## data

misdat <- read.csv("misdat_example.csv")

# FIML approach 1 ---------------------------------------------------------

# subset data for fiml
data_fiml <- misdat %>% dplyr::select(smd,v,V1)

# Create RAM formualtion
## A
  A1 <- matrix(c(
    0,0,1,0,
    0,0,0,1,
    0,0,0,"0*b1",
    0,0,0,0), byrow=TRUE, ncol=4)
  
  dimnames(A1) <- list(c("smd","V1","delta","eta1"),
                       c("smd","V1","delta", "eta1"))
  A1 <- as.mxMatrix(A1)
  
## S
  S1 <- mxMatrix("Symm", nrow=4, ncol=4, values=0, byrow=TRUE,
                 free=c(FALSE,
                        FALSE, FALSE,
                        FALSE, FALSE, TRUE,
                        FALSE, FALSE, FALSE, TRUE),
                 labels=c("v",
                          NA, NA,
                          NA, NA, "Tau2",
                          NA, NA, NA, "VarV1"),
                 name="S1")
  
## Create an M matrix for the means
  
  M1 <- matrix(c(0,0,"0*b0","0*V1"), nrow=1) 
  dimnames(M1)[[2]] <- c("smd","V1","delta", "eta1")
  M1 <- as.mxMatrix(M1)
  
## Create an F matrix to selecting the observed variables
  F1 <- create.Fmatrix(c(1,1,0,0), name="F", as.mxMatrix=FALSE)
  dimnames(F1) <- list(c("smd","V1"), c("smd","V1","delta", "eta1"))
  F1 <- as.mxMatrix(F1)
  
  
# Fit a model
  mxmodel <- mxModel("FIML", mxData(data_fiml, type="raw"),
                     A1, S1, F1, M1,
                     mxExpectationRAM("A1","S1","F1","M1", dimnames=c("smd","V1","delta", "eta1")),
                     mxFitFunctionML())
  
  fit <- mxRun(mxmodel, silent = TRUE)
 
# Result 
sum<-summary(fit)
sum$parameters

# FIML approach 2 ---------------------------------------------------------

# Fit a model
fit_fiml <- metaFIML(y = smd, v = v, x = V1, data = misdat)

# Result
sum_fiml <- summary(fit_fiml)

round(sum_fiml$coefficients,3)