tests/testthat/test-scriptUni.R

In jsakaluk/dySEM: Dyadic Structural Equation Modeling

#scriptUni testing
# Configural --------------------------------------------------------------
# ---- FF ----
test_that("scriptUni produces correct output for constr_dy_meas = none, constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  expect_equal(
    
    scriptUni(
      dvn,
      scaleset = "FF",
      lvname = "Sat",
      constr_dy_meas = "none",
      constr_dy_struct = "none"
    ),
    
    "#Measurement Model\n\n#Loadings\nSatDy=~NA*sat.g.1_1+sat.g.1_2+sat.g.1_3+sat.g.1_4+sat.g.1_5+sat.g.2_1+sat.g.2_2+sat.g.2_3+sat.g.2_4+sat.g.2_5\n\n#Intercepts\nsat.g.1_1 ~ 1\nsat.g.1_2 ~ 1\nsat.g.1_3 ~ 1\nsat.g.1_4 ~ 1\nsat.g.1_5 ~ 1\n\nsat.g.2_1 ~ 1\nsat.g.2_2 ~ 1\nsat.g.2_3 ~ 1\nsat.g.2_4 ~ 1\nsat.g.2_5 ~ 1\n\n#Residual Variances\nsat.g.1_1 ~~ sat.g.1_1\nsat.g.1_2 ~~ sat.g.1_2\nsat.g.1_3 ~~ sat.g.1_3\nsat.g.1_4 ~~ sat.g.1_4\nsat.g.1_5 ~~ sat.g.1_5\n\nsat.g.2_1 ~~ sat.g.2_1\nsat.g.2_2 ~~ sat.g.2_2\nsat.g.2_3 ~~ sat.g.2_3\nsat.g.2_4 ~~ sat.g.2_4\nsat.g.2_5 ~~ sat.g.2_5\n\n#Residual Covariances\nsat.g.1_1 ~~ sat.g.2_1\nsat.g.1_2 ~~ sat.g.2_2\nsat.g.1_3 ~~ sat.g.2_3\nsat.g.1_4 ~~ sat.g.2_4\nsat.g.1_5 ~~ sat.g.2_5\n\n#Structural Model\n\n#Latent (Co)Variances\nSatDy ~~ 1*SatDy\n\n#Latent Means\nSatDy ~ 0*1"
    )
  
  }
          )
test_that("scriptUni produces correct number of parameter estimates for consrt_dy_meas = none, constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #knowns for model w/ estimated mean structure:
    #var_num <- 10
    #knowns <- (var_num*(var_num+1)/2)+var_num
  
  #script configural invariance model w/ scaleset = FF
  script <- scriptUni(
    dvn,
    scaleset = "FF",
    lvname = "Sat",
    constr_dy_meas = "none",
    constr_dy_struct = "none"
    )
  
  #fit model
  mod <- lavaan::cfa(script, data = commitmentQ)
    
  #manually calculated free parameters (see LINK)
  #my_param <- 35
  
  lav_param <- as.double(
    lavaan::fitmeasures(
      mod, 
      "npar")
    )
  
  expect_equal(
    lav_param,
    35
  )
  
}
          )
test_that("scriptUni produces correct df for constr_dy_meas = none, constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #knowns for model w/ estimated mean structure:
    #var_num <- 10
    #knowns <- (var_num*(var_num+1)/2)+var_num
  
  
  #script configural invariance model w/ scaleset = FF
  script <- scriptUni(
    dvn,
    scaleset = "FF",
    lvname = "Sat",
    constr_dy_meas = "none",
    constr_dy_struct = "none"
    )
  
  #fit model
  mod <- lavaan::cfa(script, data = commitmentQ)
    
  #manually calculated free parameters (see LINK)
  #my_param <- 35
  
  #calculated df (should be 29)
  #my_df <- knowns-my_param
  
  lav_df <- as.double(
    lavaan::fitmeasures(
      mod, 
      "df")
    )
  
  expect_equal(
    lav_df,
    30
  )
  
}
          )

# ---- MV ----
test_that("scriptUni produces correct output for constr_dy_meas = none, constr_dy_struct = none, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  expect_equal(
    
    scriptUni(
      dvn,
      scaleset = "MV",
      lvname = "Sat",
      constr_dy_meas = "none",
      constr_dy_struct = "none"
    ),
    
