SimResult-class: Class '"SimResult"': Simulation Result Object

In simsem: SIMulated Structural Equation Modeling

Description

This class will save data analysis results from multiple replications, such as fit indices cutoffs or power, parameter values, model misspecification, etc.

Objects from the Class

Objects can be created by sim.

Slots

modelType:

Analysis model type (CFA, Path, or SEM)

nRep:

Number of replications have been created and run simulated data.

coef:

Parameter estimates from each replication

se:

Standard errors of parameter estimates from each replication

fit:

Fit Indices values from each replication

converged:

The convergence status of each replication: 0 = convergent, 1 = not convergent, 2 = nonconvergent in multiple imputed results, 3 = improper solutions for SE (less than 0 or NA), 4 = converged with improper solution for latent or observed (residual) covariance matrix (i.e., nonpositive definite, possible due to a Heywood case). For multiple imputations, these codes are applied when the proporion of imputed data sets with that characteristic is below the convergentCutoff threshold (see linkS4class{SimMissing}). For OpenMx analyses only, a code "7" indicates Optimal estimates could not be obtained ("Status 6" in OpenMx).

seed:

integer used to set the seed for the L'Ecuyer-CMRG pseudorandom number generator.

paramValue:

Population model underlying each simulated dataset.

stdParamValue:

Standardized parameters of the population model underlying each simulated dataset.

paramOnly:

If TRUE, the result object saves only population characteristics and do not save sample characteristics (e.g., parameter estimates and standard errors.

misspecValue:

Misspecified-parameter values that are imposed on the population model in each replication.

popFit:

The amount of population misfit. See details at summaryMisspec

FMI1:

Fraction Missing Method 1.

FMI2:

Fraction Missing Method 2.

cilower:

Lower bounds of confidence interval.

ciupper:

Upper bounds of confidence interval.

stdCoef:

Standardized coefficients from each replication

stdSe:

Standard Errors of Standardized coefficients from each replication

n:

The total sample size of the analyzed data.

nobs:

The sample size within each group.

pmMCAR:

Percent missing completely at random.

pmMAR:

Percent missing at random.

extraOut:

Extra outputs obtained from running the function specified in outfun argument in the sim function.

timing:

Time elapsed in each phase of the simulation.

Methods

The following methods are listed alphabetically. More details can be found by following the link of each method.

• anova to find the averages of model fit statistics and indices for nested models, as well as the differences of model fit indices among models. This function requires at least two SimResult objects.

• coef to extract parameter estimates of each replication

• findCoverage to find a value of independent variables (e.g., sample size) that provides a given value of coverage rate.

• findPower to find a value of independent variables (e.g., sample size) that provides a given value of power of a parameter estimate.

• getCoverage to get the coverage rate of the confidence interval of each parameter estimate

• getCIwidth to get a median or percentile rank (assurance) of confidence interval widths of parameters estimates

• getCutoff to get the cutoff of fit indices based on a priori alpha level.

• getCutoffNested to get the cutoff of the difference in fit indices of nested models based on a priori alpha level.

• getCutoffNonNested to get the cutoff of the difference in fit indices of nonnested models based on a priori alpha level.

• getExtraOutput to get extra outputs that users requested before running a simulation

• getPopulation to get population parameter values underlying each dataset

• getPower to get the power of each parameter estimate

• getPowerFit to get the power in rejecting alternative models based on absolute model fit cutoff.

• getPowerFitNested to get the power in rejecting alternative models based on the difference between model fit cutoffs of nested models.

• getPowerFitNonNested to get the power in rejecting alternative models based on the difference between model fit cutoffs of nonnested models.

• inspect Extract target information from the simulation result. The available information is listed in this link

• likRatioFit to find the likelihood ratio (or Bayes factor) based on the bivariate distribution of fit indices

• plotCoverage to plot the coverage rate of confidence interval of parameter estimates

• plotCIwidth to plot confidence interval widths with a line of a median or percentile rank (assurance)

• plotCutoff to plot sampling distributions of fit indices with an option to draw fit indices cutoffs by specifying a priori alpha level.

• plotCutoffNested to plot sampling distributions of the difference in fit indices between nested models with an option to draw fit indices cutoffs by specifying a priori alpha level.

