piecewiseSEM (version 2.0.2)

sem.fit: sem.fit

Description

Goodness-of-fit tests for piecewise SEM (old)

Usage

sem.fit(modelList, data, conditional = FALSE, corr.errors = NULL,
  add.vars = NULL, grouping.vars = NULL, grouping.fun = mean,
  adjust.p = FALSE, basis.set = NULL, pvalues.df = NULL,
  model.control = NULL, .progressBar = TRUE)

Arguments

modelList

a list of regressions representing the structural equation model

data

a data.frame used to construct the structured equations

conditional

whether the full set of conditioning variables should be returned. Default is FALSE

corr.errors

a vector of variables with correlated errors (separated by "~~")

add.vars

a vector of additional variables whose independence claims should be evaluated, but which do not appear in the model list

grouping.vars

an optional variable that represents the levels of data aggregation for a multi-level dataset

grouping.fun

a function defining how variables are aggregated in grouping.vars. Default is mean

adjust.p

whether p-values degrees of freedom should be adjusted. Default is FALSE

basis.set

provide an optional basis set

pvalues.df

an optional data.frame corresponding to p-values for independence claims

model.control

a list of model control arguments to be passed to d-sep models

.progressBar

enable optional text progress bar. Default is TRUE

Value

a list corresponding to: the tests of directed separation, the Fisher's C statistic, and the AIC of the model

Details

Tests independence claims and calculates Fisher's C statistic and associated p-value, and AIC and AICc, for a piecewise structural equation model (SEM).