grouped_glmer: Function to run generalized linear mixed-effects model...
In IndrajeetPatil/groupedstats: Grouped Statistical Analyses in a Tidy Way

Function to run generalized linear mixed-effects model (glmer) across multiple grouping variables.

grouped_glmer(
  data,
  grouping.vars,
  ...,
  output = "tidy",
  tidy.args = list(conf.int = TRUE, conf.level = 0.95, effects = "fixed", conf.method =
    "Wald"),
  augment.args = list()
)

`data`	Dataframe (or tibble) from which variables are to be taken.
`grouping.vars`	Grouping variables.
`...`	Arguments passed on to `lme4::glmer` `formula` a two-sided linear formula object describing both the fixed-effects and random-effects part of the model, with the response on the left of a `~` operator and the terms, separated by `+` operators, on the right. Random-effects terms are distinguished by vertical bars (`"\|"`) separating expressions for design matrices from grouping factors. `family` a GLM family, see `glm` and `family`. `start` a named list of starting values for the parameters in the model, or a numeric vector. A numeric `start` argument will be used as the starting value of `theta`. If `start` is a list, the `theta` element (a numeric vector) is used as the starting value for the first optimization step (default=1 for diagonal elements and 0 for off-diagonal elements of the lower Cholesky factor); the fitted value of `theta` from the first step, plus `start[["fixef"]]`, are used as starting values for the second optimization step. If `start` has both `fixef` and `theta` elements, the first optimization step is skipped. For more details or finer control of optimization, see `modular`. `verbose` integer scalar. If `> 0` verbose output is generated during the optimization of the parameter estimates. If `> 1` verbose output is generated during the individual penalized iteratively reweighted least squares (PIRLS) steps. `nAGQ` integer scalar - the number of points per axis for evaluating the adaptive Gauss-Hermite approximation to the log-likelihood. Defaults to 1, corresponding to the Laplace approximation. Values greater than 1 produce greater accuracy in the evaluation of the log-likelihood at the expense of speed. A value of zero uses a faster but less exact form of parameter estimation for GLMMs by optimizing the random effects and the fixed-effects coefficients in the penalized iteratively reweighted least squares step. (See Details.) `subset` an optional expression indicating the subset of the rows of `data` that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default. `weights` an optional vector of ‘prior weights’ to be used in the fitting process. Should be `NULL` or a numeric vector. `na.action` a function that indicates what should happen when the data contain `NA`s. The default action (`na.omit`, inherited from the ‘factory fresh’ value of `getOption("na.action")`) strips any observations with any missing values in any variables. `offset` this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be `NULL` or a numeric vector of length equal to the number of cases. One or more `offset` terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See `model.offset`. `contrasts` an optional list. See the `contrasts.arg` of `model.matrix.default`. `mustart` optional starting values on the scale of the conditional mean, as in `glm`; see there for details. `etastart` optional starting values on the scale of the unbounded predictor as in `glm`; see there for details. `devFunOnly` logical - return only the deviance evaluation function. Note that because the deviance function operates on variables stored in its environment, it may not return exactly the same values on subsequent calls (but the results should always be within machine tolerance).
`output`	A character describing what output is expected. Two possible options: `"tidy"` (default), which will return the results, or `"glance"`, which will return model summaries.
`tidy.args`	A list of arguments to be used in the relevant `S3` method.
`augment.args`	A list of arguments to be used in the relevant `S3` method.