lme_multilevel_model_summary: Model Summary for Mixed Effect Model

In psycModel: Integrated Toolkit for Psychological Analysis and Modeling in R

Model Summary for Mixed Effect Model

Description

An integrated function for fitting a multilevel linear regression (also known as hierarchical linear regression).

Usage

lme_multilevel_model_summary(
  data,
  model = NULL,
  response_variable = NULL,
  random_effect_factors = NULL,
  non_random_effect_factors = NULL,
  two_way_interaction_factor = NULL,
  three_way_interaction_factor = NULL,
  family = NULL,
  cateogrical_var = NULL,
  id = NULL,
  graph_label_name = NULL,
  estimation_method = "REML",
  opt_control = "bobyqa",
  na.action = stats::na.omit,
  model_summary = TRUE,
  interaction_plot = TRUE,
  y_lim = NULL,
  plot_color = FALSE,
  digits = 3,
  use_package = "lmerTest",
  standardize = NULL,
  ci_method = "satterthwaite",
  simple_slope = FALSE,
  assumption_plot = FALSE,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

data	data.frame
model	lme4 model syntax. Support more complicated model structure from lme4. It is not well-tested to ensure accuracy
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 from a HLM perspective) 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 from a HLM perspective). Factors only need to estimate fixed effect. Support dplyr::select() syntax.
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.
family	a GLM family. It will passed to the family argument in glmer. See ?glmer for possible options.
cateogrical_var	list. Specify the upper bound and lower bound directly instead of using ± 1 SD from the mean. Passed in the form of list(var_name1 = c(upper_bound1, lower_bound1),var_name2 = c(upper_bound2, lower_bound2))
id	the nesting variable (e.g. group, time). Length of 1. Support dplyr::select() syntax.
graph_label_name	optional vector or function. vector of length 2 for two-way interaction graph. vector of length 3 for three-way interaction graph. Vector should be passed in the form of c(response_var, predict_var1, predict_var2, ...). Function should be passed as a switch function (see ?two_way_interaction_plot for an example)
estimation_method	character. ML or REML default is REML.
opt_control	default is optim for lme and bobyqa for lmerTest.
na.action	default is stats::na.omit. Another common option is na.exclude
model_summary	print model summary. Required to be TRUE if you want assumption_plot.
interaction_plot	generate interaction plot. Default is TRUE
y_lim	the plot's upper and lower limit for the y-axis. Length of 2. Example: c(lower_limit, upper_limit)
plot_color	If it is set to TRUE (default is FALSE), the interaction plot will plot with color.
digits	number of digits to round to
use_package	Default is lmerTest. Only available for linear mixed effect model. Options are nlme, lmerTest, or lme4(⁠'lme4⁠ return similar result as lmerTest except the return model)
standardize	The method used for standardizing the parameters. Can be NULL (default; no standardization), "refit" (for re-fitting the model on standardized data) or one of "basic", "posthoc", "smart", "pseudo". See 'Details' in parameters::standardize_parameters()
ci_method	see options in the ⁠Mixed model⁠ section in ?parameters::model_parameters()
simple_slope	Slope estimate at ± 1 SD and the mean of the moderator. Uses interactions::sim_slope() in the background.
assumption_plot	Generate an panel of plots that check major assumptions. It is usually recommended to inspect model assumption violation visually. In the background, it calls performance::check_model().
quite	suppress printing output
streamline	print streamlined output.
return_result	If it is set to TRUE (default is FALSE), it will return the model, model_summary, and plot (plot if the interaction term is included)

Value

a list of all requested items in the order of model, model_summary, interaction_plot, simple_slope

Examples

fit <- lme_multilevel_model_summary(
  data = popular,
  response_variable = popular,
  random_effect_factors = NULL, # you can add random effect predictors here 
  non_random_effect_factors = c(extrav,texp),
  two_way_interaction_factor = NULL, # you can add two-way interaction plot here 
  graph_label_name = NULL, #you can also change graph lable name here
  id = class,
  simple_slope = FALSE, # you can also request simple slope estimate 
  assumption_plot = FALSE, # you can also request assumption plot
  plot_color = FALSE, # you can also request the plot in color
  streamline = FALSE # you can change this to get the least amount of info
)

psycModel documentation built on Nov. 2, 2023, 6:02 p.m.