piecewiseSEM

Generalized function for SEM AIC(c) score

Generalized function for SEM BIC score

Get data from model list

Obtain (observation-level) random effects from a generalized linear mixed model

Get standard deviation of predictor variables

Properly scale standard deviations depending on the error distribution

Get data from one model

Get random effects variance-covariance from lme

Remove random effects from all.vars

Get vector of untransformed variables

Get vector of transformed variables

ANOVA and chi-squared difference test for model comparison

Chi-square difference test

Single anova

Convert list to psem object

Derivation of the basis set

Captures output table

Bind data.frames of differing dimensions

Correlated errors

Extract path coefficients

Tests of directed separation

Transform variables based on model formula and store in new data frame

Identify duplicate output

Evaluate model classes and stop if unsupported model class

Remove existing paths from the basis set

Filter relationships among exogenous variables from the basis set (ignoring add.vars)

Filter interactions from the d-sep tests

Get random effects from lme

Summarize tests of directed separation using Fisher's C statistic

Format for psem

Get ANOVA results from 'merMod'

Get coefficients from linear regression

Generate adjacency matrix from list of structural equations

Identify models with correlated errors and return modified versions

Get Response Name as a Character

Correlated error operator

Handles putting categorical variables into coefficient tables for easy use in path analysis

Information criterion values for SEM

Assess significance

Data set from Grace & Keeley (2006)

Recompute P-values using Kenward-Rogers approximation

Data set from Grace & Jutila (1999)

Multigroup Analysis for Piecewise SEM

Get number of observations from a model

Get random effects from merMod

Calculate partial correlations from partial residuals

Computing partial effects

Piecewise Structural Equation Modeling

Plotting of Piecewise Structural Equation Models

Print anova

Do not print attributes with custom functions

Print basis set

Print multigroup

Print psem

Print summary

Fitting piecewise structural equation models

Remove correlated errors from the basis set

Remove data from the model list

Get residuals from innermost grouping of mixed models (replicate-level)

Residual values from fit models

Replace transformations in the basis set by cycling through neighbors and applying transformations in order of how variables are treated in the child nearest to current node

If intermediate endogenous variables are nonlinear, return both directions

R-squared for linear regression

R^2 for glm objects

R^2 for glmer objects

R^2 for glmmPQL objects

R^2 for gls objects

R^2 for lm objects

R^2 for lme objects

R^2 for phylolm objects

R^2 for negbin objects

Compute standard deviation or relevant range of response for GLMs

Calculate standard deviation or relevant range for interaction terms

Data set from Shipley (2006)

Sort DAG based on ancestry

Remove duplicate items from the basis set whose direction is not a priori specified

Calculate standardized regression coefficients

Strip transformations

Summarizing piecewise structural equation models

Evaluate conditional independence claim from the basis set

Get raw (undstandardized) coefficients from model

Update psem model object with additional values.