search for: backfit

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y * wts, ny = ny, tol = as.double(tol), coefficients = mat.or.vec(p,...

Hi, I just wanted to check I'm not re-inventing the wheel here. I'm developing a new algorithm for backfitting (i.e. additive models) and for computing partial residuals, where partial residuals are still computed even where there are missing values. Noting additive models here contain both linear terms and smooth terms. If I am re-inventing the wheel could some one please let me know. I'm kind of...

...<-1 if(any(is.na(annJMSLTv.box[,i])==F)) { fit<-lm(annJMSLTv.box~.,data=data.frame(annJMSLTv.box=annJMSLTv.box[,i],docuProxies), na.action=na.omit) RQuadrat[[j]][i]<-round(summary(fit)$r.squared, dig=3) frei.grade[[j]][i]<-fit$df.residual for(j in 2:26) { backfit<-step(fit,scale=2,trace=F,steps=1) fit<-eval(backfit$call) RQuadrat[[j]][i]<-round(summary(fit)$r.squared,3) frei.grade[[j]][i]<-fit$df.residual elimParam[[j]][i]<-paste(as.character(backfit$anova$Step),collapse="") } cat(i,' ') }...

...(m1) # forward-fit random effect structure (simple for the purposes # of the example). m2 <- ffRanefLMER.fnc(model = m1, data = eeg, ran.effects = c("(0 + LengthBc | Subject)", "(0 + WMCc | Item)"), log.file = FALSE) # backfit model m2. In this case, could use bfFixefLMER_t.fnc instead. m3 <- bfFixefLMER_F.fnc(m2, log.file = FALSE) # The calls to ffRanefLMER.fnc and bfFixefLMER_F.fnc could # be replaced by a call to fitLMER.fnc. In this latter case, however, # bfFixefLMER_F.fnc would be called first, then the...

...ne trying URL 'http://cran.cnr.Berkeley.edu/src/contrib/gam_0.97.tar.gz' Content type 'application/x-gzip' length 89613 bytes opened URL ================================================== downloaded 87Kb * Installing *source* package 'gam' ... ** libs g77 -fpic -g -O2 -c backfit.f -o backfit.o g77 -fpic -g -O2 -c backlo.f -o backlo.o g77 -fpic -g -O2 -c bsplvd.f -o bsplvd.o g77 -fpic -g -O2 -c bvalue.f -o bvalue.o g77 -fpic -g -O2 -c bvalus.f -o bvalus.o g77 -fpic -g -O2 -c linear.f -o linear.o gcc -I/usr/share/R/include -I/usr/share/R/include -fpic -g...

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y * wts, ny = ny, tol = as.double(tol), coefficients = mat.or.vec(p,...

...behaves pretty much like the Splus version of GAM. Note: this gam library and functions therein are different from the gam function in package mgcv, and both libraries should not be used simultaneously. The gam library allows both local regression (loess) and smoothing spline smoothers, and uses backfitting and local scoring to fit gams. It also allows users to supply their own smoothing methods which can then be included in gam fits. The gam function in mgcv uses only smoothing spline smoothers, with a focus on automatic parameter selection via gcv. Some of the features of the gam library: *...

...behaves pretty much like the Splus version of GAM. Note: this gam library and functions therein are different from the gam function in package mgcv, and both libraries should not be used simultaneously. The gam library allows both local regression (loess) and smoothing spline smoothers, and uses backfitting and local scoring to fit gams. It also allows users to supply their own smoothing methods which can then be included in gam fits. The gam function in mgcv uses only smoothing spline smoothers, with a focus on automatic parameter selection via gcv. Some of the features of the gam library: *...

...solve it but I don't know how to insert numbers at the i-th place. The problem seems to be the "assign" line. Thanks a lot for all hints. for(j in 1:10) { for(i in 1:30) { fit<-lm(annJMSLTv.box~.,data=data.frame(annJMSLTv.box=annJMSLTv.box[,i],allTreerings[,1:15]) backfit<-step(fit,trace=F,steps=1) assign(eval(parse(text=paste("RQuadratStep",j,sep=""))),round(summary(fit)$r.squared, dig=3)) } } -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hor...

This seems to be a recurrent topic, but I don't remember hearing a definitive answer. I also apologies for cross-posting. Say I have a numerical response variable and a bunch of multi-level factors I want to use for modeling. I don't expect factor interaction to be important so there will be no interactions in the model. All this would be a perfect job for ANOVA except for one additional

Hello: I sent this question the other day with the wrong subject heading and couple typos, with no response. So, here I go again, having made those corrections. I would like to estimate, for lack of a better description, a partially additive non-parametric model with the following structure: z~ f(x,y):w1 + g(x,y):w2 + e In other words, I'd like to estimate the marginals with respect to

...and coxph models. So for example, if you have fit a Cox model cox1 <- coxph( Surv(Survival, death) ~ Grade + ns(Age,4) + ns(Size,4)) Then plot.gam(cox1, se=T) will produce three plots, one for each term in the model, with standard error bands. 3) I have implemented the fast versions of backfitting for models consisting of all local regression terms (lo.wam) or all smoothing spline terms (s.wam). Please let me know of any problems with the gam package Trevor Hastie ------------------------------------------------------------------- Trevor Hastie...

Is there a way to add residuals to plots produced by plot.gam in the mgcv package? I'm looking for something like what you get using resid=T in Splus plot.gam. Thanks in advance Toby -----Original Message----- From: Simon Wood [mailto:snw at mcs.st-and.ac.uk] Sent: 23 January, 2002 8:14 PM To: Toby.Patterson at csiro.au Cc: r-help at stat.math.ethz.ch Subject: Re: [R] multiple surfaces in

...and coxph models. So for example, if you have fit a Cox model cox1 <- coxph( Surv(Survival, death) ~ Grade + ns(Age,4) + ns(Size,4)) Then plot.gam(cox1, se=T) will produce three plots, one for each term in the model, with standard error bands. 3) I have implemented the fast versions of backfitting for models consisting of all local regression terms (lo.wam) or all smoothing spline terms (s.wam). Please let me know of any problems with the gam package Trevor Hastie ------------------------------------------------------------------- Trevor Hastie...

Hello, in the paper "Avoiding the effects of concurvity in GAM's .." of Figueiras et al. (2003) it is mentioned that in GLM collinearity is taken into account in the calc of se but not in GAM (-> results in confidence interval too narrow, p-value understated, GAM S-Plus version). I haven't found any references to GAM and concurvity or collinearity on the R page. And I

CRAN (and crantastic) updates this week New packages ------------ * bdoc (1.0) Michael Anderson http://crantastic.org/packages/bdoc This package contains a function that will classify DNA barcodes as well as a few test and reference data sets. * bdsmatrix (1.0) Terry Therneau http://crantastic.org/packages/bdsmatrix This is a special case of sparse matrices, used by coxme and