[R-sig-ME] Model failed to converge

Carrie Perkins cperk @end|ng |rom terpm@||@umd@edu
Thu Sep 12 20:54:18 CEST 2019

Hi Everyone,

I would like to run a mixed effects model in lmer using data from a
salinity tolerance experiment. The experiment had 4 salinity treatments,
and 3 replicates of 48 plant genotypes were planted in each treatment. This
resulted in a total of 144 individuals per treatment, amounting to a grand
total of 576 individuals in the whole experiment.

I tried to run the following model in R:

lmer_model <- lmer(Y~Treatment+(Treatment|Genotype),data=dataframe)

In the formula above, Y refers to the response variable (in this case leaf
length). Treatment refers to the 4 salinity treatments and Genotype refers
to the 48 genotypes represented in the experiment.

However, this model failed to converge. At first I was worried that I do
not have enough degrees of freedom available.

However, when I calculated degrees of freedom it seemed like this must not
be the problem:

Treatment (fixed effect) degrees of freedom: 4 - 1 = 3

Random effects degrees of freedom:
According to Dr. Bolker's FAQ (

"each random term in the model with q components counts for q(q+1)/2
parameters – for example, a term of the form (slope|group) has 3 parameters
(intercept variance, slope variance, correlation between intercept and

Per the statement above, I multiplied 3 X 48 Genotypes = 144 df

This amounts to 3 + 144 = 147 degrees of freedom taken up by the
explanatory variables

Since I had 576 individuals in the experiment, I should have 429 error
degrees of freedom left to play with (576 - 147).

Please let me know if I am mistaken in how I calculated the degrees of
freedom, because I find it very confusing to try to calculate degrees of
freedom of the random effects.

Any assistance in figuring out why the model failed to converge would also
be great!


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