Provide a comparison of nested models and nonnested models across replications
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.
object |
|
... |
any additional arguments, such as additional objects or for the function with result object |
A data frame that provides the statistics described above from all parameters.
For using with linkS4class{SimResult}, the result is a list with two or three elements:
summary: Average of fit indices across all replications
diff: Average of the differences in fit indices across all replications
varyParam: The statistical power of chi-square difference test given values of varying parameters (such as sample size or percent missing)
Alexander M. Schoemann (East Carolina University; schoemanna@ecu.edu), Sunthud Pornprasertmanit (psunthud@gmail.com)
SimResult for the object input
## Not run: loading1 <- matrix(0, 6, 1) loading1[1:6, 1] <- NA loading2 <- loading1 loading2[6,1] <- 0 LY1 <- bind(loading1, 0.7) LY2 <- bind(loading2, 0.7) RPS <- binds(diag(1)) RTE <- binds(diag(6)) CFA.Model1 <- model(LY = LY1, RPS = RPS, RTE = RTE, modelType="CFA") CFA.Model2 <- model(LY = LY2, RPS = RPS, RTE = RTE, modelType="CFA") # 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 <- sim(5, n=500, model=CFA.Model1, generate=CFA.Model1, seed=123567) Output2 <- sim(5, n=500, model=CFA.Model2, generate=CFA.Model1, seed=123567) anova(Output1, Output2) # The example when the sample size is varying Output1b <- sim(NULL, n=seq(50, 500, 50), model=CFA.Model1, generate=CFA.Model1, seed=123567) Output2b <- sim(NULL, n=seq(50, 500, 50), model=CFA.Model2, generate=CFA.Model1, seed=123567) anova(Output1b, Output2b) ## End(Not run)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.