• plotCutoffNonNested to plot sampling distributions of the difference in fit indices between nonnested models with an option to draw fit indices cutoffs by specifying a priori alpha level.

• plotMisfit to visualize the population misfit and misspecified parameter values

• plotPower to plot power of parameter estimates

• plotPowerFit to plot the power in rejecting alternative models based on absolute model fit cutoff.

• plotPowerFitNested to plot the power in rejecting alternative models based on the difference between model fit cutoffs of nested models.

• plotPowerFitNonNested to plot the power in rejecting alternative models based on the difference between model fit cutoffs of nonnested models.

• pValue to find a p-value in comparing sample fit indices with the null sampling distribution of fit indices

• pValueNested to find a p-value in comparing the difference in sample fit indices between nested models with the null sampling distribution of the difference in fit indices

• pValueNonNested to find a p-value in comparing the difference in sample fit indices between nonnested models with the null sampling distribution of the difference in fit indices

• setPopulation to set population model for computing bias

• summary to summarize the result output

• summaryConverge to provide a head-to-head comparison between the characteristics of convergent and nonconvergent replications

• summaryMisspec to provide a summary of model misfit

• summaryParam to summarize all parameter estimates

• summaryPopulation to summarize the data generation population underlying the simulation study.

• summarySeed to provide a summary of the seed number in the simulation

• summaryShort to provide a short summary of the result output

• summaryTime to provide a summary of time elapsed in the simulation

Author(s)

Sunthud Pornprasertmanit ([e-mail])

See Also

• sim for the constructor of this class

Examples

showClass("SimResult")
loading <- matrix(0, 6, 1)
loading[1:6, 1] <- NA
LY <- bind(loading, 0.7)
RPS <- binds(diag(1))
RTE <- binds(diag(6))
CFA.Model <- model(LY = LY, RPS = RPS, RTE = RTE, modelType="CFA")

# We make the examples running only 5 replications to save time.
# In reality, more replications are needed.
Output <- sim(5, n=500, CFA.Model)

# Summary the simulation result
summary(Output)

# Short summary of the simulation result
summaryShort(Output)

# Find the fit index cutoff
getCutoff(Output, 0.05)

# Summary of parameter estimates
summaryParam(Output)

# Summary of population parameters
summaryPopulation(Output)

Example output

Loading required package: lavaan
This is lavaan 0.6-7
lavaan is BETA software! Please report any bugs.
 
#################################################################
This is simsem 0.5-15
simsem is BETA software! Please report any bugs.
simsem was first developed at the University of Kansas Center for
Research Methods and Data Analysis, under NSF Grant 1053160.
#################################################################

Attaching package: ‘simsem’

The following object is masked from ‘package:lavaan’:

    inspect

Class "SimResult" [package "simsem"]

Slots:
                                                                            
Name:      modelType          nRep          coef            se           fit
Class:     character       numeric    data.frame    data.frame    data.frame
                                                                            
Name:      converged    paramValue stdParamValue  misspecValue        popFit
Class:        vector    data.frame    data.frame    data.frame    data.frame
                                                                            
Name:           FMI1          FMI2       cilower       ciupper       stdCoef
Class:    data.frame    data.frame    data.frame    data.frame    data.frame
                                                                            
Name:          stdSe          seed             n          nobs        pmMCAR
Class:    data.frame       numeric        vector    data.frame        vector
                                                              
