[R] Interactions in a nls model

[Bert Gunter]

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.robertson at 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 at 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