我想知道是否有任何R包能够进行多级因子分析?
答案 0 :(得分:5)
结帐http://openmx.psyc.virginia.edu/。最初的MX软件(http://www.vcu.edu/mx/)可以进行多级因子分析(参见[1]),所以我认为openMx
也可以做到这一点。
[1]:Bai,Yun和Poon,Wai-Yin(2009)'使用Mx分析两级结构方程中的跨层效应 模型',结构方程模型:多学科期刊,16:1,163 - 178
答案 1 :(得分:4)
OpenMx 2.5.1版本包括inst/models/nightly/mplus-ex9.6.R(见下文),它实现了具有连续因子指标和协变量的两级CFA。有关比较,请参见示例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)))