combineSim: Combine result objects
In simsem: SIMulated Structural Equation Modeling
Description
Usage
Arguments
Value
Author(s)
See Also
Examples
Description
Combine result objects into a single result object
Usage
1
Arguments
...
Result objects, SimResult
Value
A combined result object
Author(s)
Terry Jorgensen (University of Kansas; TJorgensen314@gmail.com),
Sunthud Pornprasertmanit (psunthud@gmail.com)
See Also
Result object (SimResult)
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
loading <- matrix(0, 6, 2)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
LY <- bind(loading, 0.7)
latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPS <- binds(latent.cor, 0.5)
RTE <- binds(diag(6))
VY <- bind(rep(NA,6),2)
CFA.Model <- model(LY = LY, RPS = RPS, RTE = RTE, modelType = "CFA")
Output1 <- sim(5, CFA.Model, n=200, seed=123321)
Output2 <- sim(4, CFA.Model, n=200, seed=324567)
Output3 <- sim(3, CFA.Model, n=200, seed=789987)
Output <- combineSim(Output1, Output2, Output3)
summary(Output)
Example output
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
Progress: 1 / 5
Progress: 2 / 5
Progress: 3 / 5
Progress: 4 / 5
Progress: 5 / 5
Progress: 1 / 4
Progress: 2 / 4
Progress: 3 / 4
Progress: 4 / 4
Progress: 1 / 3
Progress: 2 / 3
Progress: 3 / 3
RESULT OBJECT
Model Type
[1] "cfa"
========= Fit Indices Cutoffs ============
Alpha
Fit Indices 0.1 0.05 0.01 0.001 Mean SD
chisq 11.942 12.217 12.483 12.542 8.473 3.364
aic 3160.580 3165.015 3168.110 3168.807 3123.221 31.279
bic 3223.248 3227.683 3230.778 3231.475 3185.889 31.279
rmsea 0.050 0.051 0.053 0.053 0.023 0.023
cfi 0.985 0.985 0.985 0.985 0.994 0.007
tli 0.972 0.972 0.972 0.971 0.999 0.026
srmr 0.036 0.037 0.037 0.038 0.026 0.007
========= Parameter Estimates and Standard Errors ============
Estimate Average Estimate SD Average SE Power (Not equal 0) Std Est
f1=~y1 0.689 0.070 0.074 1.000 0.695
f1=~y2 0.656 0.078 0.073 1.000 0.676
f1=~y3 0.714 0.073 0.076 1.000 0.700
f2=~y4 0.658 0.055 0.074 1.000 0.669
f2=~y5 0.681 0.070 0.074 1.000 0.694
f2=~y6 0.694 0.094 0.073 1.000 0.707
f1~~f2 0.475 0.112 0.081 1.000 0.475
y1~~y1 0.502 0.054 0.077 1.000 0.514
y2~~y2 0.507 0.083 0.074 1.000 0.539
y3~~y3 0.523 0.058 0.082 1.000 0.508
y4~~y4 0.533 0.053 0.076 1.000 0.552
y5~~y5 0.495 0.081 0.076 1.000 0.516
y6~~y6 0.468 0.039 0.075 1.000 0.496
y1~1 0.016 0.065 0.070 0.083 0.016
y2~1 0.004 0.086 0.069 0.083 0.003
y3~1 0.004 0.079 0.072 0.083 0.003
y4~1 -0.021 0.063 0.070 0.083 -0.022
y5~1 0.004 0.062 0.069 0.000 0.004
y6~1 -0.002 0.062 0.069 0.000 -0.002
Std Est SD Std Ave SE Average Param Average Bias Coverage
f1=~y1 0.053 0.056 0.70 -0.011 0.917
f1=~y2 0.066 0.057 0.70 -0.044 0.833
f1=~y3 0.049 0.056 0.70 0.014 0.917
f2=~y4 0.037 0.057 0.70 -0.042 1.000
f2=~y5 0.053 0.056 0.70 -0.019 0.917
f2=~y6 0.063 0.056 0.70 -0.006 0.833
f1~~f2 0.112 0.081 0.50 -0.025 0.833
y1~~y1 0.072 0.078 0.51 -0.008 1.000
y2~~y2 0.090 0.076 0.51 -0.003 0.917
y3~~y3 0.067 0.078 0.51 0.013 1.000
y4~~y4 0.050 0.076 0.51 0.023 1.000
y5~~y5 0.074 0.078 0.51 -0.015 0.833
y6~~y6 0.084 0.078 0.51 -0.042 1.000
y1~1 0.066 0.071 0.00 0.016 0.917
y2~1 0.089 0.071 0.00 0.004 0.917
y3~1 0.077 0.071 0.00 0.004 0.917
y4~1 0.064 0.071 0.00 -0.021 0.917
y5~1 0.064 0.071 0.00 0.004 1.000
y6~1 0.064 0.071 0.00 -0.002 1.000
========= Correlation between Fit Indices ============
chisq aic bic rmsea cfi tli srmr
chisq 1.000 -0.303 -0.303 0.902 -0.861 -0.994 0.905
aic -0.303 1.000 1.000 -0.234 0.183 0.279 -0.278
bic -0.303 1.000 1.000 -0.234 0.183 0.279 -0.278
rmsea 0.902 -0.234 -0.234 1.000 -0.966 -0.857 0.863
cfi -0.861 0.183 0.183 -0.966 1.000 0.818 -0.850
tli -0.994 0.279 0.279 -0.857 0.818 1.000 -0.892
srmr 0.905 -0.278 -0.278 0.863 -0.850 -0.892 1.000
================== Replications =====================
Number of replications = 12
Number of converged replications = 12
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
