Ah ... Iirc believe the problem is that you need to explicitly generate the spline basis and then the predicted values via predict.ns and feed that to predict.lme; i.e.
splineBas <- with(rcn10,ns(day,3)) newvals <- data.frame( predict(splineBas, num)) ## then once you've fitted your model: lines(num, predict(fit10, newvals)) I have NOT checked this though, so please post back to me and the list whether this works. -- Bert On Wed, Jun 6, 2012 at 10:38 AM, Ranae <[email protected]> wrote: > I have not been able to get "predict" (or most functions) to run well with > grouped data in nlme. I may not have it coded right, but this is what it > looks like: > > http://r.789695.n4.nabble.com/file/n4632567/spline.txt spline.txt > > library(nlme) > library(splines) > > rootCN<-read.table("spline.txt", header=TRUE) > rootCN$plotF<-as.factor(rootCN$plot) > > rcn10G<-groupedData(N ~ day | plotF, data=rcn10) > > fit10 <- lme( N~ns(day, 3), data = rcn10G) > > plot(augPred(fit10)) > > num<- seq(88,300, len=200) > lines(num, predict(fit10, data.frame(day=num))) > > -Ranae > > > Does > ?predict.ns > not do what you want without having to explicitly manipulate the spline > basis? > > -- Bert > > On Tue, Jun 5, 2012 at 1:56 PM, Ranae <[hidden email]> wrote: > >> 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 >> 1.15676524 0.14509171 0.04459627 0.09334428 >> >> and coefficients for each experimental unit in my experiment: >> >> (Intercept) ns(day, 3)1 ns(day, 3)2 ns(day, 3)3 >> 24 1.050360 -0.42666159 -0.56290877 -0.10714407 >> 13 1.104464 -0.30825350 -0.53311653 -0.05558150 >> 31 1.147878 -0.14548512 -0.78673906 -0.07231781 >> 46 1.177781 -0.22278380 -0.80278177 -0.02321460 >> 15 1.144215 -0.04484519 -0.06084798 0.07633663 >> 32 1.213007 0.00741061 0.03896933 0.15325849 >> 23 1.274615 0.16477514 0.00872224 0.23128320 >> 41 1.215626 0.57050767 0.11415467 0.10608867 >> 43 1.134203 0.48070741 0.72112899 0.18108193 >> 12 1.091422 0.39563632 1.01521528 0.22597459 >> 21 1.100631 0.44589314 0.98526322 0.23535739 >> 35 1.226980 0.82419937 0.39809568 0.16900841 >> >> NOW, I want to write a spline function where I can incorporate these >> coefficients to get the predicted N concentration value for each day. >> However, I am having trouble finding the right spline equation, since >> there >> are many forms on the internets. >> >> I know it won't be a simple one, but can some one direct me to the >> equation >> that would be best to use for ns? >> >> Thanks a lot, >> >> Ranae > > -- > View this message in context: > http://r.789695.n4.nabble.com/Do-YOU-know-an-equation-for-splines-ns-tp4632440p4632567.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
