I am trying to plot a GLMM of Gaussian data.

eg. Diversity measure = Gaussian Data (8 decimal points) = y

Variables:

Sample_ID = A, B, C - I want to include this as a random effect.

Type = D, E, F, G (fixed effect)

Colour = green, yellow, blue (fixed effect)

Time in hours (Fixed effect / continuous data)

So something like:

model <- glmm(chao1_mean ~ Type + Colour + Time + (1|Sample_ID), family = gaussian, data = Data)

I have tried using glmm function but I can only have families as 'binomial', 'poisson', or 'bernoulli'. So I am assuming I have to use a different version of the glmm.

I've tried using "mixed_models" from "GLMMadaptive" and also "lme" from "lme4" the first gives me an error and tells me to use the "lme" and "lme" just gives me errors. I thought the glmmADMB might be the correct package but it is unavailable on my R study version.

Am I on the correct lines or is there a package for modelling Gaussian data for GLMMs that I've missed.

I am also unsure if (1|Sample_ID) is the correct formulation.

Many thanks in advance.