use_linear_mixed_model: Function for mixed effect regression

In packDAMipd: Decision Analysis Modelling Package with Parameters Estimation Ability from Individual Patient Level Data

Function for mixed effect regression

Description

Function for mixed effect regression

Usage

use_linear_mixed_model(
  param_to_be_estimated,
  dataset,
  fix_eff,
  fix_eff_interact_vars,
  random_intercept_vars,
  nested_intercept_vars_pairs,
  cross_intercept_vars_pairs,
  uncorrel_slope_intercept_pairs,
  random_slope_intercept_pairs,
  package_mixed_model
)

Arguments

param_to_be_estimated	column name of dependent variable
dataset	a dataframe
fix_eff	names of variables as fixed effect predictors
fix_eff_interact_vars	those of the fixed effect predictors that show interaction
random_intercept_vars	names of variables for random intercept
nested_intercept_vars_pairs	those of the random intercept variables with nested effect
cross_intercept_vars_pairs	those of the random intercept variables with crossed effect
uncorrel_slope_intercept_pairs	variables with no correlated intercepts
random_slope_intercept_pairs	random slopes intercept pairs - this is a list of paired variables
package_mixed_model	package to be used for mixed model

Value

result regression result with plot if success and -1, if failure

Examples

datafile <- system.file("extdata", "data_linear_mixed_model.csv",
package = "packDAMipd")
dataset = utils::read.table(datafile, header = TRUE, sep = ",",
na.strings = "NA",
dec = ".", strip.white = TRUE)
result <- use_linear_mixed_model("extro",
  dataset = dataset,
  fix_eff = c("open", "agree", "social"), fix_eff_interact_vars = NULL,
  random_intercept_vars = c("school", "class"),
  nested_intercept_vars_pairs = list(c("school", "class")),
  cross_intercept_vars_pairs = NULL, uncorrel_slope_intercept_pairs = NULL,
  random_slope_intercept_pairs = NULL, package_mixed_model = NA)

packDAMipd documentation built on May 29, 2024, 3:18 a.m.