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我正在尝试向（semPlot 1.1.2）提供一个 lavaan.mi 对象（使用runMI()semTools 0.5-2 的 SEM 建模多重估算数据。 ）。semPaths()这样做会返回错误：

Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function ‘semPlotModel_S4’ for signature ‘"lavaan.mi"’

这在GitHub 上被标记为“问题” ，但我将不胜感激建议的解决方法。这是一个例子：

# Libraries 
library(mice)
library(semTools)
library(lavaan)
library(semPlot)

# Create DF 
HSMiss <- HolzingerSwineford1939[,paste("x", 1:9, sep="")]
randomMiss <- rbinom(prod(dim(HSMiss)), 1, 0.1)
randomMiss <- matrix(as.logical(randomMiss), nrow=nrow(HSMiss))
HSMiss[randomMiss] <- NA

# Specify model
HS.model <- ' visual  =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed   =~ x7 + x8 + x9 '

# Fit the model 
model_fit <- runMI(HS.model, 
              data=HSMiss,
              m = 5, 
              miPackage="mice",
              fun="sem")

# Attempt to create SEM plot
semPaths(model_fit)
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1 回答 1

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所以我使用的“解决方案”是简单地将 MI 模型的参数估计值替换为从“lavaan”对象创建的“semPlotModel”对象：

# Libraries 
library(mice)
library(semTools)
library(lavaan)
library(semPlot)

# Create DF 
HSMiss <- HolzingerSwineford1939[,paste("x", 1:9, sep="")]
randomMiss <- rbinom(prod(dim(HSMiss)), 1, 0.1)
randomMiss <- matrix(as.logical(randomMiss), nrow=nrow(HSMiss))
HSMiss[randomMiss] <- NA

# Specify model
HS.model <- ' visual  =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed   =~ x7 + x8 + x9 '

# Create a 'dummy' object, with the same model structure as we'll use in the MI method 
model_fit <- sem(HS.model,  data=HSMiss)

# Create a dummy semplot object 
SEMPLOT <- semPlot::semPlotModel(model_fit)

# Fit the actual model 
model_fit <- runMI(HS.model, 
                   data=HSMiss,
                   m = 5, 
                   miPackage="mice",
                   fun="sem")

# Extract the direct results from the SEM with MI data 
desired_output <- data.frame(standardizedsolution(model_fit))

# Subsitute the desired parameter estimates for the desired ones, in the semplot object 
SEMPLOT@Pars$std <- desired_output$est.std

# Create SEM plot
semPaths(SEMPLOT)
于 2019-12-16T17:08:53.653 回答