piecewiseSEM (version 1.2.1)

sem.fisher.c: Goodness-of-fit test for piecewise SEM

Description

Evaluates independence claims for a piecewise structural equation model (SEM), and calculates Fisher's C and associated p-value to evaluate model fit.

Usage

sem.fisher.c(modelList, data, corr.errors, add.vars, grouping.vars, grouping.fun,
  adjust.p, basis.set, pvalues.df, model.control, .progressBar)

Arguments

modelList

a list of regressions representing the structural equation model.

data

a data.frame used to construct the structured equations.

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 (see below). 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

Returns a data.frame with the first entry corresponding to Fisher's C statistic, the second corresponding to the Chi-squared test degrees of freedom, and the third corresponding to the outcome (p-value) of the significance test derived from a Chi-squared distribution.

For linear mixed effects models, p-values can be adjusted to accommodate the full model degrees of freedom using the argument p.adjust = TRUE. For more information, see Shipley 2013.

References

Shipley, Bill. "Confirmatory path analysis in a generalized multilevel context." Ecology 90.2 (2009): 363-368.

Examples

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

# Reduce dataset for example
shipley2009.reduced = shipley2009[1:200, ]

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

# Create list of models 
  shipley2009.reduced.modlist = list(
    
    lme(DD ~ lat, random = ~1|site/tree, na.action = na.omit, 
        data = shipley2009.reduced),
    
    lme(Date ~ DD, random = ~1|site/tree, na.action = na.omit, 
        data = shipley2009.reduced),
    
    lme(Growth ~ Date, random = ~1|site/tree, na.action = na.omit, 
        data = shipley2009.reduced),
    
    glmer(Live ~ Growth+(1|site)+(1|tree), 
          family=binomial(link = "logit"), data = shipley2009.reduced) 
    
  )
  
# Get goodness-of-fit statistics
sem.fisher.c(shipley2009.reduced.modlist, shipley2009.reduced)

# }
# NOT RUN {
  # Repeat with full dataset as in Shipley (2009)

  # 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 goodness-of-fit statistics
  sem.fisher.c(shipley2009.modlist, shipley2009)
  
# }