R: Testing Inequality Constrained Hypotheses in SEM

InformativeTesting {lavaan}

R Documentation

Testing Inequality Constrained Hypotheses in SEM

Description

Testing inequality constrained Hypotheses in SEM

Usage

InformativeTesting(model = NULL, data, constraints = NULL, R = 1000L, 
                   type = "bollen.stine", return.LRT = TRUE, calibrate = FALSE, 
                   calibrate.R = 500L, calibrate.alpha = 0.05, 
                   parallel = c("no", "multicore", "snow"), ncpus = 1L, 
                   cl = NULL, verbose = FALSE, stoptest = NULL, 
                   conclusion = FALSE, ...)

Arguments

`model`	Model syntax specifying the model. See `model.syntax` for more information.
`data`	The data frame containing the observed variables being used to fit the model.
`constraints`	The imposed inequality constraints on the model.
`R`	Integer; number of bootstrap draws. Default value is set to 1000.
`type`	If `"parametric"`, the parametric bootstrap is used. If `"bollen.stine"`, the semi-nonparametric Bollen-Stine boostrap is used. The default value is set to `"bollen.stine"`.
`return.LRT`	Logical; if `TRUE`, the function returns bootstrapped LRT-values.
`calibrate`	Logical; if `TRUE`, a double bootstrap is used to compute an additional set of plug-in p-values for each boostrap sample.
`calibrate.R`	Integer; number of double bootstrap draws. Only used if `calibrate=TRUE`. The default value is set to 500.
`calibrate.alpha`	The significance level to compute the adjusted alpha based on the plugin p-values. Only used if `calibrate=TRUE`
`parallel`	The type of parallel operation to be used (if any). If missing, the default is "no".
`ncpus`	Integer: number of processes to be used in parallel operation: typically one would chose this to the number of available CPUs.
`cl`	An optional parallel or snow cluster for use if `parallel = "snow"`. If not supplied, a cluster on the local machine is created for the duration of the `InformativeTesting` call.
`verbose`	Logical; if `TRUE`, information is shown at each bootstrap draw.
`stoptest`	The `InformativeTesting()` stops when the plug-in p-value for the null-hypothesis for Type A is larger than the pre-specified value.
`conclusion`	Logical; if `TRUE`, a conclusion in words is printed.
`...`	Other named arguments from the lavaan package which are passed to the function. For example `"group"` for a multiple group model.

Value

An object of class InformativeTesting for which a summary and a plot method is available.

Author(s)

Leonard Vanbrabant l.g.f.vanbrabant@uu.nl

References

Van de Schoot, R., Hoijtink, H., & Dekovic, M. (2010). Testing inequality constrained hypotheses in SEM models. Structural Equation Modeling, 17, 443-463.

Van de Schoot, R., Strohmeier, D. (2011). Testing informative hypotheses in SEM increases power: An illustration contrasting classical. International Journal of Behavioral Development 35(2), 180-190.

Examples

#Multiple regression 

model  <- '
          y1 ~ b1*x1 + b2*x2 + b3*x3 +
          start(1.8)*x1 + start(0.017)*x2 + start(-0.31)*x3
         
          y1~~start(5.7)*y1
          '
      
constraints  <-'
              b2 < b1
              b3 < b1
              '


#We only generate 5 bootstrap samples and 2 double bootstraps; 
#in practice you may wish to use a much higher number.

example <- InformativeTesting(model = model, data = PoliticalDemocracy, 
                              constraints = constraints, R = 5, 
                              type = "bollen.stine",
                              calibrate = TRUE, calibrate.R = 2,                       
                              calibrate.alpha = 0.05)
example
summary(example)
#plot(example)

[Package lavaan version 0.4-12 Index]