`glme_model.Rd`

Fit a generalized linear mixed effect model using `lme4::glmer()`

. This function is still in early development stage.

```
glme_model(
data,
model = NULL,
response_variable,
random_effect_factors = NULL,
non_random_effect_factors = NULL,
family,
two_way_interaction_factor = NULL,
three_way_interaction_factor = NULL,
id,
estimation_method = "REML",
opt_control = "bobyqa",
na.action = stats::na.omit,
quite = FALSE
)
```

- data
data frame

- model
`lme4`

model syntax. Support more complicated model. Note that model_summary will only return fixed effect estimates. This is not tested.- response_variable
DV (i.e., outcome variable / response variable). Length of 1. Support

`dplyr::select()`

syntax.- random_effect_factors
random effect factors (level-1 variable for HLM people) Factors that need to estimate fixed effect and random effect (i.e., random slope / varying slope based on the id). Support

`dplyr::select()`

syntax.- non_random_effect_factors
non-random effect factors (level-2 variable for HLM people). Factors only need to estimate fixed effect. Support

`dplyr::select()`

syntax.- family
a GLM family. It will passed to the family argument in glmer. See

`?glmer`

for possible options.- two_way_interaction_factor
two-way interaction factors. You need to pass 2+ factor. Support

`dplyr::select()`

syntax.- three_way_interaction_factor
three-way interaction factor. You need to pass exactly 3 factors. Specifying three-way interaction factors automatically included all two-way interactions, so please do not specify the two_way_interaction_factor argument. Support

`dplyr::select()`

syntax.- id
the nesting variable (e.g. group, time). Length of 1. Support

`dplyr::select()`

syntax.- estimation_method
character.

`ML`

or`REML`

default to`REML`

.- opt_control
character. default is

`bobyqa`

. See`?lme4::glmerControl`

for more options.- na.action
default is

`stats::na.omit`

. Another common option is`na.exclude`

- quite
suppress printing output

An object of class `glmerMod`

representing the linear mixed-effects model fit.

```
fit <- glme_model(
response_variable = incidence,
random_effect_factors = period,
family = "poisson", # or you can enter as poisson(link = 'log')
id = herd,
data = lme4::cbpp
)
#> Warning: The following columns are coerced into numeric: herd, period
#> Fitting Model with glmer:
#> Formula = incidence ~ period + (1 + period | herd)
#> Family = poisson
```