    "#Measurement Model\n\n#Loadings\nSatDy=~1*sat.g.1_1+sat.g.1_2+sat.g.1_3+sat.g.1_4+sat.g.1_5+sat.g.2_1+sat.g.2_2+sat.g.2_3+sat.g.2_4+sat.g.2_5\n\n#Intercepts\nsat.g.1_1 ~ 0*1\nsat.g.1_2 ~ 1\nsat.g.1_3 ~ 1\nsat.g.1_4 ~ 1\nsat.g.1_5 ~ 1\n\nsat.g.2_1 ~ 1\nsat.g.2_2 ~ 1\nsat.g.2_3 ~ 1\nsat.g.2_4 ~ 1\nsat.g.2_5 ~ 1\n\n#Residual Variances\nsat.g.1_1 ~~ sat.g.1_1\nsat.g.1_2 ~~ sat.g.1_2\nsat.g.1_3 ~~ sat.g.1_3\nsat.g.1_4 ~~ sat.g.1_4\nsat.g.1_5 ~~ sat.g.1_5\n\nsat.g.2_1 ~~ sat.g.2_1\nsat.g.2_2 ~~ sat.g.2_2\nsat.g.2_3 ~~ sat.g.2_3\nsat.g.2_4 ~~ sat.g.2_4\nsat.g.2_5 ~~ sat.g.2_5\n\n#Residual Covariances\nsat.g.1_1 ~~ sat.g.2_1\nsat.g.1_2 ~~ sat.g.2_2\nsat.g.1_3 ~~ sat.g.2_3\nsat.g.1_4 ~~ sat.g.2_4\nsat.g.1_5 ~~ sat.g.2_5\n\n#Structural Model\n\n#Latent (Co)Variances\nSatDy ~~ NA*SatDy\n\n#Latent Means\nSatDy ~ NA*1"
    
  )
  
}
)

# ---- FF vs. MV ----
test_that("scriptUni produces same df for constr_dy_meas = none, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #Script configural invariance model w/ scaleset = FF
  script_ff <- scriptUni(
    dvn, 
    scaleset = "FF",
    lvname = "Sat", 
    constr_dy_meas = "none", 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_ff <- lavaan::cfa(script_ff, data = commitmentQ)
  
  #get its df
  df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
  
  #Script configural invariance model w/ scaleset = MV
  script_mv <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = "none", 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_mv <- lavaan::cfa(script_mv, data = commitmentQ)
  
  #get its df
  df_mv <- as.double(lavaan::fitmeasures(mod_mv, "df"))
  
  #scale setting should have no impact on df
  expect_equal(df_ff,
               df_mv
  )
  
  
}
)

test_that("scriptUni produces same chisq for constr_dy_meas = none, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #Script configural invariance model w/ scaleset = FF
  script_ff <- scriptUni(
    dvn, 
    scaleset = "FF",
    lvname = "Sat", 
    constr_dy_meas = "none", 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_ff <- lavaan::cfa(script_ff, data = commitmentQ)
  
  #get its chisq
  chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
  
  #Script configural invariance model w/ scaleset = MV
  script_mv <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = "none", 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_mv <- lavaan::cfa(script_mv, data = commitmentQ)
  
  #get its chisq
  chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
  
  #scale setting should have no impact on df
  expect_equal(chisq_ff,
               chisq_mv
  )
  
  
}
)

# Loading  --------------------------------------------------------
# ---- FF ----
test_that("scriptUni produces correct output for constr_dy_meas = loadings, constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  expect_equal(
    
    scriptUni(
      dvn,
      scaleset = "FF",
      lvname = "Sat",
      constr_dy_meas = "loadings",
      constr_dy_struct = "none"
    ),
    
