Description Usage Arguments Details Value Author(s) See Also Examples
This function can be used to generate data, analyze the generated data, and summarized into a result object where parameter estimates, standard errors, fit indices, and other characteristics of each replications are saved.
1 2 3 4 5 6 7 8 9 10 11 | sim(nRep, model, n, generate = NULL, ..., rawData = NULL, miss = NULL, datafun=NULL,
lavaanfun = "lavaan", outfun=NULL, outfundata = NULL, pmMCAR = NULL,
pmMAR = NULL, facDist = NULL, indDist = NULL, errorDist = NULL,
sequential = FALSE, saveLatentVar = FALSE, modelBoot = FALSE, realData = NULL,
covData = NULL, maxDraw = 50, misfitType = "f0", misfitBounds = NULL,
averageNumMisspec = FALSE, optMisfit=NULL, optDraws = 50,
createOrder = c(1, 2, 3), aux = NULL, group = NULL, mxFit = FALSE,
mxMixture = FALSE, citype = NULL, cilevel = 0.95, seed = 123321, silent = FALSE,
multicore = options('simsem.multicore')[[1]], cluster = FALSE,
numProc = NULL, paramOnly = FALSE, dataOnly=FALSE, smartStart=FALSE,
previousSim = NULL, completeRep = FALSE, stopOnError = FALSE)
|
nRep |
Number of replications. If any of the |
model |
There are three options for this argument: 1. |
n |
Sample size(s). In single-group models, either a single |
generate |
There are four options for this argument: 1. |
rawData |
There are two options for this argument: 1. a list of data frames to be used in simulations or 2. a population data. If a list of data frames is specified, the |
miss |
A missing data template created using the |
datafun |
A function to be applied to each generated data set across replications. |
lavaanfun |
The character of the function name used in running lavaan model ( |
outfun |
A function to be applied to the |
outfundata |
A function to be applied to the |
pmMCAR |
The percentage of data completely missing at random (0 <= pmMCAR < 1). Either a single value or a vector of values in order to vary pmMCAR across replications (with length equal to nRep or a divisor of nRep). The |
pmMAR |
The percentage of data missing at random (0 <= pmCAR < 1). Either a single value or a vector of values in order to vary pmCAR across replications (with length equal to nRep or a divisor of nRep). The |
facDist |
Factor distributions. Either a list of |
indDist |
Indicator distributions. Either a list of |
errorDist |
An object or list of objects of type |
sequential |
If |
saveLatentVar |
If |
modelBoot |
When specified, a model-based bootstrap is used for data generation (for use with the |
realData |
A data.frame containing real data. Generated data will follow the distribution of this data set. |
covData |
A data.frame containing covariate data, which can have any distributions. This argument is required when users specify |
maxDraw |
The maximum number of attempts to draw a valid set of parameters (no negative error variance, standardized coefficients over 1). |
misfitType |
Character vector indicating the fit measure used to assess the misfit of a set of parameters. Can be "f0", "rmsea", "srmr", or "all". |
misfitBounds |
Vector that contains upper and lower bounds of the misfit measure. Sets of parameters drawn that are not within these bounds are rejected. |
averageNumMisspec |
If |
optMisfit |
Character vector of either "min" or "max" indicating either maximum or minimum optimized misfit. If not null, the set of parameters out of the number of draws in "optDraws" that has either the maximum or minimum misfit of the given misfit type will be returned. |
optDraws |
Number of parameter sets to draw if optMisfit is not null. The set of parameters with the maximum or minimum misfit will be returned. |
createOrder |
The order of 1) applying equality/inequality constraints, 2) applying misspecification, and 3) fill unspecified parameters (e.g., residual variances when total variances are specified). The specification of this argument is a vector of different orders of 1 (constraint), 2 (misspecification), and 3 (filling parameters). For example, |
aux |
The names of auxiliary variables saved in a vector. |
group |
The name of the group variable. This argument is used when |
mxFit |
A logical whether to find an extensive list of fit measures (which will be slower). This argument is applicable when |
mxMixture |
A logical whether to the analysis model is a mixture model. This argument is applicable when |
citype |
Type of confidence interval. For the current version, this argument will be forwarded to the |
cilevel |
Confidence level. For the current version, this argument will be forwarded to the |
seed |
Random number seed. Note that the seed number is always fixed in the |
silent |
If |
multicore |
Users may put |
cluster |
Not applicable now. Used to specify nodes in hpc in order to be parallelizable. |
numProc |
Number of processors for using multiple processors. If it is |
paramOnly |
If |
dataOnly |
If |
smartStart |
Defaults to FALSE. If TRUE, population parameter values that are real numbers will be used as starting values. When tested in small models, the time elapsed when using population values as starting values was greater than the time reduced during analysis, and convergence rates were not affected. |
previousSim |
A result object that users wish to add the results of the current simulation in |
completeRep |
If |
stopOnError |
If |
... |
Additional arguments to be passed to |
This function is executed as follows: 1. parameters are drawn from the specified data-generation model (applicable only simsem model template, SimSem
, only), 2. the drawn (or the specified) parameters are used to create data, 3. data can be transformed using the datafun
argument, 4. specified missingness (if any) is imposed, 5. data are analyzed using the specified analysis model, 6. parameter estimates, standard errors, fit indices, and other characteristics of a replication are extracted, 7. additional outputs (if any) are extracted using the outfun
argument, and 8. results across replications are summarized in a result object, SimResult
).
