape           constraints (increasing, decreasing). However,  this package is using  B-spline and can only do linear and quadratic

Hello, I have been trying to take the derivative of a quadratic B-spline obtained by using the COBS library. What I would like to do is similar to what one can do by using fit<-smooth.spline(cdf) xx<-seq(-10,10,.1) predict(fit, xx, deriv = 1) The goal is to fit the spline to data that is approximating a cumulative distribution function (e.g. in my example, cdf is a 2-column matrix with x

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

Dear R-Users, I have problems with the cobs library. When doing the cobs example, I get the folling error message: example(cobs) cobs> x <- seq(-1, 3, , 150) cobs> y <- (f.true <- pnorm(2 * x)) + rnorm(150)/10 cobs> con <- rbind(c(1, min(x), 0), c(-1, max(x), 1), c(0, 0, 0.5)) cobs> Rbs <- cobs(x, y, constraint = "increase", pointwise = con)

Hi, The department of economics at our university (Budapest) is planning a course on numerical methods in economics. They are trying to decide which software to use for that, and I would like to advocate R. The other alternative is Matlab. I have found comparisons in terms of computational time for matrix algebra, but I don't think that is relevant: the bottleneck for economists is usually

I've been asked, and interested myself: Has anybody implemented the above in R or another S language dialect? We are talking about the algorithms / methodology by Wellner, Groeneboom and Jongbloed, e.g., from the following article @Article{Jongbloed:1998:ICM, author = "Geurt Jongbloed", title = "The Iterative Convex Minorant Algorithm for

Dear list, I have tried to setup my Xemacs for use with Sweave, which I indend to learn. I have followed the instructions in the Sweave FAQ, that is to say, I put (defun Rnw-mode () (require 'ess-noweb) (noweb-mode) (if (fboundp 'R-mode) (setq noweb-default-code-mode 'R-mode))) (add-to-list 'auto-mode-alist '("\\.Rnw\\'" . Rnw-mode)) (add-to-list

Hi, I am looking for an R package that would calculate multivarite (mostly 2d and 3d, tensor) cubic interpolating splines, so that I could evaluate these splines (and their derivatives) at many points (unkown at the time of calculating the spline polynomials) repeatedly. To make things concrete, I have an array V with dim(V) = k and gridpoint vectors grid=list(...), length(grid[[i]])==k[i],

Is there a way in R to test if a given number is an integer, ie a whole number? I am not referring to the data type of a number, but to its value. That is to say, is.whole(pi-pi+2) would be TRUE, whereas is.whole(4/3) would be false. At the moment I am using is.whole <- function(a) { floor(a)==a } which is OK for real numbers, but not for complex ones (a+bi would be a whole number if both a

I need to find a computationally simple process for the movement of interest rates. In this simplified model, an interest rate can have 3--5 possible values, and its movement is characterized by a matrix of transition probabilities (ie, it is a Markov process). I would like to estimate this process from a given set of data. For example, let the interest rate time series be: 7 3 8 2 5 9 6

Hi, i need to minimize a quadratic function with boundary condidtions and one equality condition. In order to do that i converted the equality constraint into 2 inequality constaints and passed everything cia constrOptim, as the manual said: everything included in the ... will be passed to Optim that will pass it back to fn in case it does not need it. My code is the following: mat <-

A student of mine wants to use R to do some nonmetric multidimensional scaling. According to the R FAQ, there's a package called pcurve that computes multidimensional scaling solutions, but I was not able to locate it the contrib page (I am a Windows user with R version 1.4.1). Can anyone tell me whether it is possible to do nonmetric multidimensional scaling with R, and if so, how? John

Hello list. Another 'puzzle' for which I don't have a clean solution. Say I have a multidimensional object, e.g.: Mm<-matrix(1:6, nrow=2, dimnames=list(c("a","b"), c("g","h","i"))) And on the other hand I have a list Ind<-list("b","g") This holds, for each dimension, an indexer for that dimension. Now I would

Take a look at package mgcv. Hope this helps. --Matt -----Original Message----- From: RenE J.V. Bertin [mailto:rjvbertin at despammed.com] Sent: Thursday, February 27, 2003 1:39 PM To: r-help at stat.math.ethz.ch Subject: [R] multidimensional function fitting Hello, I have been looking around for how to perform a multidimensional, arbitrary function fit (in any case non-linear; more below),

Dear all, I am new to R, and I am a biotechnologist, I want to fit a smoothing spline with smoothing parameter selected by generalized maximum likelihood. I was wondering what function implement this, and, if possible how I can find the fitted results for a certain point (or predict from the fitted spline if this is the correct language) -- View this message in context:

Hello. I have a 4 dimensional array and I want to fill in the slots with values which are a function of the inputs. Through searching the forums here I found that the function "outer" is helpful for 2x2 matrices but cannot be applied to general multidimensional arrays. Is there anything which can achieve, more efficiently than the following code, the job I want? K <-

Hi, anyone knows about any functions in R can get multidimensional integration not over a multidimensional rectangle (not adapt). For example, I tried the following function f(x,n)=x^n/n! phi.fun<-function(x,n) { if (n==1) { x }else{ integrate(phi.fun, lower=0, upper=x, n=n-1)$value } } I could get f(4,2)=4^2/2!=8, but failed in f(4,3)=4^3/3! Thanks Best, Lynette

my=function(x){ len=1 for(i in 1:len){ y[i]=x[i] } g=1 w=NULL t=NULL for(i in 1:len)w[i]=x[i+len] for(i in 1:len)t[i]=x[i+2*len] for(i in 1:len)g=g*dnorm(y[i])*dnorm(w[i])*dnorm(z[i]) return(g) } cuhre(6,1,my,rep(-100,6),rep(100,6)) Error in crff(match.call(), integrand, "cuhre", libargs, ...) : Additional argument not expected in the integrand function function change to

Consider the following data frame X <- data.frame(Titanic) Does anyone know of an easy way to convert X into a multidimensional array? Example that doesn't work X <- as.array(X, dim=c(4,2,2,2)) To do what I need, X needs to be converted into an array of dimensions c(4,2,2,2) in this case, not a table. Thanks in advance.

I am moving this to r-devel. The problem and solution below posted on r-help could have been a bit slicker if %*% worked with multidimensional arrays multiplying them so that if the first arg is a multidimensional array it is mulitplied along the last dimension (and first dimension for the second arg). Then one could have written: Tbar <- tarray %*% t(wt) / rep(wti, each = 9) which is a bit