    "#Measurement Model\n\n#Loadings\nSatDy=~NA*sat.g.1_1+lxg1*sat.g.1_1+lxg2*sat.g.1_2+lxg3*sat.g.1_3+lxg4*sat.g.1_4+lxg5*sat.g.1_5+lxg1*sat.g.2_1+lxg2*sat.g.2_2+lxg3*sat.g.2_3+lxg4*sat.g.2_4+lxg5*sat.g.2_5\n\n#Intercepts\nsat.g.1_1 ~ 1\nsat.g.1_2 ~ 1\nsat.g.1_3 ~ 1\nsat.g.1_4 ~ 1\nsat.g.1_5 ~ 1\n\nsat.g.2_1 ~ 1\nsat.g.2_2 ~ 1\nsat.g.2_3 ~ 1\nsat.g.2_4 ~ 1\nsat.g.2_5 ~ 1\n\n#Residual Variances\nsat.g.1_1 ~~ sat.g.1_1\nsat.g.1_2 ~~ sat.g.1_2\nsat.g.1_3 ~~ sat.g.1_3\nsat.g.1_4 ~~ sat.g.1_4\nsat.g.1_5 ~~ sat.g.1_5\n\nsat.g.2_1 ~~ sat.g.2_1\nsat.g.2_2 ~~ sat.g.2_2\nsat.g.2_3 ~~ sat.g.2_3\nsat.g.2_4 ~~ sat.g.2_4\nsat.g.2_5 ~~ sat.g.2_5\n\n#Residual Covariances\nsat.g.1_1 ~~ sat.g.2_1\nsat.g.1_2 ~~ sat.g.2_2\nsat.g.1_3 ~~ sat.g.2_3\nsat.g.1_4 ~~ sat.g.2_4\nsat.g.1_5 ~~ sat.g.2_5\n\n#Structural Model\n\n#Latent (Co)Variances\nSatDy ~~ 1*SatDy\n\n#Latent Means\nSatDy ~ 0*1"
  )

}
)
test_that("scriptUni produces correct number of parameter estimates for consrt_dy_meas = loadings, constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  
  #knowns for model w/ estimated mean structure:
  #var_num <- 10
  #knowns <- (var_num*(var_num+1)/2)+var_num
  
  #script loading invariance model w/ scaleset = FF
  script <- scriptUni(
    dvn,
    scaleset = "FF",
    lvname = "Sat",
    constr_dy_meas = "loadings",
    constr_dy_struct = "none"
  )
  
  #fit model
  mod <- lavaan::cfa(script, data = commitmentQ)
  
  #manually calculated free parameters (see LINK)
  #my_param <- 30
  
  lav_param <- as.double(
    lavaan::fitmeasures(
      mod, 
      "npar")
  )
  
  expect_equal(
    lav_param,
    30
  )
}
)
test_that("scriptUni produces correct df for constr_dy_meas = loadings, constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #knowns for model w/ estimated mean structure:
  #var_num <- 10
  #knowns <- (var_num*(var_num+1)/2)+var_num
  
  
  #script configural invariance model w/ scaleset = FF
  script <- scriptUni(
    dvn,
    scaleset = "FF",
    lvname = "Sat",
    constr_dy_meas = "loadings",
    constr_dy_struct = "none"
  )
  
  #fit model
  mod <- lavaan::cfa(script, data = commitmentQ)
  
  #manually calculated free parameters (see LINK)
  #my_param <- 30
  
  #calculated df (should be 35)
  #my_df <- knowns-my_param
  
  lav_df <- as.double(
    lavaan::fitmeasures(
      mod, 
      "df")
  )
  
  expect_equal(
    lav_df,
    35
  )
  
}
)

# ---- MV ----
test_that("scriptUni produces correct output for constr_dy_meas = loadings, constr_dy_struct = none, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  expect_equal(
    
    scriptUni(
      dvn,
      scaleset = "MV",
      lvname = "Sat",
      constr_dy_meas = "loadings",
      constr_dy_struct = "none"
    ),
    
