similar to: Monotone splines

For a Monte Carlo study I need to extract from an lme model the estimated standard deviation of a random effect and store it in a vector. If I do a print() or summary() on the model, the number I need is displayed in the Console [it's the 0.1590195 in the output below] >print(fit) >Linear mixed-effects model fit by maximum likelihood > Data: datag2 > Log-likelihood:

I'm seeking some advice on effectively using the new Matrix library in R1.9.0 for operations with large dense matrices. I'm working on integral operator models (implemented numerically via matrix operations) and except for the way entries are generated, the examples below really are representative of my problem sizes. My main concern is speed of large dense matrix multiplication. In R

Hello, Does anybody know if there is somewhere in R a function to calculate the Lyapunov exponent in a time series? Thanks, Philippe Grosjean .......................................................<??}))><.... ) ) ) ) ) ( ( ( ( ( Prof. Philippe Grosjean \ ___ ) \/ECO\ ( Numerical Ecology of Aquatic Systems /\___/ ) Mons-Hainaut University, Pentagone / ___ /( 8, Av. du

Dear all, Has anyone worked on coding for calculating Lyapunov Exponent for a time series data? or any package is available for computing Lyapunov? Please advice and many thanks in advance. Catherine X Wang

R-users -- I would be interested in tools in R to fit the following gam model: logit(p) = a + f(x1) + f(x2) + f(x1,x2), where f(x1,x2) defines a surface. I have looked into the mgcv library, but it seems only to fit models of the form: logit(p) = a + f(x1) + f(x2) Any ideas? Cheers, Dan =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Dan Powers Associate Professor,

R help list subscribers, I am a new user of R. I am attempting to use R to explore a set of equations specifying the dynamics of a three trophic level food chain. I have put together this code for the function that is to be evaluted by LSODA. My equations Rprime, Cprime, and Pprime are meant to describe the actual equation of the derivative. When I run LSODA, I do not get the output that

Dear guRus, I am doing a logistic regression using restricted cubic splines via rcs(). However, the fitted probabilities should be nondecreasing with increasing predictor. Example: predictor <- seq(1,20) y <- c(rep(0,9),rep(1,10),0) model <- glm(y~rcs(predictor,n.knots=3),family="binomial") print(1/(1+exp(-predict(model)))) The last expression should be a nondecreasing

I am interested in using the nonlinear forecasting techniques developed by Sugihara et al. In particular the simplex projection and the S-Map (see this website for details and reprints: http://iod.ucsd.edu/simplex/ ). I've looked through CRAN but could not find any package with functions that allow such analysis. Any pointers? Thanks, Manu

Dear R users and experts, I am using Homals to perform categorical PCA on some survey data. The documentation is a bit obscure, however, I followed the examples. X is a matrix with ordinal 1-5 Likert scale entries on 24 questions from about 2500 respondents. So I run X.nlpc = homals(X, rank = 1, level = "ordinal", ndim = 10) to get the first 10 pc's (the number of dimensions is

Full_Name: Greg Kochanski Version: 2.2.1 OS: Debian Linux (testing) Submission from: (NULL) (212.159.16.190) This is really a feature request. When you do mosaicplot() on a data set where the probability of several nearby rows is small, then the labels for those rows are plotted overlapping each other. This situation can be improved by calling mosaicplot() with a large value of

Dear All, I'm trying to construct confidence interval for an additive quantile regression model. In the quantreg package, vignettes section: Additive Models for Conditional Quantiles http://cran.r-project.org/web/packages/quantreg/index.html It describes how to construct the intervals, it gives the covariance matrix for the full set of parameters, \theta is given by the sandwich formula

Hi I've simulated multivariate longitudinal. The data is a mixture of conitnous and categorical data. I've stored it in matrix form with the time dependent variables as colons. Now I want to create a monote missing data pattern starting of with MCAR-missingnes and different proportions of missingdata and then refine the function to handle MAR and NMAR. Is there anybody that could help or

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

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){ +

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.

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]]

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 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

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),  

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