blcfa_psx: Bayesian Covariance Lasso Prior Confirmatory Factor Analysis

In zhanglj37/blcfa: Bayesian Covariance Lasso Prior Confirmatory Factor Analysis

Bayesian Covariance Lasso Prior Confirmatory Factor Analysis

Description

Uses Bayesian covariance lasso Prior confirmatory factor analysis to detect significant corss-loadings and residual correlations and generate the corresponding mplus file. After running this function, you can get an mplus file that includes your model and the significant cross-loadings and residual correlations detected by Bayesian Lasso Prior Confirmatory Factor Analysis.

Usage

blcfa(filename, varnames, usevar, myModel, estimation = "ml", ms = -999999, 
      MCMAX = 10000, N.burn = 5000, bloutput = FALSE,  
	  interval = TRUE, conver_check = TRUE)

Arguments

filename	Name of the data file (eg. "Y.txt"). Make sure the data file is in the format of dat or txt, and the variable name is not included in the file.
varnames	Colnames of your dataset (eg. c("gender",paste("y", 1:19, sep = ""))).
usevar	select a subset of variables for analysis (eg. c(paste("y", 1:19, sep = ""))))
myModel	define your model by a matrix (eg. myModel<-matrix(c( 9,0,0, 1,0,0, 1,0,0, 1,0,0, -1,9,0, -1,1,0, -1,1,0, 0,1,0, 0,0,9, 0,0,1, 0,0,1, 0,0,1, ),ncol=NZ,byr=T). 9:fixed at one for identifing the model, 1:estimate this parameter without shrinkage, -1:estimate this parameter using lasso shrinkage, 0:fixed at zero. or: myModel<-' # Emotion f1 =~ y1 + y2 + y3 + y4 + y5 + y6 + y7 + y8 + y9 + y10 + y11 + y12 f2 =~ y13 + y14 + y15 + y16 f3 =~ y17 + y18 + y19 + y20 + y21 + y22 f4 =~ y23 + y24 + y25 f5 =~ y26 + y27 + y28 ' in this way, no loading will be assigned with lasso shrinkage.
estimation	Estimator in the Mplus: 'ML' or 'Bayes'.
MCMAX	Total number of MCMC samples for inference (the default value is 15000).
N.burn	Number of burn-in MCMC samples. Discarded (the default value is 5000). Besides, if the model does not converge in the N.burn iteration, the result and the mplus file will not be presented, and you need to increase the value of N.burn and MCMAX.
ms	define missing value (the default value is NA, which means null value in your dataset).
bloutput	Results of bayesian covariance lasso prior cfa, incluse: ppp, estimated value, standard error and hpd interval of ly, mu, phi and psx. The default setting is not output these results, if you just want the mplus input file then you don't need to change it.
interval	Detect the significant residual correlations by hpd interval or p-value, the default setting is using hpd interval.
conver_check	TRUE: use two MCMC chains to caculate the EPSR valus and check whether the model converge, FALSE: use one MCMC chain to get the estimates without convergence check

References

Pan, J., Ip, E. H., & Dubé, L. (2017). An alternative to post hoc model modification in confirmatory factor analysis: the Bayesian lasso. Psychological Methods, 22(4), 687???704. Chen, J.S.*, Guo, Z.H., Zhang, L.J., Pan, J.H.* (2020). A Partially Confirmatory Approach to Scale Development with the Bayesian Lasso. Psychological Methods. Advance online publication.

zhanglj37/blcfa documentation built on Oct. 6, 2023, 5:43 a.m.