    "#Measurement Model\n\n#Loadings\nSatDy=~1*sat.g.1_1+lxg1*sat.g.1_1+lxg2*sat.g.1_2+lxg3*sat.g.1_3+lxg4*sat.g.1_4+lxg5*sat.g.1_5+lxg1*sat.g.2_1+lxg2*sat.g.2_2+lxg3*sat.g.2_3+lxg4*sat.g.2_4+lxg5*sat.g.2_5\n\n#Intercepts\nsat.g.1_1 ~ 0*1\nsat.g.1_2 ~ 1\nsat.g.1_3 ~ 1\nsat.g.1_4 ~ 1\nsat.g.1_5 ~ 1\n\nsat.g.2_1 ~ 1\nsat.g.2_2 ~ 1\nsat.g.2_3 ~ 1\nsat.g.2_4 ~ 1\nsat.g.2_5 ~ 1\n\n#Residual Variances\nsat.g.1_1 ~~ sat.g.1_1\nsat.g.1_2 ~~ sat.g.1_2\nsat.g.1_3 ~~ sat.g.1_3\nsat.g.1_4 ~~ sat.g.1_4\nsat.g.1_5 ~~ sat.g.1_5\n\nsat.g.2_1 ~~ sat.g.2_1\nsat.g.2_2 ~~ sat.g.2_2\nsat.g.2_3 ~~ sat.g.2_3\nsat.g.2_4 ~~ sat.g.2_4\nsat.g.2_5 ~~ sat.g.2_5\n\n#Residual Covariances\nsat.g.1_1 ~~ sat.g.2_1\nsat.g.1_2 ~~ sat.g.2_2\nsat.g.1_3 ~~ sat.g.2_3\nsat.g.1_4 ~~ sat.g.2_4\nsat.g.1_5 ~~ sat.g.2_5\n\n#Structural Model\n\n#Latent (Co)Variances\nSatDy ~~ NA*SatDy\n\n#Latent Means\nSatDy ~ NA*1"    
  )
  
}
)

# ---- FF vs. MV ----
test_that("scriptUni produces same df for constr_dy_meas = loadings, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #Script loading invariance model w/ scaleset = FF
  script_ff <- scriptUni(
    dvn, 
    scaleset = "FF",
    lvname = "Sat", 
    constr_dy_meas = "loadings", 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_ff <- lavaan::cfa(script_ff, data = commitmentQ)
  
  #get its df
  df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
  
  #Script loading invariance model w/ scaleset = MV
  script_mv <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = "loadings", 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_mv <- lavaan::cfa(script_mv, data = commitmentQ)
  
  #get its df
  df_mv <- as.double(lavaan::fitmeasures(mod_mv, "df"))
  
  #scale setting should have no impact on df
  expect_equal(df_ff,
               df_mv
  )
  
}
)
test_that("scriptUni produces same chisq for constr_dy_meas = loadings, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #Script loading invariance model w/ scaleset = FF
  script_ff <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = "loadings", 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_ff <- lavaan::cfa(script_ff, data = commitmentQ)
  
  #get its chisq
  chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
  
  #Script loading invariance model w/ scaleset = MV
  script_mv <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = "loadings", 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_mv <- lavaan::cfa(script_mv, data = commitmentQ)
  
  #get its chisq
  chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
  
  #scale setting should have no impact on df
  expect_equal(chisq_ff,
               chisq_mv
  )
  
  
}
)



# Intercept ----------------------------------------------------------------
# ---- FF ----
test_that("scriptUni produces correct output for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  expect_equal(
    
    scriptUni(
      dvn,
      scaleset = "FF",
      lvname = "Sat",
      constr_dy_meas = c("loadings", "intercepts"),
      constr_dy_struct = "none"
    ),
    
    "#Measurement Model\n\n#Loadings\nSatDy=~NA*sat.g.1_1+lxg1*sat.g.1_1+lxg2*sat.g.1_2+lxg3*sat.g.1_3+lxg4*sat.g.1_4+lxg5*sat.g.1_5+lxg1*sat.g.2_1+lxg2*sat.g.2_2+lxg3*sat.g.2_3+lxg4*sat.g.2_4+lxg5*sat.g.2_5\n\n#Intercepts\nsat.g.1_1 ~ tx1*1\nsat.g.1_2 ~ tx2*1\nsat.g.1_3 ~ tx3*1\nsat.g.1_4 ~ tx4*1\nsat.g.1_5 ~ tx5*1\n\nsat.g.2_1 ~ tx1*1\nsat.g.2_2 ~ tx2*1\nsat.g.2_3 ~ tx3*1\nsat.g.2_4 ~ tx4*1\nsat.g.2_5 ~ tx5*1\n\n#Residual Variances\nsat.g.1_1 ~~ sat.g.1_1\nsat.g.1_2 ~~ sat.g.1_2\nsat.g.1_3 ~~ sat.g.1_3\nsat.g.1_4 ~~ sat.g.1_4\nsat.g.1_5 ~~ sat.g.1_5\n\nsat.g.2_1 ~~ sat.g.2_1\nsat.g.2_2 ~~ sat.g.2_2\nsat.g.2_3 ~~ sat.g.2_3\nsat.g.2_4 ~~ sat.g.2_4\nsat.g.2_5 ~~ sat.g.2_5\n\n#Residual Covariances\nsat.g.1_1 ~~ sat.g.2_1\nsat.g.1_2 ~~ sat.g.2_2\nsat.g.1_3 ~~ sat.g.2_3\nsat.g.1_4 ~~ sat.g.2_4\nsat.g.1_5 ~~ sat.g.2_5\n\n#Structural Model\n\n#Latent (Co)Variances\nSatDy ~~ 1*SatDy\n\n#Latent Means\nSatDy ~ 0*1"
  )
  