There are six ways to provide or generate data in this function:
SimSem
can be used as a template to generate data, which can be created by the model
function. The SimSem
can be specified in the generate
argument.
lavaan
script, parameter table for the lavaan
package, or a list of arguments for the simulateData
function. The lavaan
script can be specified in the generate
argument.
MxModel
object from the OpenMx
package. The MxModel
object can be specified in the generate
argument.
A list of raw data for each replication can be provided for the rawData
argument. The sim
function will analyze each data and summarize the result. Note that the generate
, n
and nRep
could not be specified if the list of raw data is provided.
Population data can be provided for the rawData
argument. The sim
function will randomly draw sample data sets and analyze data. Note that the n
and nRep
must be specified if the population data are provided. The generate
argument must not be specified.
A function can be used to generate data. The function must take sample size in a numeric format (or a vector of numerics for multiple groups) and return a data frame of the generated data set. Note that parameter values and their standardized values can be provided by using the attributes of the resulting data set. That is, users can assign parameter values and standardized parameter values to attr(data, "param")
and attr(data, "stdparam")
.
Note that all generated or provided data can be transformed based on Bollen-Stine approach by providing a real data in the realData
argument if any of the first three methods are used.
There are four ways to analyze the data sets for each replication by setting the model
argument as
SimSem
can be used as a template for data analysis.
lavaan
script, parameter table for the lavaan
package, or a list of arguments for the lavaan
, sem
, cfa
, or growth
function. Note that if the desired function to analyze data can be specified in the lavaanfun
argument, which the default is the lavaan
function
MxModel
object from the OpenMx
package. The object does not need to have data inside. Note that if users need an extensive fit indices, the mxFit
argument should be specified as TRUE
. If users wish to analyze by mixture model, the mxMixture
argument should be TRUE
such that the sim
function knows how to handle the data.
A function that takes a data set and returns a list. The list must contain at least three objects: a vector of parameter estimates (coef
), a vector of standard error (se
), and the convergence status as TRUE
or FALSE
(converged
). There are seven optional objects in the list: a vector of fit indices (fit
), a vector of standardized estimates (std
), a vector of standard errors of standardized estimates (stdse
), fraction missing type I (FMI1
), fraction missing type II (FMI2
), lower bounds of confidence intervals (cilower
), and upper bounds of confidence intervals (ciupper
). Note that the coef
, se
, std
, stdse
, FMI1
, FMI2
, cilower
, and ciupper
must be a vector with names. The name of those vectors across different objects must be the same. Users may optionally specify other objects in the list; however, the results of the other objects will not be automatically combined. Users need to specify the outfun
argument to get the extra objects. For example, researchers may specify residuals
in the list. The outfun argument should have the function as follows: function(obj) obj$residuals
.
Any combination of data-generation methods and data-analysis methods are valid. For example, data can be simulated using lavaan script and analyzed by MxModel
. Paralleled processing can be enabled using the multicore
argument.
