# [R] Help to solve modeling problem with gamm

Bert Gunter bgunter@4567 @end|ng |rom gm@||@com
Fri Feb 28 16:22:39 CET 2020

```Wrong list.

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Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

On Fri, Feb 28, 2020 at 12:25 AM José Antonio García Pérez <
garci95 using hotmail.com> wrote:

> I conducted an experiment where earthworms were subjected to two
> treatments, with and without herbicide in the soil. Biomass measurements
> were taken every 12 days for 398 days and the biomass growth curves as a
> function of time were plotted.
>
> There was clearly a non-linear growth pattern such that an additive mixed
> effects model was proposed to model the behavior of biomass as a function
> of time and treatments.
>
> When plotting the residuals a clear cone-shaped pattern was observed,
> therefore a series of additive models were proposed sequentially to deal
> with violations of the assumption of homogeneity. Below we can see the
> models with the following names: M.1; M.2; M.3; M.4
>
>
>
> lmc <- lmeControl (niterEM = 5000, msMaxIter = 1000)
>
> f1 <- formula (Biomass ~ Treat + s (Time, by = Treat))
>
>
>
> M.1 <-gamm (f1, random = list (fcajita = ~ 1), method = "REML", control =
> lmc, data = Acorticis)
>
>
>
> #This first model uses the experimental box factor (i.e. fcajita) as the
> random element of the model. This random effects model assumes homogeneity
> between the experimental boxes and within them over time
>
>
>
> M.2 <-gamm (f1, random = list (fcajita = ~ 1), method = "REML", control =
> lmc, data = Acorticis, weights = varIdent (form = ~ 1 | fcajita))
>
>
>
> #This second model assumes heterogeneity between boxes, but homogeneity
> within each box over time
>
>
>
> M.3 <- gamm (f1, random = list (fcajita = ~ 1), method = "REML", control =
> lmc, data = Acorticis, weights = varExp (form = ~ Time10))
>
>
>
> #The third model assumes homogeneity between boxes but heterogeneity
> within each box over time
>
>
>
> Finally, we decided to model the heterogeneity using the 'varComb'
> function in order to combine the variances where the model allows
> heterogeneity between the experimental boxes and heterogeneity within the
> experimental boxes over time:
>
>
>
> M.4 <- gamm (f1, random = list (fcajita = ~ 1), data = Acorticis, method =
> "REML", control = lmc, weights = varComb (varIdent (form = ~ 1 | fcajita),
> varPower (form = ~ Time10)))
>
>
>
> The first three models executed perfectly and the following values ​​of
> the AIC indicator were obtained:
>
> > AIC (M.1 \$ lme, M.2 \$ lme, M.3 \$ lme)
>
>
>
>         df       AIC
>
>
>
> M.1     8        379.6464
>
>
>
> M.2    15        309.5736
>
>
>
> M.3     9        310.4828
>
>
>
> Unfortunately, the execution of the M.4 model failed and the following
> error message was obtained:
>
>
>
> Error in environment (attr (ret \$ lme \$ modelStruct \$ varStruct,
> "formula")) <-. GlobalEnv:
>
> attempt to set an attribute on NULL
>
>
>
> A final model I tried was M5:
>
> M.5 <- gamm(f1, random = list(fcajita =~ 1), data = Acorticis, method =
> "REML", control = lmc, weights = varComb(varIdent(form = ~1|fcajita),
> varExp(form =~ Time10|fcajita)))
>
> and this time I got the following error message:
>
> Error in lme.formula(y ~ X - 1, random = rand, data = strip.offset(mf),  :
>
>   nlminb problem, convergence error code = 1
>
>   message = function evaluation limit reached without convergence (9)
>
>
> In logLik.reStruct(object, conLin) :
>
>   Singular precision matrix in level -1, block 1
>
>
>
> My question is: Could someone help me fix these problems to run the M.4
> and M.5 models?
>
>
>         [[alternative HTML version deleted]]
>
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