}
)
test_that("scriptUni produces correct number of parameter estimates for consrt_dy_meas = c(loadings, intercepts), constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  
  #knowns for model w/ estimated mean structure:
  #var_num <- 10
  #knowns <- (var_num*(var_num+1)/2)+var_num
  
  #script loading invariance model w/ scaleset = FF
  script <- scriptUni(
    dvn,
    scaleset = "FF",
    lvname = "Sat",
    constr_dy_meas = c("loadings", "intercepts"),
    constr_dy_struct = "none"
  )
  
  #fit model
  mod <- lavaan::cfa(script, data = commitmentQ)
  
  #manually calculated free parameters (see LINK)
  #my_param <- 25
  
  lav_param <- as.double(
    lavaan::fitmeasures(
      mod, 
      "npar")
  )
  
  expect_equal(
    lav_param,
    25
  )
}
)
test_that("scriptUni produces correct df for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #knowns for model w/ estimated mean structure:
  #var_num <- 10
  #knowns <- (var_num*(var_num+1)/2)+var_num
  
  
  #script configural invariance model w/ scaleset = FF
  script <- scriptUni(
    dvn,
    scaleset = "FF",
    lvname = "Sat",
    constr_dy_meas = c("loadings", "intercepts"),
    constr_dy_struct = "none"
  )
  
  #fit model
  mod <- lavaan::cfa(script, data = commitmentQ)
  
  #manually calculated free parameters (see LINK)
  #my_param <- 25
  
  #calculated df (should be 40)
  #my_df <- knowns-my_param
  
  lav_df <- as.double(
    lavaan::fitmeasures(
      mod, 
      "df")
  )
  
  expect_equal(
    lav_df,
    40
  )
  
}
)

# ---- MV ----
test_that("scriptUni produces correct output for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = none, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  expect_equal(
    
    scriptUni(
      dvn,
      scaleset = "MV",
      lvname = "Sat",
      constr_dy_meas = c("loadings", "intercepts"),
      constr_dy_struct = "none"
    ),
    
    "#Measurement Model\n\n#Loadings\nSatDy=~1*sat.g.1_1+lxg1*sat.g.1_1+lxg2*sat.g.1_2+lxg3*sat.g.1_3+lxg4*sat.g.1_4+lxg5*sat.g.1_5+lxg1*sat.g.2_1+lxg2*sat.g.2_2+lxg3*sat.g.2_3+lxg4*sat.g.2_4+lxg5*sat.g.2_5\n\n#Intercepts\nsat.g.1_1 ~ 0*1 + tx1*1\nsat.g.1_2 ~ tx2*1\nsat.g.1_3 ~ tx3*1\nsat.g.1_4 ~ tx4*1\nsat.g.1_5 ~ tx5*1\n\nsat.g.2_1 ~ tx1*1\nsat.g.2_2 ~ tx2*1\nsat.g.2_3 ~ tx3*1\nsat.g.2_4 ~ tx4*1\nsat.g.2_5 ~ tx5*1\n\n#Residual Variances\nsat.g.1_1 ~~ sat.g.1_1\nsat.g.1_2 ~~ sat.g.1_2\nsat.g.1_3 ~~ sat.g.1_3\nsat.g.1_4 ~~ sat.g.1_4\nsat.g.1_5 ~~ sat.g.1_5\n\nsat.g.2_1 ~~ sat.g.2_1\nsat.g.2_2 ~~ sat.g.2_2\nsat.g.2_3 ~~ sat.g.2_3\nsat.g.2_4 ~~ sat.g.2_4\nsat.g.2_5 ~~ sat.g.2_5\n\n#Residual Covariances\nsat.g.1_1 ~~ sat.g.2_1\nsat.g.1_2 ~~ sat.g.2_2\nsat.g.1_3 ~~ sat.g.2_3\nsat.g.1_4 ~~ sat.g.2_4\nsat.g.1_5 ~~ sat.g.2_5\n\n#Structural Model\n\n#Latent (Co)Variances\nSatDy ~~ NA*SatDy\n\n#Latent Means\nSatDy ~ NA*1"  )
  