Name:          pmMAR      extraOut     paramOnly        timing
Class:        vector          list       logical          list
Progress: 1 / 5 
Progress: 2 / 5 
Progress: 3 / 5 
Progress: 4 / 5 
Progress: 5 / 5 
RESULT OBJECT
Model Type
[1] "cfa"
========= Fit Indices Cutoffs ============
           Alpha
Fit Indices      0.1     0.05     0.01    0.001     Mean     SD
      chisq   12.264   13.282   14.097   14.281    9.520  2.755
      aic   7492.545 7493.877 7494.942 7495.182 7439.859 63.769
      bic   7568.408 7569.740 7570.805 7571.045 7515.722 63.769
      rmsea    0.023    0.029    0.033    0.034    0.008  0.015
      cfi      0.996    0.995    0.994    0.994    0.999  0.003
      tli      0.994    0.992    0.991    0.990    0.999  0.005
      srmr     0.016    0.017    0.018    0.018    0.014  0.002
========= Parameter Estimates and Standard Errors ============
       Estimate Average Estimate SD Average SE Power (Not equal 0) Std Est
f1=~y1            0.648       0.048      0.042                   1   0.664
f1=~y2            0.674       0.040      0.042                   1   0.680
f1=~y3            0.710       0.054      0.041                   1   0.719
f1=~y4            0.696       0.058      0.041                   1   0.704
f1=~y5            0.681       0.075      0.042                   1   0.688
f1=~y6            0.672       0.043      0.042                   1   0.681
y1~~y1            0.528       0.029      0.039                   1   0.558
y2~~y2            0.526       0.020      0.040                   1   0.537
y3~~y3            0.467       0.046      0.037                   1   0.481
y4~~y4            0.489       0.016      0.038                   1   0.504
y5~~y5            0.510       0.018      0.039                   1   0.526
y6~~y6            0.518       0.033      0.039                   1   0.535
y1~1             -0.042       0.008      0.044                   0  -0.043
y2~1             -0.032       0.026      0.044                   0  -0.033
y3~1              0.011       0.031      0.044                   0   0.011
y4~1             -0.028       0.043      0.044                   0  -0.027
y5~1             -0.010       0.065      0.044                   0  -0.011
y6~1             -0.015       0.059      0.044                   0  -0.016
       Std Est SD Std Ave SE Average Param Average Bias Coverage
f1=~y1      0.029      0.030          0.70       -0.052      0.8
f1=~y2      0.026      0.029          0.70       -0.026      1.0
f1=~y3      0.037      0.027          0.70        0.010      0.8
f1=~y4      0.027      0.028          0.70       -0.004      0.8
f1=~y5      0.039      0.028          0.70       -0.019      0.6
f1=~y6      0.028      0.029          0.70       -0.028      1.0
y1~~y1      0.039      0.039          0.51        0.018      1.0
y2~~y2      0.035      0.039          0.51        0.016      1.0
y3~~y3      0.053      0.038          0.51       -0.043      0.8
y4~~y4      0.038      0.039          0.51       -0.021      1.0
y5~~y5      0.054      0.039          0.51        0.000      1.0
y6~~y6      0.038      0.039          0.51        0.008      1.0
y1~1        0.008      0.045          0.00       -0.042      1.0
y2~1        0.026      0.045          0.00       -0.032      1.0
y3~1        0.032      0.045          0.00        0.011      1.0
y4~1        0.042      0.045          0.00       -0.028      1.0
y5~1        0.067      0.045          0.00       -0.010      1.0
y6~1        0.061      0.045          0.00       -0.015      1.0
========= Correlation between Fit Indices ============
       chisq    aic    bic  rmsea    cfi    tli   srmr
chisq  1.000 -0.282 -0.282  0.988 -0.976 -0.999  0.966
aic   -0.282  1.000  1.000 -0.319  0.166  0.263 -0.374
bic   -0.282  1.000  1.000 -0.319  0.166  0.263 -0.374
rmsea  0.988 -0.319 -0.319  1.000 -0.987 -0.986  0.981
cfi   -0.976  0.166  0.166 -0.987  1.000  0.977 -0.965
tli   -0.999  0.263  0.263 -0.986  0.977  1.000 -0.958
srmr   0.966 -0.374 -0.374  0.981 -0.965 -0.958  1.000
================== Replications =====================
Number of replications = 5 
Number of converged replications = 5 
Number of nonconverged replications: 
