similar to: plotting additive ns components

Readers, I have been reading 'the r book' by Crawley and think that the generalised additive model is appropriate for this problem. The package 'gam' was installed using the command (as root) install.package("gam") ... library(gam) > library(gam) Loading required package: splines Loading required package: akima > library(mgcv) This is mgcv 1.3-25 Attaching

I have estimated a multiple nonparametric regression using the loess command in R. I have also estimated an additive version of the model using the gam function. Is there a way of using the output of these two models to test the restrictions imposed by the additive model?

Hi, I?m trying to combine the Locfit Package with the Mgcv package (to use Generalized Additive Models with Locfit components). I read the book written by Clive Loader where it?s said that, for the S language, you just have to "load" the locfit package using the command : Library(locfit, first="T") in order to use locfit components in an additive model. But I can?t. I guess

Hi, I am looking at the change in N concentration in plant roots over 4 time points and I have fit a spline to the data using ns and lme: fit10 <- lme( N~ns(day, 3), data = rcn10G) I may want to adjust the model a little bit, but for now, let's assume it's good. I get output for the fixed effects: Fixed: N ~ ns(day, 3) (Intercept) ns(day, 3)1 ns(day, 3)2 ns(day, 3)3

Tukey divides the points into three groups, not the x and y values separately. I'll try to get hold of the book for a direct quote, might take a couple of days. On Mon, May 29, 2017 at 8:40 AM, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > On 27/05/2017 9:28 PM, GlenB wrote: > >> Bug: stats::line() does not produce correct Tukey line when n mod 6 is 2 >> or

A usually trustworthy R correspondent posted a pure R implementation on SO at some point in his lost youth: https://stackoverflow.com/questions/3224731/john-tukey-median-median-or-resistant-line-statistical-test-for-r-and-line This one does indeed generate the line of identity for the (1:9, 1:9) case, so I do suspect that we have a genuine scr*wup in line(). Notice, incidentally, that >

Hi all, Could anybody recommend some easy-to-understand and example based notes/tutorials on how to use cubic splines to do filtering on multi-dimension data? I am confused by the 1-dimensional case, and more confused by multi-dimensional case. I found all the books suddenly become very abstract when it comes to this subject. They don't provide examples in R or Splus at all. Specifically,

I am fittling a spline to a variable in a regression model, I am then using the predict.glm funtion to make some predictions. When I use bs to fit the spline I don't have any problems using the predict.glm function however when I use ns I get the following error: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for

Hi - How can I 'manually' reproduce the results in 'pred1' below? My attempt is pred_manual, but is not correct. Any help is much appreciated. library(splines) set.seed(12345) y <- rgamma(1000, shape =0.5) age <- rnorm(1000, 45, 10) glm1 <- glm(y ~ ns(age, 4), family=Gamma(link=log)) dd <- data.frame(age = 16:80) mm <- model.matrix( ~ ns(dd$age, 4)) pred1 <-

I want to fit a series of lme() regression models that differ only in the degrees of freedom of a ns() spline. I want to use a wrapper function to do this. The models will be of the form y ~ ns(x, df=splineDF) where splineDF is passed as an argument to a wrapper function. This works fine if the regression function is lm(). But with lme(), I get an error. fitfunction() below demonstrates this.

Here is an attached patch. Best, Serguei. Le 29/05/2017 ? 12:21, Serguei Sokol a ?crit : > The problem or actual R implementation relies on an assumption > that median(x[i] | x[i] <= quantile(x, 1/3)) == quantile(x, 1/6) > which reveals not to be true despite very trustful appearance. > > If we continue with the example of x=y=1:9 > then quantile(x, 1/6)=2.5 (here quantile()

Hi, I'm experimenting with random effect natural splines, and I've encountered an odd problem. library(nlme); library(splines) a <- data.frame(x = 1:10, y = 1:10 + runif(10, min=-3, max=3), c = rep(c(1,2),5)) df <- 10 lml <- lmList(y ~ ns(x,df=df) | c, a) Error in df - 1 : non-numeric argument to binary operator I presume this is because the formula isn't evaluated

Bug: stats::line() does not produce correct Tukey line when n mod 6 is 2 or 3 Example: line(1:9,1:9) should have intercept 0 and slope 1 but it gives intercept -1 and slope 1.2 Trying line(1:i,1:i) across a range of i makes it clear there's a cycle of length 6, with four of every six correct. Bug has been present across many versions. The machine I just tried it on just now has R3.2.3:

Hello, I am wondering if someone can direct me to the syntax of the Generalised Additive Model in R? In Splus this is gam(formula, ... inputs would be pretty much the same as glm I suspect..) Any response on that (or what package should I grap) would be appreciated very much. Thanks, Peppy Adi-Purnomo -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help

Apologies if this is this too obscure for R-help. In package splines, ns(x,,knots,intercept=TRUE) produces an n by K+2 matrix N, the values of K+2 basis functions for the natural splines with K (internal) knots, evaluated at x. It does this by first generating an n by K+4 matrix B of unconstrained splines, then postmultiplying B by H, a K+4 by K+2 representation of the nullspace of C (2 by K+4),

On Thu, 18 Mar 2004 k.hansen@biostat.ku.dk wrote: > The bug exists on R-1.9.0-alpha compiled the 10/3. > > Termplot has a problem if either the model only contains a single term > or if asked to plot a single term. In addition there are problems with > the option se = TRUE. I can't reproduce this in either R-devel or 1.8.1, and termplot hasn't changed since January. I do

I'm a bit puzzled by how to write out additive random effects in glmmPQL. In my situation, I have a factorial design on two (categorical) random factors, A and B. At each combination, I have a binary response, y, and two binary fixed covariates, C and D. If everything were fixed, I would use glm(y ~ A + B + C + D, family = binomial) My first thought was to use glmmPQL(y ~ A + B, random

Hi - I have a series of 9 GAM regressions with about 5 parametric effects and three non-parametric effects in each. What is a good library or command for turning GAM outputs into publication-ready regression tables? I tried apsrtable and the mtable command in memisc but neither seemed to work with the gam output. I'd be okay with two separate tables - one for the parametric effects and

Dear R users, Using the library(mgcv) and running R under MacOSX, I have fitted a generalised additive model with binomial errors in order to check the linearity of two continuous variables ap2mm and diffdaysm in a glm: > mymodel.gam <- gam(diedhos~ s(ap2mm) + Dweekm + s(diffdaysm) + Dweekm:diffdaysm + ap2mm:Dweekm, binomial) I would like postscript gam plots for the two smoothed

Hi R-users, I am using lme to fit a linear mixed model with the nlme package, does anyone know if it is possible to obtain standard error estimates of the variance components estimators and an adequate method to test the significance of the variance component? Thanks, Berta. [[alternative HTML version deleted]]