psycModel (version 0.5.0)

lme_multilevel_model_summary: 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
)

Value

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

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)

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
)