A result object (SimResult
)
Patrick Miller (University of Notre Dame; pmille13@nd.edu) Sunthud Pornprasertmanit (psunthud@gmail.com)
SimResult
for the resulting output description
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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | # Please go to https://simsem.org/ for more examples in the Vignettes.
## Example of using simsem model template
library(lavaan)
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")
# In reality, more than 5 replications are needed.
Output <- sim(5, CFA.Model, n=200)
summary(Output)
## Example of using lavaan model syntax
popModel <- "
f1 =~ 0.7*y1 + 0.7*y2 + 0.7*y3
f2 =~ 0.7*y4 + 0.7*y5 + 0.7*y6
f1 ~~ 1*f1
f2 ~~ 1*f2
f1 ~~ 0.5*f2
y1 ~~ 0.49*y1
y2 ~~ 0.49*y2
y3 ~~ 0.49*y3
y4 ~~ 0.49*y4
y5 ~~ 0.49*y5
y6 ~~ 0.49*y6
"
analysisModel <- "
f1 =~ y1 + y2 + y3
f2 =~ y4 + y5 + y6
"
Output <- sim(5, model=analysisModel, n=200, generate=popModel, std.lv=TRUE, lavaanfun = "cfa")
summary(Output)
## Example of using raw data as the population
pop <- data.frame(y1 = rnorm(100000, 0, 1), y2 = rnorm(100000, 0, 1))
covModel <- " y1 ~~ y2 "
Output <- sim(5, model=covModel, n=200, rawData=pop, lavaanfun = "cfa")
summary(Output)
## Example of user-defined functions:
# data-transformation function: Transforming to standard score
fun1 <- function(data) {
temp <- scale(data)
as.data.frame(temp)
}
# additional-output function: Extract modification indices from lavaan
fun2 <- function(out) {
inspect(out, "mi")
}
# In reality, more than 5 replications are needed.
Output <- sim(5, CFA.Model,n=200,datafun=fun1, outfun=fun2)
summary(Output)
# Get modification indices
getExtraOutput(Output)
## Example of additional output: Comparing latent variable correlation
outfundata <- function(out, data) {
predictcor <- lavInspect(out, "est")$psi[2, 1]
latentvar <- attr(data, "latentVar")[,c("f1", "f2")]
latentcor <- cor(latentvar)[2,1]
latentcor - predictcor
}
Output <- sim(5, CFA.Model, n=200, sequential = TRUE, saveLatentVar = TRUE,
outfundata = outfundata)
getExtraOutput(Output)
## Example of analyze using a function
analyzeFUN <- function(data) {
out <- lm(y2 ~ y1, data=data)
coef <- coef(out)
se <- sqrt(diag(vcov(out)))
fit <- c(loglik = as.numeric(logLik(out)))
converged <- TRUE # Assume to be convergent all the time
return(list(coef = coef, se = se, fit = fit, converged = converged))
}
Output <- sim(5, model=analyzeFUN, n=200, rawData=pop, lavaanfun = "cfa")
summary(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
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RESULT OBJECT
Model Type
[1] "cfa"
========= Fit Indices Cutoffs ============
Alpha
Fit Indices 0.1 0.05 0.01 0.001 Mean SD
chisq 16.166 17.613 18.770 19.031 8.543 6.920
aic 3145.232 3152.617 3158.525 3159.855 3113.358 36.958
bic 3207.900 3215.285 3221.193 3222.523 3176.026 36.958
