Description Usage Arguments Details Value Author(s) See Also Examples
Create SimMatrix
or SimVector
object that specifies
Pattern of fixed/freed parameters for analysis
Population parameter values for data generation
Any model misspecification (true population parameter is different than the one specified) for these parameters.
Each matrix in the Lisrelstyle notation is specified in this way (e.g. LY, PS, and TE) and is used to create a model analysis template and a data generation template for simulation through the model
function.
1 2 
free 
Required matrix or vector where each element represents a fixed or freed parameter used for analysis with structural equation models. Parameters can be freed by setting the corresponding element in the matrix to 
popParam 
Optional matrix or vector of identical dimension to the free matrix whose elements contain population parameter values for data generation in simulation. For simlutation, each free parameter requires a population parameter value, which is a quoted numeric value. Parameters that don't have population values are left as empty strings.
Population parameters can also be drawn from a distribution. This is done by wrapping a call to create 1 value from an existing random generation function in quotes: e.g If a random population parameter is constrained to equality in the free matrix, each drawn population parameter value will be the same. More details on data generation is available in To simplify the most common case, 
misspec 
Optional matrix or vector of identical dimension to the free matrix whose elements contain population parameter values for specifying misspecification. Elements of the misspec matrix contain population parameters that are added to parameters that are fixed or have an existing population value. These parameters are also quoted numeric strings, and can optionally be drawn from distributions as described above. To simplify the most common case, misspec can take 1 value or distribution and create a matrix or vector that assigns that value or distribution to all previously specified fixed parameters. Details about misspecification are included the data generation functions. 
symmetric 
Set as 
Bind is the first step in the bind
> model
> sim
workflow of simsem, and this document outlines the user interface or language used to describe these simulations. This interface, while complex, enables a wide array of simulation specifications for structural equation models by building on LISRELstyle parameter specifications.
In simulations supported by simsem, a given parameter may be either fixed or freed for analysis, but may optionally also have a population value or distribution for data generation, or a value or distribution of misspecification. The purpose of bind is to stack these multiple meanings of a parameter into an object recognized by simsem, a SimMatrix
. Each matrix in the Lisrel notation (e.g. LY, PS, TE, BE) becomes a SimMatrix
, and is passed to the function model
, which builds the data generation template and an analysis template (a lavaan parameter table), collectively forming a SimSem
object, which can be passed to the function sim
for simulation.
Note that any (dim)names
attributes will be set to NULL for any vectors or matrices passed to free
, popParam
, or misspec
in order to prevent errors elsewhere in the workflow. To set custom variable names, please use any of the indLab
, facLab
, covLab
, or groupLab
arguments to model()
.
SimMatrix
or SimVector
object that used for model specification for analysis and data generation in simsem.
Patrick Miller (University of Notre Dame; pmille13@nd.edu), Sunthud Pornprasertmanit (psunthud@gmail.com)
model
To combine simMatrix objects into a complete data analysis and data generation template, which is a SimSem
object
generate
To generate data using the simsem template.
analyze
To analyze real or generated data using the simsem template.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  loading < matrix(0, 6, 2)
loading[1:3, 1] < NA
loading[4:6, 2] < NA
loadingValues < matrix(0, 6, 2)
loadingValues[1:3, 1] < 0.7
loadingValues[4:6, 2] < 0.7
LY < bind(loading, loadingValues)
summary(LY)
# Set both factor correlations to .05
latent.cor < matrix(NA, 2, 2)
diag(latent.cor) < 1
RPS < binds(latent.cor, 0.5)
# Misspecify all error covarainces
error.cor < matrix(0, 6, 6)
diag(error.cor) < NA
RTE < binds(error.cor,1,"runif(1,.05,.05)")

Loading required package: lavaan
This is lavaan 0.523.1097
lavaan is BETA software! Please report any bugs.
###############################################################################################
This is simsem 0.513
simsem is BETA software! Please report any bugs.
simsem was developed at the University of Kansas Center for Research Methods and Data Analysis.
###############################################################################################
[1] "Random Full Matrix Object."
[1] "Free/Fixed Parameters:"
[,1] [,2]
[1,] NA 0
[2,] NA 0
[3,] NA 0
[4,] 0 NA
[5,] 0 NA
[6,] 0 NA
[1] "Population Parameters/Starting Values:"
[,1] [,2]
[1,] "0.7" ""
[2,] "0.7" ""
[3,] "0.7" ""
[4,] "" "0.7"
[5,] "" "0.7"
[6,] "" "0.7"
[1] "Model misspecification"
[,1]
[1,] NaN
[1] "Symmetric: FALSE"
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