`check_convergence()`

provides an alternative convergence
test for `merMod`

-objects.

## Arguments

- x
A

`merMod`

-object.- tolerance
Indicates up to which value the convergence result is accepted. The smaller

`tolerance`

is, the stricter the test will be.- ...
Currently not used.

## Value

`TRUE`

if convergence is fine and `FALSE`

if convergence
is suspicious. Additionally, the convergence value is returned as attribute.

## Details

### Convergence and log-likelihood

Convergence problems typically arise when the model hasn't converged to a solution where the log-likelihood has a true maximum. This may result in unreliable and overly complex (or non-estimable) estimates and standard errors.

### Inspect model convergence

**lme4** performs a convergence-check (see `?lme4::convergence`

),
however, as as discussed here
and suggested by one of the lme4-authors in
this comment,
this check can be too strict. `check_convergence()`

thus provides an
alternative convergence test for `merMod`

-objects.

### Resolving convergence issues

Convergence issues are not easy to diagnose. The help page on
`?lme4::convergence`

provides most of the current advice about
how to resolve convergence issues. Another clue might be large parameter
values, e.g. estimates (on the scale of the linear predictor) larger than
10 in (non-identity link) generalized linear model *might* indicate
complete separation.
Complete separation can be addressed by regularization, e.g. penalized
regression or Bayesian regression with appropriate priors on the fixed effects.

### Convergence versus Singularity

Note the different meaning between singularity and convergence: singularity indicates an issue with the "true" best estimate, i.e. whether the maximum likelihood estimation for the variance-covariance matrix of the random effects is positive definite or only semi-definite. Convergence is a question of whether we can assume that the numerical optimization has worked correctly or not.

## Examples

```
if (require("lme4")) {
data(cbpp)
set.seed(1)
cbpp$x <- rnorm(nrow(cbpp))
cbpp$x2 <- runif(nrow(cbpp))
model <- glmer(
cbind(incidence, size - incidence) ~ period + x + x2 + (1 + x | herd),
data = cbpp,
family = binomial()
)
check_convergence(model)
}
#> Loading required package: lme4
#> Loading required package: Matrix
#> boundary (singular) fit: see ?isSingular
#> [1] TRUE
#> attr(,"gradient")
#> [1] 8.444895e-05
```