similar to: plotting output from LME with natural cubic spline

Hi, all I am trying to get the basis matrix and penalty matrix for natural cubic splines. In the "splines" package of R,"ns" can generate the B-spline basis matrix for a natural cubic spline. How can I get the basis matrix and penalty matrix for natural cubic spline. Thanks a lot! Lee [[alternative HTML version deleted]]

Hi, Everyboday, Does anybody know how to get the first derivative form the cubic natural spline matrix? Thanks !!! Jassy ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Nuoo-Ting (Jassy) Molitor, MS, Junior Statistician Division of Biostatistics Department of Preventive Medicine University of Southern California Tel: (323) 442-2584 1540 Alcazar St., CHP-210F Fax:

Suppose I have two var x and y,now I want to fits a natural cubic spline in x to y,at the same time create new var containing the smoothed values of y. How can I get it?

HI, While I was working on R , I had a pop up from mcafee asking me if I wasih to block or allow something to connect. I did not see r in the question, so I had clicked on the option " block". since then, when I start R through excel , I only get R console and I do nto get R script anymore. Please advise Thanks

Hello, I am using R and the smooth.Pspline function in the pspline package to smooth some data by using natural cubic splines. After fitting a sufficiently smooth spline using the following call: (ps=smooth.Pspline(x,y,norder=2,spar=0.8,method=1) [the values of x are age in years from 1 to 100] I tried to check that R in fact had fitted a natural cubic spline by checking that the resulting

Dear R-helpers, I need to fit natural cubic spline with specified number of knots. I expected 'splines' package will be helpful, but I am confused by its help. Is more detailed documentation available for it or could you recommend another R function? Best regards Ondrej Mikula

Hi all: I am a user of "JM" package. Here's the problem of "sample size". The warning is: Error in jointModel(fitLME, fitSURV_death, timeVar = "time", method = "piecewise-PH-GH") : sample sizes in the longitudinal and event processes differ. According to the suggestion of "missing data",I use the same data set(data_JM) without any

I contacted the package developer and that lead to me removing events at time 0 (or subjects with only 1 longitudinal measurement). I then still had the error message "Can't fit a Cox model with 0 failures" which I have managed to avoid by adding 1.8*10^(-15) to all my survival times, any number greater than this also works but nothing smaller! Any explanation of this would help!

Dear alltogether, how can a delay like possible with par(ask=TRUE) be attained while using trellis-plots within a loop or something like that? the following draws each plot without waiting for a signal (mouse-klick), so par() does not work for that: library(nlme) for(i in 1:3) { fitlme <- lme(Orthodont) par(ask=TRUE) # does not work with trellis.... print(

Hi All, We recently upgraded from UW (boy, starting to sound like a broken record, eh?)... Two problems: 1. Every two or three days users are unable to access their email via IMAP. It seems that it's because dovecot-auth has hung. Has anyone seen anything like this? 2. (the really serious one)... one of our users, "angie" has twice had her Outlook download

I have a question about the basis functions of cubic regression spline in mgcv. Are there some ways I can get the exact forms of the basis functions and the penalty matrix that are used in mgcv? Thanks in advance! Yan [[alternative HTML version deleted]]

Hi Users, I want to fit the maximums of a data series with a cubic spline. How do I go about this in R. I failed to figure out how I can use the mgcv library to do this. Thanks ---------------------------- ZABLONE

Hello,        Dr. Simon Wood told me how to force a cubic spline passing through a point. The code is as following. Anyone  who knows how I can change the code to force the first derivative to be certain value. For example, the first derivative of the constrained cubic spline equals 2 at point (0, 0.6).        I really appreciate your help!        Thanks!                 Best             Victor   

I'm trying to use R to determine the quality of a cubic Bezier curve approximation of an elliptical arc. I know the four control points and I want to compute (x,y) coordinates of many points on the curve. I can't find anything in either the base distribution or CRAN that does this; all the spline-related packages seem to be about *fitting* piecewise Bezier curves to a data set.

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Dear Thomas, I want to use evaluate effect of Age using restricted cubic form in the FGR function as Fgr.crr <- FGR(Hist(time, event) ~ rcs(Age_years), data=dat) It provides error. " Error in parse(text = termtext, keep.source = FALSE): .... 1: response ~ rcs(Age_years Do I need to change any of the R code? Regards Amalraj Raja The University of Aberdeen is a charity

I want to compare the fit of a quadratic model to continuous data, with that of a cubic spline fit. Is there a way of computing AIC from for e.g. a GAM with a smoothing spine, and comparing this to AIC from a quadratic model? Cheers ****************************************** Tom Reed PhD Student Institute of Evolutionary Biology 102 Ashworth Laboratories Kings Buildings University of

Dear list, I'm currently analyzing some count data using a hurdle model. I've used the rcspline.eval function in the Hmisc-library to contruct the spline terms for the regression model, and what I want in the end is the ability to compute coefficients and confidence intervals for different changes in the smooth function as well as plotting the smooth function along with the

Hi, I'm using the cubic splines from termstrc package. I invoked the splines_estim function with a group of 43 bonds. It computes 6 knot points and returns values for alpha1 to alpha7. My question is how to use these alpha1 to alpha7 in the equation of yield? For example, if I'm trying to find the yield at, say, 12.25 years, which falls between 3rd & 4th knot points what should the

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests). I am following Smoothing Splines, D.G. Pollock (available online) where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) + e_i