rmsea 0.069 0.076 0.082 0.083 0.026 0.038
cfi 0.973 0.969 0.965 0.964 0.990 0.016
tli 0.950 0.941 0.934 0.933 1.000 0.047
srmr 0.038 0.041 0.044 0.044 0.025 0.012
========= Parameter Estimates and Standard Errors ============
Estimate Average Estimate SD Average SE Power (Not equal 0) Std Est
f1=~y1 0.718 0.033 0.074 1.0 0.724
f1=~y2 0.654 0.084 0.072 1.0 0.675
f1=~y3 0.688 0.114 0.075 1.0 0.683
f2=~y4 0.656 0.045 0.072 1.0 0.676
f2=~y5 0.707 0.104 0.074 1.0 0.702
f2=~y6 0.708 0.100 0.073 1.0 0.719
f1~~f2 0.439 0.157 0.081 1.0 0.439
y1~~y1 0.470 0.095 0.078 1.0 0.474
y2~~y2 0.503 0.071 0.074 1.0 0.540
y3~~y3 0.522 0.036 0.079 1.0 0.529
y4~~y4 0.510 0.057 0.073 1.0 0.542
y5~~y5 0.503 0.055 0.077 1.0 0.505
y6~~y6 0.455 0.039 0.074 1.0 0.480
y1~1 0.024 0.064 0.070 0.0 0.024
y2~1 0.029 0.110 0.068 0.2 0.030
y3~1 0.016 0.126 0.071 0.2 0.012
y4~1 -0.068 0.071 0.069 0.2 -0.070
y5~1 -0.029 0.056 0.071 0.0 -0.031
y6~1 -0.018 0.086 0.069 0.0 -0.018
Std Est SD Std Ave SE Average Param Average Bias Coverage
f1=~y1 0.047 0.055 0.70 0.018 1.0
f1=~y2 0.073 0.057 0.70 -0.046 0.8
f1=~y3 0.074 0.056 0.70 -0.012 0.8
f2=~y4 0.035 0.056 0.70 -0.044 1.0
f2=~y5 0.057 0.055 0.70 0.007 0.8
f2=~y6 0.061 0.055 0.70 0.008 0.8
f1~~f2 0.157 0.081 0.50 -0.061 0.6
y1~~y1 0.069 0.080 0.51 -0.040 0.8
y2~~y2 0.096 0.076 0.51 -0.007 1.0
y3~~y3 0.096 0.077 0.51 0.012 1.0
y4~~y4 0.048 0.075 0.51 0.000 1.0
y5~~y5 0.082 0.077 0.51 -0.007 1.0
y6~~y6 0.083 0.078 0.51 -0.055 1.0
y1~1 0.064 0.071 0.00 0.024 1.0
y2~1 0.113 0.071 0.00 0.029 0.8
y3~1 0.126 0.071 0.00 0.016 0.8
y4~1 0.072 0.071 0.00 -0.068 0.8
y5~1 0.058 0.071 0.00 -0.029 1.0
y6~1 0.087 0.071 0.00 -0.018 1.0
========= Correlation between Fit Indices ============
chisq aic bic rmsea cfi tli srmr
chisq 1.000 -0.021 -0.021 0.980 -0.970 -0.991 0.966
aic -0.021 1.000 1.000 -0.059 0.097 0.027 0.176
bic -0.021 1.000 1.000 -0.059 0.097 0.027 0.176
rmsea 0.980 -0.059 -0.059 1.000 -0.976 -0.951 0.937
cfi -0.970 0.097 0.097 -0.976 1.000 0.933 -0.954
tli -0.991 0.027 0.027 -0.951 0.933 1.000 -0.944
srmr 0.966 0.176 0.176 0.937 -0.954 -0.944 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
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RESULT OBJECT
Model Type
[1] "lavaan"
========= Fit Indices Cutoffs ============
Alpha
Fit Indices 0.1 0.05 0.01 0.001 Mean SD
chisq 17.320 18.701 19.806 20.054 12.645 4.598
aic 3101.268 3112.605 3121.674 3123.715 3063.279 40.642
bic 3144.146 3155.483 3164.552 3166.593 3106.157 40.642
rmsea 0.075 0.081 0.086 0.087 0.048 0.027
cfi 0.972 0.967 0.964 0.963 0.985 0.014
tli 0.947 0.938 0.932 0.930 0.973 0.026
srmr 0.047 0.050 0.052 0.052 0.038 0.009
========= Parameter Estimates and Standard Errors ============
Estimate Average Estimate SD Average SE Power (Not equal 0) Std Est
f1=~y1 0.658 0.085 0.071 1 0.678
f1=~y2 0.635 0.049 0.068 1 0.684
f1=~y3 0.737 0.043 0.070 1 0.762
f2=~y4 0.667 0.058 0.073 1 0.678
