simsem (version 0.2-0)

anova: Provide a comparison of nested models across replications

Description

This function will provide averages of model fit statistics and indices for nested models. It will also provide average differences of fit indices and power for likelihood ratio tests of nested models.

Arguments

object

SimResult object being described. Currently at least two objects must be included as arguments

...

any additional arguments, such as additional objects or for the function with result object

Value

• A data frame that provides the statistics described above from all parameters. For using with SimModelOut, each column means
  • Chisq.diff:Average chi square difference across all replications
  • Df.diff:Average degrees of freedom difference across all replications
  • Power:The proportion of replications where the likelihood ratio test is significant
  • CFI.diff:Average difference of the CFI across all replications
  • TLI.diff:Average difference of the TLI across all replications
  • RMSEA.diff:Average difference of the RMSEA across all replications
  For using with linkS4class{SimResult}, each column means
  • Df:Average of degrees of freedom across all replications
  • Chisq:Average of the chi square statistic across all replications
  • CFI:Average of the CFI across all replications
  • TLI:Average of the TLI across all replications
  • RMSEA:Average of the RMSEA across all replications
  • Chisq diff:Average chi square difference across all replications
  • Df diff:Average degrees of freedom difference across all replications
  • Power:The proportion of replications where the likelihood ratio test is significant
  • CFI diff:Average difference of the CFI across all replications
  • TLI diff:Average difference of the TLI across all replications
  • RMSEA diff:Average difference of the RMSEA across all replications

See Also

SimResult for the object input

Examples

loading1 <- matrix(0, 6, 1)
loading1[1:6, 1] <- NA
loading2 <- loading1
loading2[6,1] <- 0
LX1 <- simMatrix(loading1, 0.7)
LX2 <- simMatrix(loading2, 0.7)
RPH <- symMatrix(diag(1))
RTD <- symMatrix(diag(6))
CFA.Model1 <- simSetCFA(LY = LX1, RPS = RPH, RTE = RTD)
CFA.Model2 <- simSetCFA(LY = LX2, RPS = RPH, RTE = RTD)
SimData <- simData(CFA.Model1, 500)
SimModel1 <- simModel(CFA.Model1)
SimModel2 <- simModel(CFA.Model2)
# We make the examples running only 5 replications to save time.
# In reality, more replications are needed.
# Need to make sure that both simResult calls have the same seed!
Output1 <- simResult(5, SimData, SimModel1, seed=123567)
Output2 <- simResult(5, SimData, SimModel2, seed=123567)
anova(Output1, Output2)