similar to: pseudo-additive seasonal decomposition

Dear R users, I am trying to use "gamm" from package "mgcv" to model results from a mesocosm experiment. My model is of type M1 <- gamm(Resp ~ s(Day, k=8) + s(Day, by=C, k=8) + Flow + offset(LogVol), data=MyResp, correlation = corAR1(form= ~ Day|Mesocosm), family=poisson(link=log)) where the response variable is counts, offset by the

Hello, I am trying to fit a gamm (package mgcv) model with a smooth term, a linear term, and an interaction between the two. The reason I am using gamm rather than gam is that there are repeated measures in time (which is the smooth term x1), so I am including an AR1 autocorrelation term. The model I have so far ended up with is of the type gamm(y ~ s(x1) + s(x1, by=x2), correlation =

Hi! I'm using a large panel data, and now I have faced some difficulties with my analysis. The predictors are not normally distributed and there are quite many outliers (some of them are influential though). I have tried to change the logarythm, but i'm not sure, how to do that. I want also draw a plot picture in which logarythms of predictors x and y are changed. How could I do that?

I'm taking the residual-regression approach to semiparametric estimation (Robinson 1988, Econometrica), and basically using SemiPar simply as a convenient means of doing multivariate nonparamteric additive models. The final bit of code is here: finalfit <- spm(res~f(V3,basis="trunc.poly")+f(V5,basis="trunc.poly")+f(V6,basis="trunc.poly")) summary(finalfit)

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

Hello! I would like to ask whether the seasonality implemented in arima() is additive or multiplicative? I searched a lot, but I could not find an answer to that question, although it has been asked other times too. Thank you very much for your attention. Regards, Martin

Hello List, I've been unsuccessful in tracking down a package that does MART. J Friedman's website has a page that mentions an R implementation but the links lead nowhere. There is also a nice walkthru/vignette pdf on his site. But I can not find this package anywhere. Perhaps it's commercial? Does anyone know of where it might be available or of any other R package (or Matlab

Dear R-users, I'd like to announce the release of the amer-package that adds the capability to fit generalized additive mixed models to lme4. It includes a vignette with real data examples and a brief summary of the theory behind the implementation. Best, Fabian [[alternative HTML version deleted]] _______________________________________________ R-packages mailing list R-packages at

? stato filtrato un testo allegato il cui set di caratteri non era indicato... Nome: non disponibile URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20100929/bedab79b/attachment.pl>

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, I'd like to announce the release of the amer-package that adds the capability to fit generalized additive mixed models to lme4. It includes a vignette with real data examples and a brief summary of the theory behind the implementation. Best, Fabian [[alternative HTML version deleted]] _______________________________________________ R-packages mailing list R-packages at

Hello, My name is Dontrece Smith. I am creating figures for my GAMs. I change my qualitative variables to 1 or 2 in my dataset, so I would be able to run my GAMs. However, R will only display plots for my quantitative variables and not my qualitative variables. Is there any way to fix this issue? I listed some of my code below: > library(mgcv) This is mgcv 1.7-13. For overview type

Hello, I work with a mixed model with 4 predictor variables Time, Size, Charge, Density and Size, Charge, Density are factors, all with two levels. Hence I want to put their interactions with Time into the model. But, I have two data sets (Replication 1 and 2) and I want that Replication is random effect. Here is my code: knots <- default.knots(Time) z <- outer(Time, knots, "-")

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

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

Hello, I am trying to fit a GAM for a simple model, a simple model, y ~ s(x0) + s(x1) ; with a constraint that the fitted smooth functions s(x0) and s(x1) have to each always be >0. >From the library documentation and a search of the R-site and R-help archives I have not been able to decipher whether the following is possible using this, or other GAM libraries, or whether I will have to try

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?

I have an additive model of the following form : zmdlfit <- lm(z~ns(x,df=6)+ns(y,df=6)) I can get the fitted values and plot them against z easily enough, but I also want to both obtain and plot the two additive components (the estimates of the two additive terms on the RHS) I've been looking at manuals and searching on the internet and searching the archives, but I'm apparently

Would anyone be able to provide insight for the following question, please? Setting: estimation of prediction intervals for age-period-cohort models using GAMs (rate ~ s(age,period)) Method: bootstrap (Davison and Hinkley, 1997) Issue: standardisation of the residuals for resampling requires an adjustment using the diagonals of the hat matrix. Is there a simple way to get the hat values out of a

Dear All, I've used the X-12-ARIMA or its earlier versions from S+ and R under both Unix and Windows platforms for many years using the klugey approach of calling an executable using in R the system function. I've found this serviceable for the following reasons. 1) Paul Gilbert's hunch is correct that many of the subroutines have extensive IO calls (especially the X-11 engine)