}
)

# ---- FF vs. MV ----
test_that("scriptUni produces same df for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #Script intercept invariance model w/ scaleset = FF
  script_ff <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = c("loadings", "intercepts"), 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_ff <- lavaan::cfa(script_ff, data = commitmentQ)
  
  #get its df
  df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
  
  #Script intercept invariance model w/ scaleset = MV
  script_mv <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = c("loadings", "intercepts"), 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_mv <- lavaan::cfa(script_mv, data = commitmentQ)
  
  #get its df
  df_mv <- as.double(lavaan::fitmeasures(mod_mv, "df"))
  
  #scale setting should have no impact on df
  expect_equal(df_ff,
               df_mv
  )
  
}
)
test_that("scriptUni produces same chisq for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #Script intercept invariance model w/ scaleset = FF
  script_ff <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = c("loadings", "intercepts"), 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_ff <- lavaan::cfa(script_ff, data = commitmentQ)
  
  #get its chisq
  chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
  
  #Script intercept invariance model w/ scaleset = MV
  script_mv <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = c("loadings", "intercepts"), 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_mv <- lavaan::cfa(script_mv, data = commitmentQ)
  
  #get its chisq
  chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
  
  #scale setting should have no impact on df
  expect_equal(chisq_ff,
               chisq_mv
  )
  
}
)

# Residual ----------------------------------------------------------------
# ---- FF ----
test_that("scriptUni produces correct output for constr_dy_meas = c(loadings, intercepts, residuals), constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  expect_equal(
    
    scriptUni(
      dvn,
      scaleset = "FF",
      lvname = "Sat",
      constr_dy_meas = c("loadings", "intercepts", "residuals"),
      constr_dy_struct = "none"
    ),
    
    "#Measurement Model\n\n#Loadings\nSatDy=~NA*sat.g.1_1+lxg1*sat.g.1_1+lxg2*sat.g.1_2+lxg3*sat.g.1_3+lxg4*sat.g.1_4+lxg5*sat.g.1_5+lxg1*sat.g.2_1+lxg2*sat.g.2_2+lxg3*sat.g.2_3+lxg4*sat.g.2_4+lxg5*sat.g.2_5\n\n#Intercepts\nsat.g.1_1 ~ tx1*1\nsat.g.1_2 ~ tx2*1\nsat.g.1_3 ~ tx3*1\nsat.g.1_4 ~ tx4*1\nsat.g.1_5 ~ tx5*1\n\nsat.g.2_1 ~ tx1*1\nsat.g.2_2 ~ tx2*1\nsat.g.2_3 ~ tx3*1\nsat.g.2_4 ~ tx4*1\nsat.g.2_5 ~ tx5*1\n\n#Residual Variances\nsat.g.1_1 ~~ thx1*sat.g.1_1\nsat.g.1_2 ~~ thx2*sat.g.1_2\nsat.g.1_3 ~~ thx3*sat.g.1_3\nsat.g.1_4 ~~ thx4*sat.g.1_4\nsat.g.1_5 ~~ thx5*sat.g.1_5\n\nsat.g.2_1 ~~ thx1*sat.g.2_1\nsat.g.2_2 ~~ thx2*sat.g.2_2\nsat.g.2_3 ~~ thx3*sat.g.2_3\nsat.g.2_4 ~~ thx4*sat.g.2_4\nsat.g.2_5 ~~ thx5*sat.g.2_5\n\n#Residual Covariances\nsat.g.1_1 ~~ sat.g.2_1\nsat.g.1_2 ~~ sat.g.2_2\nsat.g.1_3 ~~ sat.g.2_3\nsat.g.1_4 ~~ sat.g.2_4\nsat.g.1_5 ~~ sat.g.2_5\n\n#Structural Model\n\n#Latent (Co)Variances\nSatDy ~~ 1*SatDy\n\n#Latent Means\nSatDy ~ 0*1"
  )
  
