library(MIIVefa)
#> This is version 0.1.0 of MIIVefa.
#> MIIVefa is BETA software! Please report any bugs.MIIVefa is data-driven algorithm for Exploratory Factor Analysis (EFA) that uses Model Implied Instrumental Variables (MIIVs). The method starts with a one factor model and arrives at a suggested model with enhanced interpretability that allows cross-loadings and correlated errors.
1, Prepare your data.
The input dataframe should be in a wide format: columns being different observations and rows being the specific data entries.
Column names should be clearly labeled.
2, Installing MIIVefa.
In the R console, enter and execute ‘install.packages(“MIIVefa”)’ or ‘devtools::install_github(“https://github.com/lluo0/MIIVefa”)’ after installing the “devtools” package.
Load the MIIVefa by executing ‘library(MIIVefa)’ after installing.
3, Running miivefa.
The only necessarily required input is the raw data matrix.
All 4 arguments are shown below.
‘sigLevel’ is the significance level with a default of 0.05. ‘scalingCrit’ is the specified criterion for selecting the scaling indicator whenever a new latent factor is created and the default is ‘sargan+factorloading_R2.’ And ‘CorrelatedErrors’ is a vector containing correlated error relations between observed variables with a default of NULL.
miivefa(
data = yourdata,
sigLevel = 0.05,
scalingCrit = 'sargan+factorloading_R2',
correlatedErrors = NULL
)The output of a miivefa object contains 2 parts:
1, a suggested model, of which the syntax is identical to a ‘lavaan’ model.
2, a miivsem model fit of the suggested model. The suggested model is run and evaluated using ‘MIIvsem’ and all miivsem attributes can be accessed.