summaryParam: Provide summary of parameter estimates and standard error...
In simsem: SIMulated Structural Equation Modeling

Description Usage Arguments Value Author(s) References See Also Examples

This function will provide averages of parameter estimates, standard deviations of parameter estimates, averages of standard errors, and power of rejection with a priori alpha level for the null hypothesis of parameters equal 0.

1 2	summaryParam(object, alpha = 0.05, std = FALSE, detail = FALSE, improper = TRUE, digits = NULL, matchParam = FALSE)

`object`	`SimResult` object being described
`alpha`	The alpha level used to find the statistical power of each parameter estimate
`std`	If `TRUE`, (a) standardized coefficients and their standard errors substitute unstandardized coefficients, (b) standardized parameter values substitute parameter values, (c) confidence intervals of standardized coefficients are calculated using Wald confidence interval, and (d) all results (e.g., biases or coverage) are calculated based on standardized coefficients.
`detail`	If TRUE, more details about each parameter estimate are provided, such as relative bias, standardized bias, or relative standard error bias.
`improper`	If TRUE, include the replications that provided improper solutions
`digits`	The number of digits rounded in the result. If `NULL`, the results will not be rounded.
`matchParam`	If `TRUE`, only parameter estimates that have the same names as the parameter values will be reported. This argument is recommended when users know that the data-generation model and analysis model are the same. Then the comparison between the parameter estimates and parameter value will be valid.

A data frame that provides the statistics described above from all parameters. For using with linkS4class{SimResult}, each column means

Estimate.Average: Average of parameter estimates across all replications
Estimate.SD: Standard Deviation of parameter estimates across all replications
Average.SE: Average of standard errors across all replications
Power (Not equal 0): Proportion of significant replications when testing whether the parameters are different from zero. The alpha level can be set by the alpha argument of this function.
Average.Param: Parameter values or average values of parameters if random parameters are specified
SD.Param: Standard Deviations of parameters. Show only when random parameters are specified.
Average.Bias: The difference between parameter estimates and parameter underlying data
SD.Bias: Standard Deviations of bias across all replications. Show only when random parameters are specified. This value is the expected value of average standard error when random parameter are specified.
Coverage: The percentage of (1-alpha)% confidence interval covers parameters underlying the data.
Rel.Bias: Relative Bias, which is (Estimate.Average - Average.Param)/Average.Param. Hoogland and Boomsma (1998) proposed that the cutoff of .05 may be used for acceptable relative bias. This option will be available when detail=TRUE. This value will not be available when parameter values are very close to 0.
Std.Bias: Standardized Bias, which is (Estimate.Average - Average.Param)/Estimate.SD for fixed parameters and (Estimate.Average - Average.Param)/SD.Bias for random parameters. Collins, Schafer, and Kam (2001) recommended that biases will be only noticeable when standardized bias is greater than 0.4 in magnitude. This option will be available when detail=TRUE
Rel.SE.Bias: Relative Bias in standard error, which is (Average.SE - Estimate.SD)/Estimate.SD for fixed parameters and (Average.SE - SD.Bias)/SD.Bias for random parameters. Hoogland and Boomsma (1998) proposed that 0.10 is the acceptable level. This option will be available when detail=TRUE
Not Cover Below: The percentage of (1-alpha)% confidence interval does not cover the parameter and the parameter is below the confidence interval.
Not Cover Above: The percentage of (1-alpha)% confidence interval does not cover the parameter and the parameter is above the confidence interval.
Average CI Width: The average of (1-alpha)% confidence interval width across replications.
SD CI Width: The standard deviation of (1-alpha)% confidence interval width across replications.

Sunthud Pornprasertmanit (psunthud@gmail.com)

Collins, L. M., Schafer, J. L., & Kam, C. M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6, 330-351.

Hoogland, J. J., & Boomsma, A. (1998). Robustness studies in covariance structure modeling. Sociological Methods & Research, 26, 329-367.

SimResult for the object input

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 of the parameter estimates
summaryParam(Output)

# Summary of the parameter estimates with additional details
summaryParam(Output, detail=TRUE)

Loading required package: lavaan
This is lavaan 0.6-3
lavaan is BETA software! Please report any bugs.
 