simsem documentation built on March 29, 2021, 1:07 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Author(s) See Also Examples
Description
Combine result objects into a single result object
Usage
1 |
Arguments
... |
Result objects, |
Value
A combined result object
Author(s)
Terry Jorgensen (University of Kansas; TJorgensen314@gmail.com), Sunthud Pornprasertmanit (psunthud@gmail.com)
See Also
Result object (SimResult)
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | loading <- matrix(0, 6, 2)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
LY <- bind(loading, 0.7)
latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPS <- binds(latent.cor, 0.5)
RTE <- binds(diag(6))
VY <- bind(rep(NA,6),2)
CFA.Model <- model(LY = LY, RPS = RPS, RTE = RTE, modelType = "CFA")
Output1 <- sim(5, CFA.Model, n=200, seed=123321)
Output2 <- sim(4, CFA.Model, n=200, seed=324567)
Output3 <- sim(3, CFA.Model, n=200, seed=789987)
Output <- combineSim(Output1, Output2, Output3)
summary(Output)
|
Example output
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
Progress: 1 / 5
Progress: 2 / 5
Progress: 3 / 5
Progress: 4 / 5
Progress: 5 / 5
Progress: 1 / 4
Progress: 2 / 4
Progress: 3 / 4
Progress: 4 / 4
Progress: 1 / 3
Progress: 2 / 3
Progress: 3 / 3
RESULT OBJECT
Model Type
[1] "cfa"
========= Fit Indices Cutoffs ============
Alpha
Fit Indices 0.1 0.05 0.01 0.001 Mean SD
chisq 11.942 12.217 12.483 12.542 8.473 3.364
aic 3160.580 3165.015 3168.110 3168.807 3123.221 31.279
bic 3223.248 3227.683 3230.778 3231.475 3185.889 31.279
rmsea 0.050 0.051 0.053 0.053 0.023 0.023
cfi 0.985 0.985 0.985 0.985 0.994 0.007
tli 0.972 0.972 0.972 0.971 0.999 0.026
srmr 0.036 0.037 0.037 0.038 0.026 0.007
========= Parameter Estimates and Standard Errors ============
Estimate Average Estimate SD Average SE Power (Not equal 0) Std Est
f1=~y1 0.689 0.070 0.074 1.000 0.695
f1=~y2 0.656 0.078 0.073 1.000 0.676
f1=~y3 0.714 0.073 0.076 1.000 0.700
f2=~y4 0.658 0.055 0.074 1.000 0.669
f2=~y5 0.681 0.070 0.074 1.000 0.694
f2=~y6 0.694 0.094 0.073 1.000 0.707
f1~~f2 0.475 0.112 0.081 1.000 0.475
y1~~y1 0.502 0.054 0.077 1.000 0.514
y2~~y2 0.507 0.083 0.074 1.000 0.539
y3~~y3 0.523 0.058 0.082 1.000 0.508
y4~~y4 0.533 0.053 0.076 1.000 0.552
y5~~y5 0.495 0.081 0.076 1.000 0.516
y6~~y6 0.468 0.039 0.075 1.000 0.496
y1~1 0.016 0.065 0.070 0.083 0.016
y2~1 0.004 0.086 0.069 0.083 0.003
y3~1 0.004 0.079 0.072 0.083 0.003
y4~1 -0.021 0.063 0.070 0.083 -0.022
y5~1 0.004 0.062 0.069 0.000 0.004
y6~1 -0.002 0.062 0.069 0.000 -0.002
Std Est SD Std Ave SE Average Param Average Bias Coverage
f1=~y1 0.053 0.056 0.70 -0.011 0.917
f1=~y2 0.066 0.057 0.70 -0.044 0.833
f1=~y3 0.049 0.056 0.70 0.014 0.917
f2=~y4 0.037 0.057 0.70 -0.042 1.000
f2=~y5 0.053 0.056 0.70 -0.019 0.917
f2=~y6 0.063 0.056 0.70 -0.006 0.833
f1~~f2 0.112 0.081 0.50 -0.025 0.833
y1~~y1 0.072 0.078 0.51 -0.008 1.000
y2~~y2 0.090 0.076 0.51 -0.003 0.917
y3~~y3 0.067 0.078 0.51 0.013 1.000
y4~~y4 0.050 0.076 0.51 0.023 1.000
y5~~y5 0.074 0.078 0.51 -0.015 0.833
y6~~y6 0.084 0.078 0.51 -0.042 1.000
y1~1 0.066 0.071 0.00 0.016 0.917
y2~1 0.089 0.071 0.00 0.004 0.917
y3~1 0.077 0.071 0.00 0.004 0.917
y4~1 0.064 0.071 0.00 -0.021 0.917
y5~1 0.064 0.071 0.00 0.004 1.000
y6~1 0.064 0.071 0.00 -0.002 1.000
========= Correlation between Fit Indices ============
chisq aic bic rmsea cfi tli srmr
chisq 1.000 -0.303 -0.303 0.902 -0.861 -0.994 0.905
aic -0.303 1.000 1.000 -0.234 0.183 0.279 -0.278
bic -0.303 1.000 1.000 -0.234 0.183 0.279 -0.278
rmsea 0.902 -0.234 -0.234 1.000 -0.966 -0.857 0.863
cfi -0.861 0.183 0.183 -0.966 1.000 0.818 -0.850
tli -0.994 0.279 0.279 -0.857 0.818 1.000 -0.892
srmr 0.905 -0.278 -0.278 0.863 -0.850 -0.892 1.000
================== Replications =====================
Number of replications = 12
Number of converged replications = 12
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
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.