similar to: GAMS and Knots

Dear List, I'm using GAMs in a multiple imputation project, and I want to be able to combine the parameter estimates and covariance matrices from each completed dataset's fitted model in the end. In order to do this, I need the knots to be uniform for each model with partially-imputed data. I want to specify these knots based on the quantiles of the unique values of the non-missing

Hi, all I want to choose same knots in GAM for 10 different studies so that they has the same basis function. Even though I choose same knots and same dimensions of basis smoothing, the basis representations are still not same. My command is as follows: data.gam<-gam(y~s(age,bs='cr',k=10)+male,family=binomial,knots=list(age=seq(45,64,length=10))) What is my mistake for choice of

Dear list, if I do smooth.spline(tmpSec, tmpT, all.knots=T) with the attached data, I get this error-message: Error in smooth.spline(tmpSec, tmpT, all.knots = T) : smoothing parameter value too small If I do smooth.spline(tmpSec[-single arbitrary number], tmpT[-single arbitrary number], all.knots=T) it works! I just don't see it. It works for hundrets other datasets, but not for

I get the following error when I run qplot() qplot(grade, read,data = hhm.long.m, geom = c("point", "smooth")) Error in smooth.construct.cr.smooth.spec(object, data, knots) : x has insufficient unique values to support 10 knots: reduce k. I am not sure how to tackle this problem. When I take a subsample (< 1000) than I am able to run that function but with my sample

Hi, I have fit a series of ols() models, by group, in this manner: l <- ols(y ~ rcs(x, 4)) ... where the series of 'x' values in each group is the same, however knots are not always identical between groups. The result is a table of 'coefs' derived from the ols objects, by group: group Intercept top top' top'' 1 6.864 0.01 2.241 -2.65

Hi r-helpers. Please forgive my ignorance, but I would like to plot a smoothing spline (smooth.spline) from package "stats", and show the knots in the plot, and I can't seem to figure out where smooth.spline has located the knots (when I use nknots). Unfortunately, I don't know a lot about splines, but I know that they provide me an easy way to estimate the location of local

Hi all, I'm trying to use a gam (mgcv package) to analyse some data with a roughly U shaped curve. My model is very simple with just one explanatory variable: m1<-gam(CoT~s(incline)) However I just keep getting the error message "Error in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots) : A term has fewer unique covariate combinations than specified maximum degrees of

Dear List, I'm using gam in a multiple imputation framework -- specifying the knot locations, and saving the results of multiple models, each of which is fit with slightly different data (because some of it is predicted when missing). In MI, coefficients from multiple models are averaged, as are variance-covariance matrices. VCV's get an additional correction to account for how

Hi All, I am trying to figure out how to get the position of the knots in a pspline used in a cox model. my.model = coxph(Surv(agein, ageout, status) ~ pspline(x), mydata) # x being continuous How do I find out where the knot of the spline are? I would like to know to figure out how many cases are there between each knot. Best, Federico -- Federico C. F. Calboli Department of Epidemiology

Dear R users, I'm new to both R and to this list and would like to get advice on how to build generalized additive models in R. Based on the description of gam, which I found on the R website, I specified the following model: model1<-gam(ST~s(MOWST1),family=binomial,data=strikes.S), in which ST is my binary response variable and MOWST1 is a categorical independent variable. I get the

Hi, I have a variable (current speed direction) which is circular (0=360 degrees), and I'd like my GLM to include the variable as a circular variable. Can I do this? And what is the code? I'm actually doing a GLM-GEE using the 'geepack' package, so want to use it in that, but also interested in whether it can also be used in GLMs and GAMs (I use the 'mgcv' package for

Dear Simon, Thank you for your quick reply! I used to perform the GAMsetup in the following manner: GAMsetup sintax: x.summer: vector used for construct the spline knots<-14 N<-length(x.summer) x<-array(x.summer,dim=c(1,N)) G<-list(m=1,n=N,nsdf=0,df=knots+1,dim=1,s.type=0,by=0,by.exists=FALSE,p.order=0,x=x,n.knots=knots,fit.method="mgcv") H<-GAMsetup(G) with the

I would like to fit a logistic regression using a smothing spline, where the spline is a piecewise cubic polynomial. Is the knots option used to define the subintervals for each piece of the cubic spline? If yes and there are k knots, then why does the coefficients field in the returned object from gam only list k coefficients? Shouldn't there be 4k -4 coefficients? Sincerely, Bill

Hi, I have a piece of R code that calls mgcv::gam. The code runs fine in the interactive mode, but terminates R w/o a single message when run non-interactively. Though I think I should be able to locate the problem by brute force I'd appreciate an advise how to do it more intelligently using R debugging tools. At this time I only know that it has something to do with me loading my custom

Hi, I am using GAMs (package mgcv) to smooth event rates in a penalized regression setting and I was wondering if/how one can select the order of the derivative penalty. For my particular problem the order of the penalty (parameter "m" inside the "s" terms of the formula argument) appears to have a larger effect on the AIC/deviance of the estimated model than the

Hi, there, I have 5 datasets. I would like to choose a basis spline with same knots in GAM function in order to obtain same basis function for 5 datasets. Moreover, the basis spline is used to for an interaction of two covarites. I used "cr" in one covariate, but it can only smooth w.r.t 1 covariate. Can anyone give me some suggestion about how to choose a proper smoothing spline

Hi there, I am reading the 2004 paper "Smoothing with mixed model software" in Journal of Statistical Software, by Ngo and Wand. I tried to run their first example in Section 2.1 using R but I had some problems. Here is the code: library(nlme) fossil <- read.table("fossil.dat",header=T) x <- fossil$age y <- 100000*fossil$strontium.ratio knots <-

Hello I'm using function gam from package mgcv to fit splines. ?When I try to make a prediction slightly beyond the original 'x' range, I get this error: > A = runif(50,1,149) > B = sqrt(A) + rnorm(50) > range(A) [1] 3.289136 145.342961 > > > fit1 = gam(B ~ s(A, bs="ps"), outer.ok=TRUE) > predict(fit1, newdata=data.frame(A=149.9), outer.ok=TRUE) Error

Hello R Users, I have a question regarding fitting a model with GAM{mgcv}. I have data from several predictor (X) variables I wish to use to develop a model to predict one Y variable. I am working with ecological data, so have data collected many times (about 20) over the course of two years. Plotting data independently for each date there appears to be relationships between Y (fish density)

I tried this over a the grasp users yahoo group and got no response....So I wonder if anyone here knows about grasper I keep getting this error when trying to run a model. Error in smooth.construct.tp.smooth.spec(object, data, knots) : Too many knots for t.p.r.s term: see `gam.control' to increase limit, or use a different basis, or see large data set help for `gam'. I'm using