[R-sig-ME] Non-linear mixed model

[Jesus Maria Frias Celayeta]

Hi Juan,

You probably want to have a look at the "fixed" argument in the nlme help
page.

If the variable that gives you the indicator of the climate region is
Clima1 you probably will need something like

n2 <- nlme(diam ~ thy * exp(thq * (temp - thx)^2 + thc * (temp - thx)^3),
           fixed = list(thy + thq +  thc ~ 1, thx~Clima1),
           random = thy + thq + thx + thc ~ 1 | race,
           start = c(thy = 5.5, thq = -0.08, thc = -0.01, thx.clima1=xxx,
thx.clima2=xxx...,thx.climan=xxx),
           data = df)

Note that you will need estimates for your baseline ths and for the
analytical contrasts defined.

If you want to see if the addition of Clima1 to the model is giving you joy
then you need
anova(n1,n2)

and from  summary(n2) you will see if any of your climate regions are
different from the baseline control.

Alternatively 1) you may choose different contrast .2) you can give a try
to multiple comparisons. Both are well documented in this list (or the
R-help).

all the best,

Jesus

Jesus



On 14 September 2017 at 13:57, Juan Pablo Edwards Molina <
edwardsmolina at gmail.com> wrote:

> Dear list members,
>
> I´m trying to test the effect of the climate region classification on
> the in vitro growth of a sample (n =20) of fungus races.  I grew them
> in several temperatures (20, 22, 25, 28, 31) that I knew they could
> have the maximum growth:
>
>  race state   Clima1  Kopp Kopp2   temp   rep    diam
>  1      TO       F          Aw             B     20        1     4.4
>  1      TO       F          Aw             B     20        2     4.1
>  1      TO       F          Aw             B     20        3     4.3
>  1      TO       F          Aw             B     22        1     4.8
>  1      TO       F          Aw             B     22        2     4.5
>  1      TO       F          Aw             B     22        3     4.4
> ..
>
>
> The approach that I´m considering is to fitt a non- linear model:
>
> diam ~ thy * exp (thq*(temp-thx)² + thc*(temp-thx)³)
>
> # thx: Optimum temperature
> # thy: Diameter at optimum
> # thq: Curvature
> # thc: Skewness
>
> Since I have particular interest on "thx": How should I include the
> effect of my climate classiification variables on that coefficient?
>
> This is my try in nlme:
>
> df <- groupedData(diam~temp|race, data=d, order=FALSE)
>
> n1 <- nlme(diam ~ thy * exp(thq * (temp - thx)^2 + thc * (temp - thx)^3),
>            fixed = thy + thq + thx + thc ~ 1,
>            random = thy + thq + thx + thc ~ 1 | race,
>            start = c(thy = 5.5, thq = -0.08, thx = 25, thc = -0.01),
>            data = df)
>
> The overall model converged and this is the summary:
>
> ======================================================
> Nonlinear mixed-effects model fit by maximum likelihood
> Model: diam ~ thy * exp(thq * (temp - thx)^2 + thc * (temp - thx)^3)
> Data: df
>        AIC      BIC    logLik
>   619.2972 652.1712 -301.6486
>
> Random effects:
> Formula: list(thy ~ 1, thx ~ 1)
> Level: race
>
> Structure: General positive-definite, Log-Cholesky parametrization
>          StdDev        Corr
> thy      0.00002186836 thy
> thx      0.00001466761 0
> Residual 0.47302438540
>
> Fixed effects: thy + thq + thx + thc ~ 1
>         Value        Std.Error         DF   t-value        p-value
> thy   5.456386   0.03598277  427   151.63885  0.0000
> thq  -0.011081   0.00043084  427   -25.71992  0.0000
> thx  25.908119  0.17218070  427   150.47052  0.0000
> thc   0.000458   0.00015103  427     3.03271    0.0026
>  Correlation:
>     thy    thq    thx
> thq -0.567
> thx  0.217  0.289
> thc -0.231 -0.192 -0.924
>
> Standardized Within-Group Residuals:
>         Min          Q1         Med          Q3         Max
> -3.53487665 -0.64456754  0.06126737  0.67103195  2.17757223
>
> Number of Observations: 450
> Number of Groups: 20
>
> =========================================================
>
> Thanks in advance... Any help would be very helpful!
>
> J. Edwards
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models




-- 

*Jesús María Frías Celayeta, PhD CFS*

 *Ceannaire Acadúil, Institiúid na hInbhuanaitheachta Comhshaoil agus na
Sláinte | Academic Leader, Environmental Sustainability and Health
Institute*

*Institiúid Teicneolaíochta Bhaile Átha Cliath | Dublin Institute of
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