   1. Nonconvergent Results = 0 
   2. Nonconvergent results from multiple imputation = 0 
   3. At least one SE were negative or NA = 0 
   4. At least one variance estimates were negative = 0 
   5. At least one correlation estimates were greater than 1 or less than -1 = 0 
   6. Model-implied covariance matrices of any groups of latent variables are not positive definite = 0 
RESULT OBJECT
[1] "cfa"
Model Type: cfa 
Convergence 5 / 5 
Sample size: 500 
Percent Completely Missing at Random: 0 
Percent Missing at Random: 0 
========= Fit Indices Cutoffs ============
           Alpha
Fit Indices     0.05     Mean     SD
      chisq   13.282    9.520  2.755
      aic   7493.877 7439.859 63.769
      bic   7569.740 7515.722 63.769
      rmsea    0.029    0.008  0.015
      cfi      0.995    0.999  0.003
      tli      0.992    0.999  0.005
      srmr     0.017    0.014  0.002
       chisq      aic     bic      rmsea       cfi       tli       srmr
95% 13.28247 7493.877 7569.74 0.02881863 0.9953228 0.9922047 0.01702757
       Estimate Average Estimate SD Average SE Power (Not equal 0)     Std Est
f1=~y1       0.64769347 0.047633792 0.04152994                   1  0.66443090
f1=~y2       0.67410365 0.039784006 0.04197009                   1  0.68003224
f1=~y3       0.70962059 0.053933926 0.04101338                   1  0.71933877
f1=~y4       0.69587656 0.057541606 0.04136722                   1  0.70406733
f1=~y5       0.68077522 0.074956208 0.04166100                   1  0.68779081
f1=~y6       0.67152361 0.042907043 0.04170321                   1  0.68148099
y1~~y1       0.52821328 0.028904477 0.03909841                   1  0.55784320
y2~~y2       0.52625967 0.020207669 0.03956792                   1  0.53702484
y3~~y3       0.46690998 0.046045804 0.03686850                   1  0.48147618
y4~~y4       0.48912047 0.015610947 0.03790651                   1  0.50372730
y5~~y5       0.50960273 0.017969772 0.03877970                   1  0.52573342
y6~~y6       0.51831050 0.033075434 0.03910778                   1  0.53496217
y1~1        -0.04168278 0.007929115 0.04355652                   0 -0.04273744
y2~1        -0.03239567 0.025848733 0.04430591                   0 -0.03304879
y3~1         0.01099607 0.030823321 0.04408538                   0  0.01123800
y4~1        -0.02810014 0.042865901 0.04414599                   0 -0.02735015
y5~1        -0.01049442 0.064670892 0.04416644                   0 -0.01058453
y6~1        -0.01510456 0.059148091 0.04404404                   0 -0.01574890
        Std Est SD Std Ave SE Average Param  Average Bias Coverage
f1=~y1 0.029333801 0.02972991          0.70 -0.0523065323      0.8
f1=~y2 0.025771089 0.02889890          0.70 -0.0258963462      1.0
f1=~y3 0.036666589 0.02672021          0.70  0.0096205854      0.8
f1=~y4 0.026502082 0.02760399          0.70 -0.0041234442      0.8
f1=~y5 0.038897084 0.02846897          0.70 -0.0192247765      0.6
f1=~y6 0.027872231 0.02884983          0.70 -0.0284763883      1.0
y1~~y1 0.039228053 0.03940489          0.51  0.0182132773      1.0
y2~~y2 0.035065889 0.03922481          0.51  0.0162596737      1.0
y3~~y3 0.052848397 0.03828097          0.51 -0.0430900220      0.8
y4~~y4 0.037901166 0.03879579          0.51 -0.0208795314      1.0
y5~~y5 0.054338806 0.03900646          0.51 -0.0003972686      1.0
y6~~y6 0.037825683 0.03925652          0.51  0.0083104954      1.0
y1~1   0.007732284 0.04474231          0.00 -0.0416827795      1.0
y2~1   0.026350478 0.04473977          0.00 -0.0323956700      1.0
y3~1   0.031769962 0.04473180          0.00  0.0109960673      1.0
y4~1   0.042192605 0.04474563          0.00 -0.0281001441      1.0
y5~1   0.066912851 0.04476263          0.00 -0.0104944232      1.0
y6~1   0.060600318 0.04475696          0.00 -0.0151045607      1.0
                 f1=~y1 f1=~y2 f1=~y3 f1=~y4 f1=~y5 f1=~y6 y1~~y1 y2~~y2 y3~~y3
Population Value 0.7    0.7    0.7    0.7    0.7    0.7    0.51   0.51   0.51  
                 y4~~y4 y5~~y5 y6~~y6 y1~1 y2~1 y3~1 y4~1 y5~1 y6~1
Population Value 0.51   0.51   0.51   0    0    0    0    0    0   

simsem documentation built on March 29, 2021, 1:07 a.m.