用于多级因子分析的R包

Question

我想知道是否有任何R包能够进行多级因子分析？

Answer 1

结帐http://openmx.psyc.virginia.edu/。最初的MX软件（http://www.vcu.edu/mx/）可以进行多层次因子分析（例如见[1]），所以我想openMx也可以做到这一点。

[1]：Bai，Yun，Poon，Wai-Yin（2009）'使用Mx分析两级结构方程中的交叉效应 Models'，Structural Equation Modeling：A Multidisciplinary Journal，16：1,163 - 178

Answer 2

OpenMx 2.5.1发布包含inst/models/nightly/mplus-ex9.6.R（见下文），该实现具有连续因子指标和协变量的两级CFA。作为比较，参见实施例9.6，https://www.statmodel.com/usersguide/chapter9.shtml

library(OpenMx) 

set.seed(1) 
ex96 <- suppressWarnings(try(read.table("models/nightly/data/ex9.6.dat"))) 
if (is(ex96, "try-error")) ex96 <- read.table("data/ex9.6.dat") 

ex96$V8 <- as.integer(ex96$V8) 
bData <- ex96[!duplicated(ex96$V8), c('V7', 'V8')] 
colnames(bData) <- c('w', 'clusterID') 
wData <- ex96[,-match(c('V7'), colnames(ex96))] 
colnames(wData) <- c(paste0('y', 1:4), paste0('x', 1:2), 'clusterID') 

bModel <- mxModel('between', type="RAM", 
        mxData(type="raw", observed=bData, primaryKey="clusterID"), 
        latentVars = c("lw", "fb"), 
        mxPath("one", "lw", labels="data.w", free=FALSE), 
        mxPath("fb", arrows=2, labels="psiB"), 
        mxPath("lw", 'fb', labels="phi1")) 

wModel <- mxModel('within', type="RAM", bModel, 
        mxData(type="raw", observed=wData, sort=FALSE), #[abs(wData$clusterID - 41)<= 25,] 
        manifestVars = paste0('y', 1:4), 
        latentVars = c('fw', paste0("xe", 1:2)), 
        mxPath("one", paste0('y', 1:4), values=runif(4), labels=paste0("gam0", 1:4)), 
        mxPath("one", paste0('xe', 1:2), labels=paste0('data.x',1:2), free=FALSE), 
        mxPath(paste0('xe', 1:2), "fw", labels=paste0('gam', 1:2, '1')), 
        mxPath('fw', arrows=2, values=1.1, labels="varFW"), 
        mxPath('fw', paste0('y', 1:4), free=c(FALSE, rep(TRUE, 3)), 
         values=c(1,runif(3)), labels=paste0("loadW", 1:4)), 
        mxPath('between.fb', paste0('y', 1:4), values=c(1,runif(3)), 
         free=c(FALSE, rep(TRUE, 3)), labels=paste0("loadB", 1:4), 
         joinKey="clusterID"), 
        mxPath(paste0('y', 1:4), arrows=2, values=rlnorm(4), labels=paste0("thetaW", 1:4)))