On Tue, Feb 8, 2011 at 11:43 PM, Gabor Grothendieck <ggrothendi...@gmail.com> wrote: > On Tue, Feb 8, 2011 at 4:12 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) > > There is an anova.nls method: anova(model1, model2)
Just to be clear the two models would each include both groups -- one model would assume the parameters are the same for the two groups so it would have 3 parameters and the other model would allow them to be different (up to 6 parameters depending on how many parameters you wish to allow to be different between the two groups) -- these are not the models shown above. -- Statistics & Software Consulting GKX Group, GKX Associates Inc. tel: 1-877-GKX-GROUP email: ggrothendieck at gmail.com ______________________________________________ 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.