f2=~y5 0.701 0.130 0.072 1 0.709
f2=~y6 0.728 0.064 0.076 1 0.709
y1~~y1 0.503 0.098 0.072 1 0.536
y2~~y2 0.459 0.074 0.065 1 0.531
y3~~y3 0.391 0.034 0.072 1 0.419
y4~~y4 0.525 0.085 0.075 1 0.539
y5~~y5 0.467 0.106 0.074 1 0.489
y6~~y6 0.523 0.107 0.082 1 0.495
f1~~f2 0.481 0.112 0.078 1 0.481
Std Est SD Std Ave SE Average Param Average Bias Coverage
f1=~y1 0.071 0.054 0.70 -0.042 1.0
f1=~y2 0.049 0.054 0.70 -0.065 1.0
f1=~y3 0.031 0.051 0.70 0.037 1.0
f2=~y4 0.034 0.056 0.70 -0.033 1.0
f2=~y5 0.098 0.054 0.70 0.001 0.6
f2=~y6 0.064 0.055 0.70 0.028 1.0
y1~~y1 0.095 0.073 0.49 0.013 0.8
y2~~y2 0.067 0.073 0.49 -0.031 0.8
y3~~y3 0.046 0.078 0.49 -0.099 1.0
y4~~y4 0.047 0.076 0.49 0.035 1.0
y5~~y5 0.134 0.075 0.49 -0.023 1.0
y6~~y6 0.095 0.077 0.49 0.033 1.0
f1~~f2 0.112 0.078 0.50 -0.019 0.8
========= Correlation between Fit Indices ============
chisq aic bic rmsea cfi tli srmr
chisq 1.000 0.111 0.111 0.980 -0.996 -0.996 0.948
aic 0.111 1.000 1.000 0.220 -0.152 -0.152 0.322
bic 0.111 1.000 1.000 0.220 -0.152 -0.152 0.322
rmsea 0.980 0.220 0.220 1.000 -0.975 -0.975 0.916
cfi -0.996 -0.152 -0.152 -0.975 1.000 1.000 -0.964
tli -0.996 -0.152 -0.152 -0.975 1.000 1.000 -0.964
srmr 0.948 0.322 0.322 0.916 -0.964 -0.964 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
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RESULT OBJECT
Model Type
[1] "lavaan"
========= Fit Indices Cutoffs ============
Alpha
Fit Indices 0.1 0.05 0.01 0.001 Mean SD
chisq 0.000 0.000 0.000 0.000 0.000 0.000
aic 1142.098 1145.983 1149.090 1149.790 1121.339 19.069
bic 1151.993 1155.878 1158.985 1159.685 1131.234 19.069
rmsea 0.000 0.000 0.000 0.000 0.000 0.000
cfi 1.000 1.000 1.000 1.000 1.000 0.000
tli 1.000 1.000 1.000 1.000 1.000 0.000
srmr 0.000 0.000 0.000 0.000 0.000 0.000
========= Parameter Estimates and Standard Errors ============
Estimate Average Estimate SD Average SE Power (Not equal 0) Std Est
y1~~y2 -0.078 0.091 0.068 0.2 -0.079
y1~~y1 0.900 0.100 0.090 1.0 1.000
y2~~y2 1.026 0.067 0.103 1.0 1.000
Std Est SD Std Ave SE Average Param Average Bias Coverage
y1~~y2 0.094 0.07 0 -0.078 0.8
y1~~y1 0.000 0.00 1 -0.100 0.8
y2~~y2 0.000 0.00 1 0.026 1.0
========= Correlation between Fit Indices ============
chisq aic bic cfi srmr
chisq 1.000 -0.535 -0.535 -1.000 0.989
aic -0.535 1.000 1.000 0.535 -0.610
bic -0.535 1.000 1.000 0.535 -0.610
cfi -1.000 0.535 0.535 1.000 -0.989
srmr 0.989 -0.610 -0.610 -0.989 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
Progress: 1 / 5
Progress: 2 / 5
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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 16.166 17.613 18.770 19.031 8.543 6.920
aic 3183.866 3198.673 3210.518 3213.184 3144.952 40.195
bic 3246.534 3261.341 3273.186 3275.852 3207.620 40.195
rmsea 0.069 0.076 0.082 0.083 0.026 0.038
cfi 0.973 0.969 0.965 0.964 0.990 0.016
