Nonlinear models are an entirely different kettle of fish then linear models. You need to specify exactly how the different crops affect the parameters in your growth model. I suggest you consult a local statistician for help (this is not an R question).
Incidentally, depending on the nature of your data -- does it consist of repeated measures of individual plants, for example? -- you may need nlme or nlmer (or other special packages) to fit the data and do inference -- nls makes independence assumptions that aren't appropriate for such data. Your local statistician can help here, too, if needed. But be warned, inference is inherently difficult in such situations. -- Bert On Tue, Feb 8, 2011 at 1:12 PM, Anthony Lawrence Nguy-Robertson <anthony.robert...@huskers.unl.edu> wrote: > I am interested in testing two similar nls models to determine if the lines > are statistically different when fitted with two different data sets; one > corn, another soybean. I know I can do this in linear models by testing for > interactions. See Introductory Statistics with R by Dallgaard p212-218 for > an example. I have two different data sets I am comparing to lai. ci.re > should have very little difference between corn and soybean, while ci.gr, > there should be a difference. If I use the simplistic form described in > Dallgaard (see example in code below) it does not work correctly. What do I > need to do to test for this interaction? Thank you! > > My data is located here: ftp://snrftp.unl.edu/Outgoing/example/ > > Here is my example code: > > ###load data### > data <- read.csv("example.csv", header=TRUE) > eq <- function(x,a,b,c) {a+b*exp(x^c)} > > ##create non-linear models## > nls.ci.re <- nls(lai~eq(ci.re,a,b,c),data=data, start=c(a=1,b=1,c=1)) > nls.ci.gr <- nls(lai~eq(ci.gr,a,b,c),data=data, start=c(a=1,b=1,c=1)) > > #create non-linear models for corn# > nls.ci.re.corn <- nls(lai~eq(ci.re,a,b,c),data=subset(data,crop=="corn"), > start=c(a=1,b=1,c=1)) > nls.ci.gr.corn <- nls(lai~eq(ci.gr,a,b,c),data=subset(data,crop=="corn"), > start=c(a=1,b=1,c=1)) > > #create non-linear models for soybean# > nls.ci.re.soybean <- > nls(lai~eq(ci.re,a,b,c),data=subset(data,crop=="soybean"), > start=c(a=1,b=1,c=1)) > nls.ci.gr.soybean <- > nls(lai~eq(ci.gr,a,b,c),data=subset(data,crop=="soybean"), > start=c(a=1,b=1,c=1)) > > #test interactions according to Introductory Statistics with R by Dalgaard > p. 213# > corn.soybean.interactions.ci.re.a <- > abs((summary(nls.ci.re.corn)$coeff[1,1]- > summary(nls.ci.re.soybean)$coeff[1,1])/ > sqrt(summary(nls.ci.re.corn)$coeff[1,2]^2+ > summary(nls.ci.re.soybean)$coeff[1,2]^2)) > > corn.soybean.interactions.ci.gr.a <- > abs((summary(nls.ci.gr.corn)$coeff[1,1]- > summary(nls.ci.gr.soybean)$coeff[1,1])/ > sqrt(summary(nls.ci.gr.corn)$coeff[1,2]^2+ > summary(nls.ci.gr.soybean)$coeff[1,2]^2)) > > corn.soybean.interactions.ci.re.a.p.value <- > pt(corn.soybean.interactions.ci.re.a,df=summary(nls.ci.re.corn)$df[2],lower.tail=FALSE) > corn.soybean.interactions.ci.gr.a.p.value <- > pt(corn.soybean.interactions.ci.gr.a,df=summary(nls.ci.gr.corn)$df[2],lower.tail=FALSE) > > ______________________________________________ > R-help@r-project.org 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 ______________________________________________ R-help@r-project.org 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.
