similar to: How to get the penalty matrix for natural cubic spline?

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?

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]]

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

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

Greetings, I would like to use a cubic spline or smoother to model the fixed effects within nlme. So far the only smoother I have been able to get to run successfully in nlme is smooth(). I tried smooth.spline: fixed=list(lKa~1,lCL~smooth.spline(BSA, df=3)) the error I got was the following. Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : invalid

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 http://www.nabble.com/file/p25569553/SPLINES.PDF SPLINES.PDF where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) +

Hello,   does anyone have an example on how to use restricted cubic splines function rcs within survfit.cph, if cph (Cox Proportional Hazard Regression) was done with restricted cubic splines (which I made to work)? Thank you. > [[alternative HTML version deleted]]

Dear all, I want to use smooth.spline to construct a cubic smoothing spline and its first derivative to my data. However, the predict.smooth.spline does not seem to provide a SE for both the fitted values and their derivatives. How should I calculate it? Thank you very much, Bingzhang

Hallo, I'm facing a problem and I would really appreciate your support. I have to translate some Matalb code in R that I don't know very well but I would like to. I have to interpolate 5 point with a cubic spline function and then I expect my function returns the Y value as output a specific X value inside the evaluation range. Let's suppose that: 1- *X = [-10, -5, 0, 5, 10]* 2

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.

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 all, Is the restricted cubic spline function working properly in the glm model? We used glm(y~rcs(x,5), family=binomial) but it seems that for some theoretical reasons the rcs, restricted cubic spline function can not be fitted by a glm function. Is this correct? Regards, Jan ((Originally, we used lrm(y~ rcs(x,5)) but we couldn't find how to derive the AIC value of the fitted model.

Hi, I'm using natural cubic splines from splines::ns() in survival regression (regressing inter-arrival times of patients to a queue on queue size). The queue size fluctuates between 3600 and 3900. I would like to be able to run predict.survreg() for sizes <3600 and >3900 by assuming that the rate for <3600 is the same as for 3600 and that for >4000 it's the same as for

Hello everyone,          Dr. wood told me that I can adapting his example to force cubic spline to pass through certain point.          I still have no idea how to achieve this. Suppose we want to force the cubic spline to pass (1,1), how can I achieve this by adapting the following code? # Penalized example: monotonic penalized regression spline ..... # Generate data from a monotonic truth.

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

(corrected version of previous posting) I fit a GAM to turtle growth data following methods in Limpus & Chaloupka 1997 (http://www.int-res.com/articles/meps/149/m149p023.pdf). I want to obtain figures similar to Fig 3 c & f in Limpus & Chaloupka (1997), which according to the figure legend are "expected size-at-age functions" obtained by numerically integrating

# I need to generate some data. I'm modeling some time series that follow a # negative exponential decay (mostly). I have 20 samples that can easily be fit with cubic splines. # What I want to do is generate many thousands of similar samples using the parameters from the splines # For instance one data sample looks not unlike this: foo.curve <- 1 * exp(-0.01 * 1:500) + 0.5

I have used LME to fit a mixed effects model on my data. The data has 274 subjects with 1 to 6 observations per subject. Time is not linearly associated with the outcome, so I used ns to fit a natural cubic spline with 3 auto knots. Subject and the natural cubic time of spline are both treated as random effects. This model has run without any problem, but now I would like to plot trajectories for

R-users E-mail: r-help@r-project.org My understanding is that package "mgcv" is based on "Generalized Additive Models: An Introduction with R (by Simon N. Wood)". On the page 126 of this book, eq(3.4) looks a quartic equation with respect to "x", not a cubic equation. I am wondering if all routines which uses cubic splines in mgcv are based on this quartic

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