jammGLM: GLM Mediation Model

In jamovi-amm/jamm: Advanced Mediational Models

GLM Mediation Model

Description

GLM mediation model

Usage

jammGLM(
  data,
  dep = NULL,
  mediators = NULL,
  factors = NULL,
  covs = NULL,
  modelTerms = NULL,
  ciType = "standard",
  ciWidth = 95,
  bootN = 1000,
  contrasts = NULL,
  showRealNames = TRUE,
  showContrastCode = FALSE,
  bogus = FALSE,
  simpleScale = "mean_sd",
  cvalue = 1,
  percvalue = 25,
  simpleScaleLabels = "labels",
  scaling = NULL,
  tableOptions = list("beta", "component"),
  pathOptions = list("suggested"),
  mediatorsTerms = list(list()),
  moderatorsTerms = list(list()),
  formula
)

Arguments

data	the data as a data frame
dep	a string naming the dependent variable from data, variable must be numeric. Not useful if formula is used.
mediators	a vector of strings naming the mediators from data. Not useful if formula is used.
factors	a vector of strings naming the fixed factors from data. Not useful if formula is used or the variable is already a factor in the data
covs	a vector of strings naming the covariates from data. Not useful if formula is used.
modelTerms	a list of character vectors describing fixed effects terms. Not useful if formula is used
ciType	Choose the confidence interval type
ciWidth	a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the parameter estimates
bootN	number of bootstrap samples for estimating confidence intervals
contrasts	a named vector of the form c(var1='type', var2='type2') specifying the type of contrast to use, one of 'deviation', 'simple', 'dummy', 'difference', 'helmert', 'repeated' or 'polynomial'. If NULL, 'simple' is used. Can also be passed as a list of list of the form list(list(var='var1',type='type1')).
showRealNames	TRUE or FALSE (default), provide raw names of the contrasts variables
showContrastCode	TRUE or FALSE (default), provide contrast coefficients tables
bogus	a bogus option to define a label without visible children, used for internal checks
simpleScale	'mean_sd' (default), 'custom' , or 'custom_percent'. Use to condition the covariates (if any).
cvalue	offset value for conditioning. Values are mean +/- cvalue.
percvalue	offset value for conditioning. Values are median +/- pecvalue
simpleScaleLabels	style for presenting condition values of a moderator. It can be 'labels' (default), 'values' or 'labels_values' for both.
scaling	a named vector of the form c(var1='type1',var2='type2'). Types are 'centered' to the mean, 'standardized', log-transformed 'log' or 'none'. 'none' leaves the variable as it is. It can also be passed as a list of lists.
tableOptions	a vector of options to be shown in the tables. 'component' shows the indirect effects components in the results tables. 'regression' show the results of all the regression (R-squared, F-test and coefficients) involved in the model.
pathOptions	.
mediatorsTerms	a list of lists specifying the models for with the mediators as dependent variables. Not required in formula is used.
moderatorsTerms	a named list of the form list("med"=c("mod1",mod2"),med2="mod1") specifying the moderator(s) of each mediator. This is required to decide for which variable we need to condition the mediated effects and to single out moderators in the path diagram. If not specified, any interaction is considered as any other term in the model
formula	a list of formulas to use, see the examples

Value

A results object containing:

results$info					a table
results$pathmodelgroup$pathmodel					a path model
results$pathmodelgroup$pathnotes					a table
results$models$moderationEffects					a table
results$models$main					a table
results$models$contrastCodeTables					an array of contrast coefficients tables
results$regressions$overall					a group
results$regressions$mediator_regressions					an array of regressions for the mediators
results$regressions$full					a group

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$info$asDF

as.data.frame(results$info)

jamovi-amm/jamm documentation built on Nov. 15, 2023, 9:18 p.m.