tli 0.950 0.941 0.934 0.933 1.000 0.047
srmr 0.038 0.041 0.044 0.044 0.025 0.012
========= Parameter Estimates and Standard Errors ============
Estimate Average Estimate SD Average SE Power (Not equal 0) Std Est
f1=~y1 0.722 0.046 0.074 1 0.724
f1=~y2 0.673 0.073 0.075 1 0.675
f1=~y3 0.682 0.074 0.074 1 0.683
f2=~y4 0.674 0.035 0.074 1 0.676
f2=~y5 0.700 0.057 0.074 1 0.702
f2=~y6 0.718 0.061 0.074 1 0.719
f1~~f2 0.439 0.157 0.081 1 0.439
y1~~y1 0.471 0.068 0.079 1 0.474
y2~~y2 0.537 0.095 0.078 1 0.540
y3~~y3 0.526 0.096 0.078 1 0.529
y4~~y4 0.539 0.048 0.077 1 0.542
y5~~y5 0.502 0.082 0.077 1 0.505
y6~~y6 0.477 0.083 0.077 1 0.480
y1~1 0.000 0.000 0.071 0 0.000
y2~1 0.000 0.000 0.071 0 0.000
y3~1 0.000 0.000 0.071 0 0.000
y4~1 0.000 0.000 0.071 0 0.000
y5~1 0.000 0.000 0.071 0 0.000
y6~1 0.000 0.000 0.071 0 0.000
Std Est SD Std Ave SE Average Param Average Bias Coverage
f1=~y1 0.047 0.055 0.70 0.022 1.0
f1=~y2 0.073 0.057 0.70 -0.027 1.0
f1=~y3 0.074 0.056 0.70 -0.018 1.0
f2=~y4 0.035 0.056 0.70 -0.026 1.0
f2=~y5 0.057 0.055 0.70 0.000 1.0
f2=~y6 0.061 0.055 0.70 0.018 1.0
f1~~f2 0.157 0.081 0.50 -0.061 0.6
y1~~y1 0.069 0.080 0.51 -0.039 1.0
y2~~y2 0.096 0.076 0.51 0.027 0.8
y3~~y3 0.096 0.077 0.51 0.016 0.8
y4~~y4 0.048 0.075 0.51 0.029 1.0
y5~~y5 0.082 0.077 0.51 -0.008 1.0
y6~~y6 0.083 0.078 0.51 -0.033 1.0
y1~1 0.000 0.071 0.00 0.000 1.0
y2~1 0.000 0.071 0.00 0.000 1.0
y3~1 0.000 0.071 0.00 0.000 1.0
y4~1 0.000 0.071 0.00 0.000 1.0
y5~1 0.000 0.071 0.00 0.000 1.0
y6~1 0.000 0.071 0.00 0.000 1.0
========= Correlation between Fit Indices ============
chisq aic bic rmsea cfi tli srmr
chisq 1.000 -0.371 -0.371 0.980 -0.970 -0.991 0.966
aic -0.371 1.000 1.000 -0.196 0.188 0.489 -0.324
bic -0.371 1.000 1.000 -0.196 0.188 0.489 -0.324
rmsea 0.980 -0.196 -0.196 1.000 -0.976 -0.951 0.937
cfi -0.970 0.188 0.188 -0.976 1.000 0.933 -0.954
tli -0.991 0.489 0.489 -0.951 0.933 1.000 -0.944
srmr 0.966 -0.324 -0.324 0.937 -0.954 -0.944 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
[[1]]
NULL
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[[5]]
NULL
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[[1]]
[1] -0.01218423
[[2]]
[1] -0.1084645
[[3]]
[1] 0.1592328
[[4]]
[1] 0.07325282
[[5]]
[1] 0.01852426
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RESULT OBJECT
Model Type
[1] "function"
========= Fit Indices Cutoffs ============
Alpha
Fit Indices 0.1 0.05 0.01 0.001 Mean SD
loglik -280.369 -280.261 -280.175 -280.155 -284.848 5.572
========= Parameter Estimates and Standard Errors ============
Estimate Average Estimate SD Average SE Power (Not equal 0)
(Intercept) 0.056 0.081 0.072 0.2
y1 -0.085 0.104 0.076 0.2
Average Param Average Bias Coverage
(Intercept) 0.002 0.054 0.8
y1 -0.002 -0.083 0.8
================== 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
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