Exploratory Linear Mixed Effect Model Table

Source: R/lme_model_explore.R

[Experimental]
Exploratory analyses for linear regression models with multiple response, predictor, and two-way interaction variables. (lmer models). At the moment, multi-categorical variables are not supported as predictors or interactions (but control is fine). Binary variable should be numeric instead of factor This function also do not supports changing random slopes.

lme_model_explore(
  ...,
  data,
  response_variable,
  predictor_variable,
  two_way_interaction_variable = NULL,
  three_way_interaction_variable = NULL,
  random_effect,
  control_variable = NULL,
  marginal_alpha = 0.1,
  return_result = FALSE,
  print_control = FALSE,
  verbose = TRUE,
  show_p = TRUE,
  show_formula = FALSE
)

Arguments

...

additional parameters pass to lme4::lmer()

data

data.frame

response_variable

Response variable. Support dplyr::select() syntax.

predictor_variable

Pred variable. Support dplyr::select() syntax.

two_way_interaction_variable

Two-way interaction variable. Each two-way interaction variable will interact with each pred variable. Support dplyr::select() syntax.

three_way_interaction_variable

Three-way interaction variable. Each three-way interaction variable will interact with each pred and two-way interaction variables. Support dplyr::select() syntax.

random_effect

The random-effects terms in the format of (|). See lm4::lmer for specifics.

control_variable

Control variables. Support dplyr::select() syntax.

marginal_alpha

Set marginal_alpha level for marginally significant (denoted by .). Set to 0.05 if do not want marginally significant denotation.

return_result

Default is FALSE. If TRUE, it returns the model estimates as a data frame.

print_control

Default is FALSE. If TRUE, print coefficients of control variables.

verbose

Default is TRUE. Set to FALSE to suppress outputs

show_p

Default is TRUE. When TRUE, show the p-value in parenthesis.

show_formula

Default is FALSE. Set to TRUE to show the formula.

Value

data.frame

Examples


lme_model_explore(data = popular,
                  response_variable = c(popular,extrav),
                  predictor_variable = c(texp),
                  two_way_interaction_variable = sex,
                  random_effect = '(1 | class)')
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>   Response  Pred  Interact_pred  Interact_term  Interact_term_coef  Interact_pred_coef           Pred_coef         (Intercept)        df  conditional_r2  marginal_r2
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>    popular  texp            sex       texp*sex  -0.016 (0.025) *     1.577 (0.000) ***   0.063 (0.000) ***   3.502 (0.000) ***  1994.000           0.540        0.344
#>     extrav  texp            sex       texp*sex  -0.018 (0.029) *     0.482 (0.000) ***  -0.066 (0.000) ***   6.047 (0.000) ***  1994.000           0.261        0.160
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> You can drag and resize the R console to view the entire table
#> Note: Coefficient (p-value): + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001