Linear mixedeffects model (lmer
) across multiple grouping
variables.
grouped_lmer(
data,
grouping.vars,
...,
output = "tidy",
tidy.args = list(conf.int = TRUE, conf.level = 0.95, effects = "fixed", conf.method =
"Wald"),
augment.args = list()
)
Arguments
data 
Dataframe (or tibble) from which variables are to be taken. 
grouping.vars 
Grouping variables. 
... 
Arguments passed on to lme4::lmer
formula a twosided linear formula object describing both the
fixedeffects and randomeffects part of the model, with the
response on the left of a ~ operator and the terms, separated
by + operators, on the right. Randomeffects terms are
distinguished by vertical bars ( ) separating expressions
for design matrices from grouping factors. Two vertical bars
( ) can be used to specify multiple uncorrelated random
effects for the same grouping variable.
(Because of the way it is implemented, the  syntax works
only for design matrices containing numeric (continuous) predictors;
to fit models with independent categorical effects, see dummy
or the lmer_alt function from the afex package.)
REML logical scalar  Should the estimates be chosen to
optimize the REML criterion (as opposed to the loglikelihood)?
start a named list of starting values for the
parameters in the model. For lmer this can be a numeric
vector or a list with one component named "theta" .
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.
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.
Prior weights are not normalized or standardized in
any way. In particular, the diagonal of the residual covariance
matrix is the squared residual standard deviation parameter
sigma times the vector of inverse weights .
Therefore, if the weights have relatively large magnitudes,
then in order to compensate, the sigma parameter will
also need to have a relatively large magnitude.
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 .
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. 
Value
A tibble dataframe with tidy results from a linear mixedeffects
model. Note that pvalue is computed using parameters::p_value
.
Examples
#> Warning: unable to evaluate scaled gradient
#> Warning: Model failed to converge: degenerate Hessian with 1 negative eigenvalues
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> Warning: unable to evaluate scaled gradient
#> Warning: Model failed to converge: degenerate Hessian with 1 negative eigenvalues
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> # A tibble: 24 x 10
#> year effect term estimate std.error statistic conf.low conf.high
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1952 fixed (Intercept) 0.201 0.743 0.270 1.26 1.66
#> 2 1952 fixed scale(gdpPercap) 0.900 0.742 1.21 0.555 2.35
#> 3 1957 fixed (Intercept) 0.190 0.377 0.504 0.548 0.928
#> 4 1957 fixed scale(gdpPercap) 0.756 0.384 1.97 0.00255 1.51
#> 5 1962 fixed (Intercept) 0.226 0.505 0.447 0.765 1.22
#> 6 1962 fixed scale(gdpPercap) 0.547 0.273 2.01 0.0130 1.08
#> 7 1967 fixed (Intercept) 0.234 0.307 0.764 0.367 0.836
#> 8 1967 fixed scale(gdpPercap) 0.269 0.0779 3.46 0.117 0.422
#> 9 1972 fixed (Intercept) 0.241 0.307 0.783 0.362 0.843
#> 10 1972 fixed scale(gdpPercap) 0.378 0.109 3.45 0.163 0.593
#> p.value significance
#> <dbl> <chr>
#> 1 0.787 ns
#> 2 0.225 ns
#> 3 0.615 ns
#> 4 0.0492 *
#> 5 0.655 ns
#> 6 0.0447 *
#> 7 0.445 ns
#> 8 0.000548 ***
#> 9 0.434 ns
#> 10 0.000556 ***
#> # ... with 14 more rows
# }