#################################################################
This is simsem 0.5-14
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 
       Estimate Average Estimate SD Average SE Power (Not equal 0)      Std Est
f1=~y1      0.673722887  0.05269382 0.04105702                 1.0  0.690974730
f1=~y2      0.688183100  0.04040824 0.04199595                 1.0  0.690800369
f1=~y3      0.676505611  0.02711606 0.04198031                 1.0  0.682390133
f1=~y4      0.703249754  0.05381503 0.04216267                 1.0  0.699711771
f1=~y5      0.669202469  0.06101624 0.04117997                 1.0  0.685549027
f1=~y6      0.668726918  0.08547044 0.04090277                 1.0  0.687505265
y1~~y1      0.493504935  0.03384905 0.03759368                 1.0  0.521588144
y2~~y2      0.517317905  0.03566803 0.03938505                 1.0  0.522140907
y3~~y3      0.525253825  0.03984563 0.03963206                 1.0  0.534052724
y4~~y4      0.512457432  0.04083552 0.03944708                 1.0  0.509438335
y5~~y5      0.500429829  0.02997438 0.03794635                 1.0  0.528870007
y6~~y6      0.489602904  0.01706654 0.03736055                 1.0  0.525467079
y1~1       -0.010112115  0.02053080 0.04356251                 0.0 -0.010043840
y2~1       -0.012455830  0.02417306 0.04453451                 0.0 -0.012627385
y3~1       -0.023005874  0.06132684 0.04433566                 0.2 -0.022337795
y4~1        0.004872976  0.04518219 0.04490787                 0.0  0.004962158
y5~1       -0.050908767  0.01430090 0.04359341                 0.0 -0.052173114
y6~1       -0.025171901  0.06470420 0.04336845                 0.4 -0.025959637
       Std Est SD Std Ave SE Average Param Average Bias Coverage
f1=~y1 0.03474511 0.02830065          0.70 -0.026277113      1.0
f1=~y2 0.02859070 0.02832471          0.70 -0.011816900      1.0
f1=~y3 0.01907164 0.02881365          0.70 -0.023494389      1.0
f1=~y4 0.03473296 0.02782200          0.70  0.003249754      1.0
f1=~y5 0.03795597 0.02859785          0.70 -0.030797531      0.8
f1=~y6 0.04834036 0.02848247          0.70 -0.031273082      0.6
y1~~y1 0.04950494 0.03897351          0.51 -0.016495065      1.0
y2~~y2 0.03961677 0.03904245          0.51  0.007317905      1.0
y3~~y3 0.02601147 0.03929399          0.51  0.015253825      1.0
y4~~y4 0.04960985 0.03879666          0.51  0.002457432      1.0
y5~~y5 0.05381623 0.03905676          0.51 -0.009570171      1.0
y6~~y6 0.06845240 0.03893188          0.51 -0.020397096      1.0
y1~1   0.02064539 0.04472630          0.00 -0.010112115      1.0
y2~1   0.02459190 0.04472855          0.00 -0.012455830      1.0
y3~1   0.06199248 0.04476128          0.00 -0.023005874      0.8
y4~1   0.04581313 0.04474040          0.00  0.004872976      1.0
y5~1   0.01435687 0.04475362          0.00 -0.050908767      1.0
y6~1   0.06820552 0.04477046          0.00 -0.025171901      0.6
       Estimate Average Estimate SD Average SE Power (Not equal 0)      Std Est
f1=~y1      0.673722887  0.05269382 0.04105702                 1.0  0.690974730
f1=~y2      0.688183100  0.04040824 0.04199595                 1.0  0.690800369
f1=~y3      0.676505611  0.02711606 0.04198031                 1.0  0.682390133
f1=~y4      0.703249754  0.05381503 0.04216267                 1.0  0.699711771
f1=~y5      0.669202469  0.06101624 0.04117997                 1.0  0.685549027
f1=~y6      0.668726918  0.08547044 0.04090277                 1.0  0.687505265
y1~~y1      0.493504935  0.03384905 0.03759368                 1.0  0.521588144
y2~~y2      0.517317905  0.03566803 0.03938505                 1.0  0.522140907
y3~~y3      0.525253825  0.03984563 0.03963206                 1.0  0.534052724
y4~~y4      0.512457432  0.04083552 0.03944708                 1.0  0.509438335
y5~~y5      0.500429829  0.02997438 0.03794635                 1.0  0.528870007
y6~~y6      0.489602904  0.01706654 0.03736055                 1.0  0.525467079
