# Best practice MLR vs GLM(M)

I am modelling timeseries of organism traits (7 different traits in total) using GLM(M)s in R. The data was collected at very irregular intervals and from 6 different locations. For every location 5-10 animals were sampled and some locations have been sampled multiple times whereas others just once or twice. I am interested to see if Trait significantly changes with Year.

I decided to use Location as a random variable so that the model is of the form:

glmer(Trait~ Year + Var1 + (1|Location),
data = data)


The model diagnostics lock good and don't give any reasons for concerns to me.

I did just for the sake of it model the same traits using multiple linear regression, this time having Location as a fixed term. This required the log transformation of Traits

lm(log(Trait) ~ Year + Var1 + Location, data = data)


Also for these models the diagnostics look good. Both models suggest the same trend in the data.

Now the problem that I am facing is decide what model I should use? In the literature it is commonly mentioned that the simplest possible statistical tool should be favored over more complicated ones which suggests to me I should favor the linear regression but one is also discouraged to transform data to fit the model.

• How many locations do you have? Commented Oct 14, 2020 at 16:49
• 6 different locations Commented Oct 14, 2020 at 16:52
• Note that there is a difference between a model with a transformed response & with a link function for the mean (see: Why is GLM different than an LM with transformed variable). These can't both be right. Commented Oct 14, 2020 at 18:32
• @gung-ReinstateMonica is quite right. I have updated my answer. You need to be consistent in what you are modelling. Commented Oct 14, 2020 at 18:52

6 is considered to be at or very near the lower limit for fitting random intercepts. In your case I would suggest the linear model with fixed effects for location. If this is for publication / presentation then I would also mention that you have fitted a mixed model with random intercepts for location, and found that the inferences were very similar.

Also, note that, to be consisent, you should fit the models:

glm(Trait ~ Year + Var1 + Location, family = gaussian(link = "log"), data = data)


and

glmer(Trait ~ Year + Var1 + (1|Location),family = gaussian(link = "log"), data = data)


OR,

lm(log(Trait) ~ Year + Var1 + Location, data = data)


and

lmer(log(Trait) ~ Year + Var1 + (1|Location), data = data)

• Thank you very much! This answer is great. Commented Oct 15, 2020 at 8:02
• You're welcome. Happy to help ! Commented Oct 15, 2020 at 8:03