# Comparing Regression Parameters of Different lavaan Models Fitted to the Same Dataset The function `dmlavaan` compares model parameters from different `lavaan` models fitted to the same dataset. This leads to dependent coefficients. Statistical inference is either conducted by M-estimation (i.e., robust sandwich method; `method="bootstrap"`) or bootstrap (`method="bootstrap"`). See Mize et al. (2019) or Weesie (1999) for more details. ```r dmlavaan(fun1, args1, fun2, args2, method="sandwich", R=50) ``` ## Arguments - `fun1`: lavaan function of the first model (e.g., `"lavaan"`, `"cfa"`, or `"sem"`) - `args1`: arguments for lavaan function in the first model - `fun2`: lavaan function of the second model (e.g., `"lavaan"`, `"cfa"`, or `"sem"`) - `args2`: arguments for lavaan function in the second model - `method`: estimation method for standard errors - `R`: Number of bootstrap samples ## Details In bootstrap estimation, a normal approximation is applied in the computation of confidence intervals. Hence, `R` could be chosen relatively small. TO DO (not yet implemented): ||| |:--|:--| |1)|inclusion of sampling weights| |2)|cluster robust standard errors in hierarchical sampling| |3)|stratification| ## Returns A list with following entries - **coef**: Model parameters of both models - **vcov**: Covariance matrix of model parameters of both models - **partable**: Parameter table containing all univariate model parameters - ****: More entries ## References Mize, T.D., Doan, L., & Long, J.S. (2019). A general framework for comparing predictions and marginal effects across models. **Sociological Methodology, 49**(1), 152-189. tools:::Rd_expr_doi("10.1177/0081175019852763") Weesie, J. (1999) Seemingly unrelated estimation and the cluster-adjusted sandwich estimator. **Stata Technical Bulletin, 9**, 231-248. ## Examples ```r ## Not run: ############################################################################ # EXAMPLE 1: Confirmatory factor analysis with and without fourth item ############################################################################# #**** simulate data N <- 200 # number of persons I <- 4 # number of items # loadings and error correlations lam <- seq(.7,.4, len=I) PSI <- diag( 1-lam^2 ) # define some model misspecification sd_error <- .1 S1 <- matrix( c( -1.84, 0.39,-0.68, 0.13, 0.39,-1.31,-0.07,-0.27, -0.68,-0.07, 0.90, 1.91, 0.13,-0.27, 1.91,-0.56 ), nrow=4, ncol=4, byrow=TRUE) S1 <- ( S1 - mean(S1) ) / sd(S1) * sd_error Sigma <- lam %*% t(lam) + PSI + S1 dat <- MASS::mvrnorm(n=N, mu=rep(0,I), Sigma=Sigma) colnames(dat) <- paste0("X",1:4) dat <- as.data.frame(dat) rownames(Sigma) <- colnames(Sigma) <- colnames(dat) #*** define two lavaan models lavmodel1 <- "F=~ X1 + X2 + X3 + X4" lavmodel2 <- "F=~ X1 + X2 + X3" #*** define lavaan estimation arguments and functions fun2 <- fun1 <- "cfa" args1 <- list( model=lavmodel1, data=dat, std.lv=TRUE, estimator="MLR") args2 <- args1 args2$model <- lavmodel2 #* run model comparison res1 <- sirt::dmlavaan( fun1=fun1, args1=args1, fun2=fun2, args2=args2) # inspect results sirt:::print_digits(res1$partable, digits=3) ## End(Not run) ```

dmlavaan function