piecewiseSEM (version 1.2.1)

sem.basis.set: Derive independence claims for SEM

Description

Generates a list representing the (conditional) independence claims from a model list.

Usage

sem.basis.set(modelList, corr.errors, add.vars)

Arguments

modelList

a list of regressions representing the structural equation model.

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.

Value

Returns a list of independence claims. Each entry in the list is a vector where the first entry is the predictor whose independence from the response is being evaluated, the second is the response, and remaining entries represent the variables on which the independence claim is conditional. E.g.,

[1] "x1" "y" "x2"

would be the missing path between y ~ x1 conditional on x2. The basis set is constructed using the function basiSet in the ggm package.

Details

Variables with correlated errors have no direct relationship but rather are hypothesized to be driven by the same underlying factor. This covariance should be reflected as correlated errors (double-headed arrow). Correlated errors are specified using the same syntax as the lavaan package: var1 ~~ var2. Variables with correlated errors are ignored in the basis set under the assumption that their correlations will be quantified later using the function sem.coefs.

The argument add.vars requires a vector of character strings corresponding to column names in the dataset used to construct the models in modelList. This is useful if comparing nested SEMs where one wishes to account for additional variables whose independence claims should be evaluated, but which do not have any hypothesized paths in the current SEM. The default assumes there is no additional independence claims that do not appear in the model list.

References

Shipley, Bill. "A new inferential test for path models based on directed acyclic graphs." Structural Equation Modeling 7.2 (2000): 206-218.

Examples

# NOT RUN {
# Load example data
data(shipley2009)

# Load model packages
library(lme4)
library(nlme)

# Create list of models 
shipley2009.modlist = list(
  
  lme(DD ~ lat, random = ~1|site/tree, na.action = na.omit, 
      data = shipley2009),
  
  lme(Date ~ DD, random = ~1|site/tree, na.action = na.omit, 
      data = shipley2009),
  
  lme(Growth ~ Date, random = ~1|site/tree, na.action = na.omit, 
      data = shipley2009),
  
  glmer(Live ~ Growth+(1|site)+(1|tree), 
        family=binomial(link = "logit"), data = shipley2009) 
  
)
  
# Get AIC and AICc values for the SEM
sem.basis.set(shipley2009.modlist)
# }