search for: smoothcon

...quot;one","two"), class="factor") yy <- predict(f.ug, newdata=data.frame(m=mm,x=xx)) bool <- mm=="one" lines(xx[bool],yy[bool], lty=2) # show effect of extrapolation lines(xx[!bool],yy[!bool], lty=2) # this is where I start running into trouble sm<-smoothCon(s(x,k=10,by=m,bs="cr"),dat,knots=NULL)[[1]] FF<-mono.con(sm$xp); # get constraints G<-list(y=y, w=rep(1, n), X=sm$X, C=matrix(0,0,0), S = sm$S, off = 0, sp=f.ug$sp, p=sm$xp, Ain = FF$A,...

...*4-1;x<-sort(x); f<-exp(4*x)/(1+exp(4*x));y<-f+rnorm(100)*0.1;plot(x,y) dat<-data.frame(x=x,y=y) # Show regular spline fit (and save fitted object) f.ug<-gam(y~s(x,k=10,bs="cr"));lines(x,fitted(f.ug)) # Create Design matrix, constraints etc. for monotonic spline.... sm<-smoothCon(s(x,k=10,bs="cr"),dat,knots=NULL)[[1]] F<-mono.con(sm$xp); # get constraints G<-list(X=sm$X,C=matrix(0,0,0),sp=f.ug$sp,p=sm$xp,y=y,w=y*0+1) G$Ain<-F$A;G$bin<-F$b;G$S<-sm$S;G$off<-0 p<-pcls(G); # fit spline (using s.p. from unconstrained fit) fv<-Predict.matrix(sm,d...

...ing tensor product smooths. See ?fixDependence and ?gam.side for details. * By default all smooths are now parameterized to be centred, without requiring additional constraints (this is automatic and applies also to user defined smooths). The old behaviour is still available as an option. See ?smoothCon for details. (This should be user transparent.) * Smoothing parameter initialization has been modified for better performance with tensor product smooths. See ?initial.sp. * By default, tensor product smooths have been modified to use more interpretable penalties. See ?te for details. This lea...

...ing tensor product smooths. See ?fixDependence and ?gam.side for details. * By default all smooths are now parameterized to be centred, without requiring additional constraints (this is automatic and applies also to user defined smooths). The old behaviour is still available as an option. See ?smoothCon for details. (This should be user transparent.) * Smoothing parameter initialization has been modified for better performance with tensor product smooths. See ?initial.sp. * By default, tensor product smooths have been modified to use more interpretable penalties. See ?te for details. This lea...

Dear all, for demonstration purposes I want to display the basis functions used by a thin plate regression spline in a gamm model. I've been searching the help files, but I can't really figure out how to get the plots of the basis functions. Anybody an idea? Some toy code : require(mgcv) require(nlme) x1 <- 1:1000 x2 <- runif(1000,10,500) fx1 <- -4*sin(x1/50) fx2 <-

...exp(4*x));y <- f+rnorm(100)*0.1;plot(x,y)  dat <- data.frame(x=x,y=y)  ## Create a spline basis and penalty, making sure there is a knot  ## at the constraint point, (0 here, but could be anywhere)  knots <- data.frame(x=seq(-1,3,length=9)) ## create knots  ## set up smoother...  sm <- smoothCon(s(x,k=9,bs="cr"),dat,knots=knots)[[1]]  ## 3rd parameter is value of spline at knot location 0,  ## set it to 0 by dropping...  X <- sm$X[,-3]        ## spline basis  S <- sm$S[[1]][-3,-3] ## spline penalty  off <- y*0 + .6      ## offset term to force curve through (0, .6)  ##...