在家庭=高斯（身份链接）的 glmer() 中调用错误

Question

I am trying to fix a glmm for a dataframe with 53 obs. of 17 variables. All variables are standardized, but don't follow the normal distribution and have no missing values. The str() of the data frame is something like below.

• species : Factor w/ 19 levels "spp1","spp2",..: 5 18 12 15 19 4 6 14 16 5 ...
• association : Factor w/ 4 levels "assoA","assoB",..: 1 1 2 2 2 3 3 4 4 1 ...
• site : Factor w/ 2 levels "site1","site2": 1 1 1 1 1 1 1 1 1 2 ...
• obs.no : int 1 1 1 1 1 1 1 1 1 1 ...
• trait1: num 0.652 0.428 0.535 0.389 0.486 ...
• trait2 : num 0.135 0.16 0.134 0.142 0.159
• (clipped trait3 to 13)

I executed the following code to check the significance between sites an association classes for the given trait.

model1= glmer(trait1 ~ association+site+ (1 | species),data=df6,family=gaussian)

and received the error given below.

In glmer(trait1 ~ association+site+ (1 | species),data=df6, : calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is deprecated; please call lmer() directly

After this I want to estimate parameters with Gauss-Hermite quadrature. Any recommendation to fix this error and code to execute Gauss-Hermite quadrature is very much appreciated.

Answer 1

You actually posted the answer. Use lmer, not glmer:

model1 = lmer(trait1~association+site+(1|species), data=df6)

Answer 2

Clarifying: the reason that glmer(..., family = gaussian(link = "identity")) is not allowed (and that lme4 insists you use lmer(...) instead) is that there is no point using numerical (Gauss-Hermite) quadrature for a linear mixed model (which is exactly the special case of a GLMM with a Gaussian response and an identity link); in this case the integral can be expressed in closed form as a penalized least-squares problem (conditional on the random-effects variance/covariance parameters): seeBates et al. 2015 .