在R中使用Lavaan的循环找不到变量

Question

我运行了以下代码，以便我的Lavaan模型能够测试不同的预测器。然而，错误说它找不到我的“预测器”。我需要保留“粘贴”行，以便指定它是".x“还是".y”变量。我觉得这是个简单的问题。有人能帮帮我吗？谢谢!

gender_thermometer.x <-sample(0:10, 1000, rep = TRUE)
gender_thermometer.y <-sample(0:10, 1000, rep = TRUE)
COVID_threat.x <-sample(0:10, 1000, rep = TRUE)
COVID_threat.y <-sample(0:10, 1000, rep = TRUE)
d_exhaustion.x <-sample(0:10, 1000, rep = TRUE)
d_relaxation.x <-sample(0:10, 1000, rep = TRUE)
d_not_sharing_negative.x <-sample(0:1, 1000, rep = TRUE)
Couple_ID <-sample(0:100, 1000, rep = TRUE)
data_wide<-data.frame(gender_thermometer.x,gender_thermometer.y,COVID_threat.x,COVID_threat.y,
                      d_exhaustion.x,d_relaxation.x,d_not_sharing_negative.x,Couple_ID)

library(lavaan)
models <- list()

for (i in c( "gender_thermometer","COVID_threat")) {
  
  print(paste0("###################:",i))
  predictor= paste(i,".y",sep = "")
  
  model[[i]] <- '
    level: 1
        d_exhaustion.x ~ b1*d_relaxation.x + c1*d_not_sharing_negative.x + predictor
        d_relaxation.x ~ a1*d_not_sharing_negative.x + predictor
        d_not_sharing_negative.x~f1*predictor
        indirect1:=f1*a1*b1
        indirect11:=f1*c1
    level: 2
        d_exhaustion.x ~ b2*d_relaxation.x + c2*d_not_sharing_negative.x + predictor
        d_relaxation.x ~ a2*d_not_sharing_negative.x + predictor
        d_not_sharing_negative.x~f2*predictor
        indirect2:=f2*a2*b2
        indirect22:=f2*c2
        
'
  
  fit[[i]] <- sem(model = model[[i]], data = data_wide, cluster = "Couple_ID")
  print(summary(fit[[i]]))
}

lav_data_full中的错误(数据=数据，组=组，集群=集群，：lavaan错误:数据集中缺少观测变量:预测器

)

Answer 1

R字符串格式需要像sprintf或paste这样的插值处理程序，就像在分配predictor时所做的那样。目前，模型字符串采用文字预测器，而不是它的基础值。

因此，考虑使用 (C函数的包装器)等字符串格式：

vars <- c("gender_thermometer", "COVID_threat")
models <- vector(mode="list", length=length(vars))

for (i in seq_along(vars)) {  
  print(paste0("###################:", vars[[i]]))
  predictor <- paste0(vars[[i]], ".y")
  
  # STRING WITH %1$s PLACEHOLDERS
  model_string <- '
    level: 1
        d_exhaustion.x ~ b1*d_relaxation.x + c1*d_not_sharing_negative.x + %1$s
        d_relaxation.x ~ a1*d_not_sharing_negative.x + %1$s
        d_not_sharing_negative.x~f1*%1$s
        indirect1:=f1*a1*b1
        indirect11:=f1*c1
    level: 2
        d_exhaustion.x ~ b2*d_relaxation.x + c2*d_not_sharing_negative.x + %1$s
        d_relaxation.x ~ a2*d_not_sharing_negative.x + %1$s
        d_not_sharing_negative.x~f2*%1$s
        indirect2:=f2*a2*b2
        indirect22:=f2*c2
  '
  
  # FORMAT STRING WITH VALUE
  models[[i]] <- sprintf(model_string, predictor)
  cat(models[[i]])

  fit[[i]] <- sem(model = models[[i]], data = data_wide, cluster = "Couple_ID")

  print(summary(fit[[i]]))
}

或者使用gsub，用变量值替换文字预测器：

model_string <- '
    level: 1
        d_exhaustion.x ~ b1*d_relaxation.x + c1*d_not_sharing_negative.x + predictor
        d_relaxation.x ~ a1*d_not_sharing_negative.x + predictor
        d_not_sharing_negative.x~f1*predictor
        indirect1:=f1*a1*b1
        indirect11:=f1*c1
    level: 2
        d_exhaustion.x ~ b2*d_relaxation.x + c2*d_not_sharing_negative.x + predictor
        d_relaxation.x ~ a2*d_not_sharing_negative.x + predictor
        d_not_sharing_negative.x~f2*predictor
        indirect2:=f2*a2*b2
        indirect22:=f2*c2
'

models[[i]] <- gsub("predictor", predictor, model_string)