Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/pValueNonNested.R
This function will provide p value from comparing the results of fitting real data into two models against the simulation from fitting the simulated data from both models into both models. The p values from both sampling distribution under the datasets from the first and the second models are reported.
1 2 3 
outMod1 

outMod2 

dat1Mod1 

dat1Mod2 

dat2Mod1 

dat2Mod2 

usedFit 
Vector of names of fit indices that researchers wish to getCutoffs from. The default is to getCutoffs of all fit indices. 
nVal 
The sample size value that researchers wish to find the p value from. 
pmMCARval 
The percent missing completely at random value that researchers wish to find the p value from. 
pmMARval 
The percent missing at random value that researchers wish to find the the p value from. 
df 
The degree of freedom used in spline method in predicting the fit indices by the predictors. If 
onetailed 
If 
In comparing fit indices, the p value is the proportion of the number of replications that provide less preference for either model 1 or model 2 than the analysis result from the observed data. In twotailed test, the function will report the proportion of values under the sampling distribution that are more extreme that one obtained from real data. If the resulting p
value is high (> .05) on one model and low (< .05) in the other model, the model with high p
value is preferred. If the p
values are both high or both low, the decision is undetermined.
This function provides a vector of p values based on the comparison of the difference in fit indices from the real data with the simulation results. The p values of fit indices are provided, as well as two additional values: andRule
and orRule
. The andRule
is based on the principle that the model is retained only when all fit indices provide good fit. The proportion is calculated from the number of replications that have all fit indices indicating a better model than the observed data. The proportion from the andRule
is the most stringent rule in retaining a hypothesized model. The orRule
is based on the principle that the model is retained only when at least one fit index provides good fit. The proportion is calculated from the number of replications that have at least one fit index indicating a better model than the observed data. The proportion from the orRule
is the most lenient rule in retaining a hypothesized model.
Sunthud Pornprasertmanit ([email protected])
SimResult
to run a simulation study
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49  ## Not run:
# Model A; Factor 1 > Factor 2; Factor 2 > Factor 3
library(lavaan)
loading < matrix(0, 11, 3)
loading[1:3, 1] < NA
loading[4:7, 2] < NA
loading[8:11, 3] < NA
path.A < matrix(0, 3, 3)
path.A[2, 1] < NA
path.A[3, 2] < NA
model.A < estmodel(LY=loading, BE=path.A, modelType="SEM", indLab=c(paste("x", 1:3, sep=""),
paste("y", 1:8, sep="")))
out.A < analyze(model.A, PoliticalDemocracy)
# Model A; Factor 1 > Factor 3; Factor 3 > Factor 2
path.B < matrix(0, 3, 3)
path.B[3, 1] < NA
path.B[2, 3] < NA
model.B < estmodel(LY=loading, BE=path.B, modelType="SEM", indLab=c(paste("x", 1:3, sep=""),
paste("y", 1:8, sep="")))
out.B < analyze(model.B, PoliticalDemocracy)
loading.mis < matrix("runif(1, 0.2, 0.2)", 11, 3)
loading.mis[is.na(loading)] < 0
# Create SimSem object for data generation and data analysis template
datamodel.A < model.lavaan(out.A, std=TRUE, LY=loading.mis)
datamodel.B < model.lavaan(out.B, std=TRUE, LY=loading.mis)
# Get sample size
n < nrow(PoliticalDemocracy)
# The actual number of replications should be greater than 20.
output.A.A < sim(20, n=n, model.A, generate=datamodel.A)
output.A.B < sim(20, n=n, model.B, generate=datamodel.A)
output.B.A < sim(20, n=n, model.A, generate=datamodel.B)
output.B.B < sim(20, n=n, model.B, generate=datamodel.B)
# Find the pvalue comparing the observed fit indices against the simulated
# sampling distribution of fit indices
pValueNonNested(out.A, out.B, output.A.A, output.A.B, output.B.A, output.B.B)
# If the pvalue for model A is significant but the pvalue for model B is not
# significant, model B is preferred.
## End(Not run)

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