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...Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: x86_64-pc-linux-gnu (64-bit) > library(mgcv) This is mgcv 1.7-12. For overview type 'help("mgcv-package")'. > dat <- gamSim(1 , n=400 , dist="normal",scale = 2) Gu & Wahba 4 term additive model > gam( y ~ s(x0) , data = dat) Aborted </pre> I am using Ubuntu 11.10 - amd64, R version 2.14.0 and mgcv 1.7-12. any ideas? Reinhard [[alternative HTML version deleted]]

Dear R listers, When using smooth.spline to interpolate data, results are generally good. However, some cases produce totally unreasonable results. The data are values of pressure, temperature, and salinity from a probe that is lowered into the ocean, and the objective is to interpolate temperature and salinity to specified pressures. While smooth.spline provides excellent values at the

Hey all, I'm using the gam() function inside the mgcv package to fit a penalised spline to some data. However, I don't quite understand what exactly the intercept it includes by default is / how to interpret it. Ideally I'd like to understand what the intercept is in terms of the B-Spline and/or truncated power series basis representation. Thanks!

Hi, I've managed to get Skype running under Debian with the latest Debian release of wine. The sound is perfect, and there are no connection problems, but I noticed the following: 1) Sound from my microphone disappears ONLY when I start some other application or switch to another application. I can only vouch for this in KDE, and it happens whenever anything accesses the soundcard, so

Christoph Lehman had problems with seperated data in two-class logistic regression. One useful little trick is to penalize the logistic regression using a quadratic penalty on the coefficients. I am sure there are functions in the R contributed libraries to do this; otherwise it is easy to achieve via IRLS using ridge regressions. Then even though the data are separated, the penalized

I've written a series of functions that evaluates an integral from -inf to a or b to +inf using equally spaced quadrature points along a normal distribution from -10 to +10 moving in increments of .01. These functions are working and give very good approximations, but I think they are computationally wasteful as I am evaluating the function at *many* points. Instead, I would prefer to use

Hi there, I'm using R1.0.1 Windows 98. This file contains some inputs and an aov function code. Can someone check it for me? Somehow I got completely different answer when typing them in R and in Splus. Splus gives me this: > summary( Turnip.aov ) Error: Blocks Df Sum of Sq Mean Sq F Value Pr(F) Residuals 3 163.7367 54.57891 Error: Plots %in% Blocks