y1~1       -0.010112115  0.02053080 0.04356251                 0.0 -0.010043840
y2~1       -0.012455830  0.02417306 0.04453451                 0.0 -0.012627385
y3~1       -0.023005874  0.06132684 0.04433566                 0.2 -0.022337795
y4~1        0.004872976  0.04518219 0.04490787                 0.0  0.004962158
y5~1       -0.050908767  0.01430090 0.04359341                 0.0 -0.052173114
y6~1       -0.025171901  0.06470420 0.04336845                 0.4 -0.025959637
       Std Est SD Std Ave SE Average Param Average Bias Coverage     Rel Bias
f1=~y1 0.03474511 0.02830065          0.70 -0.026277113      1.0 -0.037538733
f1=~y2 0.02859070 0.02832471          0.70 -0.011816900      1.0 -0.016881286
f1=~y3 0.01907164 0.02881365          0.70 -0.023494389      1.0 -0.033563414
f1=~y4 0.03473296 0.02782200          0.70  0.003249754      1.0  0.004642506
f1=~y5 0.03795597 0.02859785          0.70 -0.030797531      0.8 -0.043996473
f1=~y6 0.04834036 0.02848247          0.70 -0.031273082      0.6 -0.044675831
y1~~y1 0.04950494 0.03897351          0.51 -0.016495065      1.0 -0.032343264
y2~~y2 0.03961677 0.03904245          0.51  0.007317905      1.0  0.014348833
y3~~y3 0.02601147 0.03929399          0.51  0.015253825      1.0  0.029909462
y4~~y4 0.04960985 0.03879666          0.51  0.002457432      1.0  0.004818495
y5~~y5 0.05381623 0.03905676          0.51 -0.009570171      1.0 -0.018765042
y6~~y6 0.06845240 0.03893188          0.51 -0.020397096      1.0 -0.039994306
y1~1   0.02064539 0.04472630          0.00 -0.010112115      1.0           NA
y2~1   0.02459190 0.04472855          0.00 -0.012455830      1.0           NA
y3~1   0.06199248 0.04476128          0.00 -0.023005874      0.8           NA
y4~1   0.04581313 0.04474040          0.00  0.004872976      1.0           NA
y5~1   0.01435687 0.04475362          0.00 -0.050908767      1.0         -Inf
y6~1   0.06820552 0.04477046          0.00 -0.025171901      0.6           NA
          Std Bias  Rel SE Bias Not Cover Below Not Cover Above
f1=~y1 -0.49867538 -0.220838040             0.0             0.0
f1=~y2 -0.29243785  0.039291575             0.0             0.0
f1=~y3 -0.86643823  0.548171515             0.0             0.0
f1=~y4  0.06038749 -0.216526167             0.0             0.0
f1=~y5 -0.50474317 -0.325098278             0.0             0.2
f1=~y6 -0.36589354 -0.521439602             0.2             0.2
y1~~y1 -0.48731250  0.110627455             0.0             0.0
y2~~y2  0.20516706  0.104211573             0.0             0.0
y3~~y3  0.38282304 -0.005359988             0.0             0.0
y4~~y4  0.06017879 -0.034000877             0.0             0.0
y5~~y5 -0.31927836  0.265959299             0.0             0.0
y6~~y6 -1.19515137  1.189111471             0.0             0.0
y1~1   -0.49253396  1.121812715             0.0             0.0
y2~1   -0.51527738  0.842319886             0.0             0.0
y3~1   -0.37513548 -0.277059497             0.0             0.2
y4~1    0.10785171 -0.006071444             0.0             0.0
y5~1   -3.55983052  2.048298835             0.0             0.0
y6~1   -0.38903038 -0.329742937             0.0             0.4
       Average CI Width SD CI Width
f1=~y1        0.1609406 0.002569773
f1=~y2        0.1646211 0.003739531
f1=~y3        0.1645598 0.004269441
f1=~y4        0.1652746 0.004330657
f1=~y5        0.1614225 0.002982417
f1=~y6        0.1603359 0.004247527
y1~~y1        0.1473645 0.006211867
y2~~y2        0.1543866 0.008102138
y3~~y3        0.1553548 0.008667704
y4~~y4        0.1546297 0.009460303
y5~~y5        0.1487469 0.005465505
y6~~y6        0.1464507 0.002985273
y1~1          0.1707619 0.005483457
y2~1          0.1745721 0.004634194
y3~1          0.1737926 0.005098253
y4~1          0.1760356 0.006123785
y5~1          0.1708830 0.006343966
y6~1          0.1700012 0.009258796