Thanks for your help and suggestions.
Thats exactly what I did, i.e. a sin () and cos () code using daily
observations of soil temperature, and putting a variable vegetation types as
a covariate. Therefore, I can build contrasts to compare parameters of the
model such as an intercept, amplitude and phase between different vegetation
types.
If I understand correctly, you mean to do the similar model but using average
monthly data of soil temperature instead of daily soil temperature? Still, I
think that the model will allow us comparison of the parameters of the model
such as an intercept, amplitude and phase between the vegetation types, but not
the monthly soil temperature between different vegetation types
Thanks for your help and I am sorry if I did not understand it correctly or did
not describe the experiment clearly.
Julia> Date: Sun, 6 Jul 2008 08:38:51 -0700> From: [EMAIL PROTECTED]> To:
[EMAIL PROTECTED]> CC: r-help@r-project.org> Subject: Re: [R] Test for multiple
comparisons: Nonlinear model, autocorrelation?> > If I had only a very limited
time to do this, I might include > 'month' as another effect, probably coded as
'sin' and 'cos' on an > annual cycle rather than as 12 individual Indicators.
This would allow > you to explore not only main effects but interactions with
plots. > > Before I did that, however, I'd want to generate more plots of >
data, residuals, and coefficients. For example, > qqnorm(resid(fm1Soy.nlme),
datax=TRUE) displayed an S shape that > indicated inhomogeniety of variance.
This suggests that there is > something else to be modeled in these data. I
would next try plotting > residuals by 'month'. I'd also plot the averages and
standard > deviations by 'month'. This might tell me if I only need to add a
fixed > annual cycle, and how much of a Fourier series approximation to add. If
> the standard deviations show a pattern, it suggests I need to model >
heteroscedasticity. For that see '?varClasses' and the corresponding >
information in a book by Pinheiro and Bates (2000), mentioned on that > help
page. You can do 'anova' for any of these effects. [To test > changes in fixed
effects, you will need to use method='ML', as discussed > in a book by Pinheiro
and Bates (2000).]> > Hope this helps. > Spencer> > J S wrote:> >> > Thanks.
Here is a similar example from a book by Pinheiro and Bates > > (2000, chapter
6):> >> > > >> > library(nlme)> > data(Soybean)> >> > fm1Soy.lis <- nlsList(
weight ~ SSlogis(Time, Asym, xmid, scal),> > data = Soybean )> > fm1Soy.nlme <-
nlme( fm1Soy.lis )> >> > > >> > *If we would like to make comparisons among the
years we could just > > simply involve years as a covariate, and later we could
use L argument > > to ANOVA to could compute contrasts. *> >> > > >> > soyFix
<- fixef( fm1Soy.nlme )> > fm2Soy.nlme <- update( fm1Soy.nlme,> > fixed = Asym
+ xmid + scal ~ Year,> > start = c(soyFix[1], 0, 0, soyFix[2], 0, 0, soyFix[3],
0, 0) )> >> > * *> >> > *My question is: How can I compare variety of soybeans
in a separate > > month, i.e. if there was a difference in weight of soybeans F
and P in > > first month,
in twelve month?*> >> > > >> > The dataset
Soybean:> >> > Plot Variety Year Time weight> >> > 1 1988F1 F 1988 14
0.106000> >> > 2 1988F1 F 1988 21 0.261000> >> > 3 1988F1 F 1988 28 0.666000>
>> > 4 1988F1 F 1988 35 2.110000> >> > 5 1988F1 F 1988 42 3.560000> >> >
.> >>
> 407 1990P8 P 1990 30 1.478330> >> > 408 1990P8 P 1990 37 2.601667> >> > 409
1990P8 P 1990 43 6.343330> >> > 410 1990P8 P 1990 51 6.131670> >> > 411 1990P8
P 1990 64 16.411700> >> > 412 1990P8 P 1990 79 16.946700> >> > > >> > 1)
Involving months and variety as a covariates will probably > > create too many
parameters for the model?> >> > 2) Is it possible to use some test for
comparisons, lets say t > > test? Perhaps not in case the data are dependent
(i.e. previous > > measurement is dependent on the next measurement, i.e. there
is > > temporal correlation (as in my study of Soil temperature)? What is an >
> alternative suggestion?> >> > > >> > Thanks,> >> > Julia> >> >> >> > > Date:
Fri, 4 Jul 2008 17:36:29 -0700> > > From: [EMAIL PROTECTED]> > > To: [EMAIL
PROTECTED]> > > CC: r-help@r-project.org> > > Subject: Re: [R] Test for
multiple comparisons: Nonlinear model, > > autocorrelation?> > >> > > The
question seems too general for me to offer specific suggestions.> > >> > > What
problem are you trying to solve that you think 'multiple> > > comparisons' will
answer?> > >> > > Can you produce a similar problem that is completely
self-contained> > > example that eliminates complexity that may not be needed
to understand> > > your question (similar to the 'Auxiliary Problem' technique
in "How to> > > Solve It", http://en.wikipedia.org/wiki/How_to_Solve_It)? If
you > > can, it> > > may lead you to a solution. If you get such an example but
still can't> > > see a solution, send that example to this list (following the
advice in> > > the posting guide http://www.R-project.org/posting-guide.html).
The> > > simpler the example, the more likely someone on this list will reply>
> > quickly with a useful suggestion.> > >> > > I know this doesn't solve your
problem, but I hope it helps.> > > Spencer> > >> > > J S wrote:> > > > Dear R
community,> > > >> > > > I have a nonlinear model describing average daily soil
> > temperature. What test should I use to compare differences in soil > >
temperature of the two studied vegetation types depending upon month?> > > >> >
> > Building linear contrasts for the developed nonlinear model does > > not
help since this model does not include variable Months (only > > Days).> >
> >> > > > 1) Just a Students test is not probably an option because I would >
> violate an assumption of independency, since the daily soil > > temperature
observations have high autocorrelation. Or maybe I could > > average the
observations for each month and then use this test since I > > have
observations for a few years, and it might overcome the problem > > of
independency?> > > >> > > > 2) Should I develop a second nonlinear model with
months instead > > of days, but it would considerably increase a number of
parameters in > > the model...> > > >> > > > Or:> > > > 3) ?> > > >> > > >
Thanks for your help,> > > > Julia> > > >
_________________________________________________________________> > > > Its a
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