dredge an nlme model with multiple parameters

Question

Disclaimer:

• Firstly, I have tried to create a mock dataset for a reproducible example, but whenever I create a random set or take a portion of the real data, the model does not converge. I can't see a way of providing the necessary material for a reproducible example without the real data. If deemed necessary and someone is willing to help, I can post the real data.

• I think this post is addressing a similar idea, but the comments do not explain how to go about 'hacking' this particular coding challenge.

Adding covariates to nlme model in R

• I am aware of the issues/debate/literature relating to data-dredging, but would like to figure this out regardless.

Overview:

Modeling the percent cover of a sessile species (dependent variable) with explanatory abitoic variables (independent variable) from multiple sites.

With the help of others, I have created the following logistic growth model using the nlme package in R. Percent cover is reliant upon four model parameters. Days is just the time in days when measurements were taken. MaxPop = the maximum population (percent cover - between 0 -100%) per a given site. Days50 = Time in days it take for 50 % of the MaxPop. Hill = Controls the lag of growth and slope through the inflection point. Each of MaxPop, Days50 and Hill are made up of independent abiotic variables, temperature, water acceleration, salinity and pH (centred where necessary).

Global_Model <- nlme(PercentCover ~ I(100 *((inv.logit(exp(MaxPop) *Days^Hill/(exp(Days50)^Hill + Days^Hill))-0.5)*2)),
                 data = Data15,
                 fixed = list(MaxPop ~ I(Temp-14) + Accel + I(Sal-30) + I(pH-8), LogDays50 ~ I(Temp-14) + Accel + I(Sal-30) +I(pH-8), Hill ~ I(Temp-14) + Accel + I(Sal-30) + I(pH-8)),
                 random = MaxPop ~1|Site,
                 start = c(11, -1, -1925,  1.6, 0, 7.4, -0.2, -776, 0.6, 0,  3.9, -.004, 2009, -0.3, 0), control = nlmeControl(maxIter=100), verbose = T)

I began with a predefined set of theoretic models, but I proceeded to develop a global model as all of the abiotic variables were preselected and all have a theoretic reason for inclusion in the model. Hopefully that will reduce the risk of overfitting with a global model.

With the MuMIn package I attempted to use dredge(Global_Model) to run all nested models within the global model, but get "Error in FUN(X[[i]], ...) : subscript out of bounds"

I am not sure how to go about attempting this as each model parameter (MaxPop, Day50, and Hill) is composed of the same four independent variables. I would like to run a dredge that tests every permutation of IVs intra and inter-parameter.

Questions:

Is it possible to carry out a 'dredge-like' process in nlme?

How would one go about it?

______

I would greatly appreciate some advise.

Cheers!

Answer 1

You would need to write a "wrapper" function around nlme that maps a linear formula (such as y ~ x1 + x2 + x3), to the three nlme components: fixed and random (I assume you want to keep the same model for all), and then feed it to dredge. It's doable, but tricky.