using lavaan structural regression, with agency as auxiliary covariate in r

To use lavaan for structural regression with an auxiliary covariate in R, you would first need to install and load the lavaan package:

main.r
install.packages("lavaan")
library(lavaan)
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Next, you would need to specify your model in lavaan syntax. I will provide a generic example here, but you would need to adapt it to your specific research question and dataset:

main.r
# Specify the model in lavaan syntax
model <- '
     # define latent variables
          Agency =~ A1 + A2 + A3
          LatentVar1 =~ LV1a + LV1b + LV1c
          LatentVar2 =~ LV2a + LV2b + LV2c
     
     # define observed variables
          OvsVar1 ~~ LV1a + LV2a + Agency
          OvsVar2 ~~ LV1b + LV2b + Agency
          OvsVar3 ~~ LV1c + LV2c + Agency
     
     # define regression paths
          LatentVar2 ~ LatentVar1 + Agency
          OvsVar3 ~ LatentVar1 + LatentVar1*Agency
     '
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In this example, we have three latent variables (Agency, LatentVar1, and LatentVar2), as well as three observed variables (OvsVar1, OvsVar2, and OvsVar3). We are regressing LatentVar2 on both LatentVar1 and Agency, and we are regressing OvsVar3 on LatentVar1, as well as the interaction between LatentVar1 and Agency.

Note that we have included the auxiliary covariate (Agency) in the model, by specifying it as a predictor variable for some of the observed variables.

Once you have specified your model in lavaan syntax, you can use the sem() function to estimate the model, as follows:

main.r
# Estimate the model
fit <- sem(model, data=mydata)
summary(fit)
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Here, mydata is the name of your dataset that you have previously defined. The sem() function will estimate the model using maximum likelihood estimation, and return a summary of the results. You can also use the summary() function to get more detailed output, including the estimates and standard errors for each parameter in the model.