Description Usage Arguments Value Author(s) References See Also Examples
View source: R/summarySimResult.R
This function provides the summary of the population misfit and misspecified-parameter values across replications. The summary will be summarized for the convergent replications only.
1 | summaryMisspec(object, improper = TRUE)
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object |
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improper |
If TRUE, include the replications that provided improper solutions |
A data frame that provides the summary of population misfit and misspecified-parameter values imposed on the real parameters.
The discrepancy value (f_0; Browne & Cudeck, 1992) is calculated by
F_0 = tr≤ft( \tilde{Σ} Σ^{-1} \right) - \log{≤ft| \tilde{Σ} Σ^{-1} \right|} - p + ≤ft( \tilde{μ} - μ \right)^{\prime} Σ^{-1} ≤ft( \tilde{μ} - μ \right).
where μ is the model-implied mean from the real parameters, Σ is the model-implied covariance matrix from the real parameters, \tilde{μ} is the model-implied mean from the real and misspecified parameters, \tilde{Σ} is the model-implied covariance matrix from the real and misspecified parameter, p is the number of indicators. For the multiple groups, the resulting f_0 value is the sum of this value across groups.
The root mean squared error of approximation (rmsea) is calculated by
rmsea = √{\frac{f_0}{df}}
where df is the degree of freedom in the real model.
The standardized root mean squared residual (srmr) can be calculated by
srmr = √{\frac{2∑_{g} ∑_{i} ∑_{j ≤ i} ≤ft( \frac{s_{gij}}{√{s_{gii}}√{s_{gjj}}} - \frac{\hat{σ}_{gij}}{√{\hat{σ}_{gii}}√{\hat{σ}_{gjj}}} \right)}{g \times p(p + 1)}}
where s_{gij} is the observed covariance between indicators i and j in group g, \hat{σ}_{ij} is the model-implied covariance between indicators i and j in group g, p is the number of indicators.
Sunthud Pornprasertmanit (psunthud@gmail.com)
Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21, 230-258.
SimResult
for the object input
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## Not run:
path <- matrix(0, 4, 4)
path[3, 1:2] <- NA
path[4, 3] <- NA
pathVal <- matrix("", 4, 4)
pathVal[3, 1:2] <- "runif(1, 0.3, 0.5)"
pathVal[4, 3] <- "runif(1, 0.5, 0.7)"
pathMis <- matrix(0, 4, 4)
pathMis[4, 1:2] <- "runif(1, -0.1, 0.1)"
BE <- bind(path, pathVal, pathMis)
residual.error <- diag(4)
residual.error[1,2] <- residual.error[2,1] <- NA
RPS <- binds(residual.error, "rnorm(1, 0.3, 0.1)")
Path.Model <- model(RPS = RPS, BE = BE, modelType="Path")
# The number of replications in actual analysis should be much more than 5
ParamObject <- sim(5, n=200, Path.Model)
# Summarize the model misspecification that is specified in the 'pathMis' object
summaryMisspec(ParamObject)
## End(Not run)
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