}
)
test_that("scriptUni produces correct number of parameter estimates for consrt_dy_meas = c(loadings, intercepts, residuals), constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  
  #knowns for model w/ estimated mean structure:
  #var_num <- 10
  #knowns <- (var_num*(var_num+1)/2)+var_num
  
  #script loading invariance model w/ scaleset = FF
  script <- scriptUni(
    dvn,
    scaleset = "FF",
    lvname = "Sat",
    constr_dy_meas = c("loadings", "intercepts", "residuals"),
    constr_dy_struct = "none"
  )
  
  #fit model
  mod <- lavaan::cfa(script, data = commitmentQ)
  
  #manually calculated free parameters (see LINK)
  #my_param <- 20
  
  lav_param <- as.double(
    lavaan::fitmeasures(
      mod, 
      "npar")
  )
  
  expect_equal(
    lav_param,
    20
  )
}
)
test_that("scriptUni produces correct df for constr_dy_meas = c(loadings, intercepts, residuals), constr_dy_struct = none, and scaleset = FF", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #knowns for model w/ estimated mean structure:
  #var_num <- 10
  #knowns <- (var_num*(var_num+1)/2)+var_num
  
  
  #script configural invariance model w/ scaleset = FF
  script <- scriptUni(
    dvn,
    scaleset = "FF",
    lvname = "Sat",
    constr_dy_meas = c("loadings", "intercepts", "residuals"),
    constr_dy_struct = "none"
  )
  
  #fit model
  mod <- lavaan::cfa(script, data = commitmentQ)
  
  #manually calculated free parameters (see LINK)
  #my_param <- 20
  
  #calculated df (should be 45)
  #my_df <- knowns-my_param
  
  lav_df <- as.double(
    lavaan::fitmeasures(
      mod, 
      "df")
  )
  
  expect_equal(
    lav_df,
    45
  )
  
}
)

# ---- MV ----
test_that("scriptUni produces correct output for constr_dy_meas = c(loadings, intercepts, residuals), constr_dy_struct = none, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  expect_equal(
    
    scriptUni(
      dvn,
      scaleset = "MV",
      lvname = "Sat",
      constr_dy_meas = c("loadings", "intercepts", "residuals"),
      constr_dy_struct = "none"
    ),
    
    "#Measurement Model\n\n#Loadings\nSatDy=~1*sat.g.1_1+lxg1*sat.g.1_1+lxg2*sat.g.1_2+lxg3*sat.g.1_3+lxg4*sat.g.1_4+lxg5*sat.g.1_5+lxg1*sat.g.2_1+lxg2*sat.g.2_2+lxg3*sat.g.2_3+lxg4*sat.g.2_4+lxg5*sat.g.2_5\n\n#Intercepts\nsat.g.1_1 ~ 0*1 + tx1*1\nsat.g.1_2 ~ tx2*1\nsat.g.1_3 ~ tx3*1\nsat.g.1_4 ~ tx4*1\nsat.g.1_5 ~ tx5*1\n\nsat.g.2_1 ~ tx1*1\nsat.g.2_2 ~ tx2*1\nsat.g.2_3 ~ tx3*1\nsat.g.2_4 ~ tx4*1\nsat.g.2_5 ~ tx5*1\n\n#Residual Variances\nsat.g.1_1 ~~ thx1*sat.g.1_1\nsat.g.1_2 ~~ thx2*sat.g.1_2\nsat.g.1_3 ~~ thx3*sat.g.1_3\nsat.g.1_4 ~~ thx4*sat.g.1_4\nsat.g.1_5 ~~ thx5*sat.g.1_5\n\nsat.g.2_1 ~~ thx1*sat.g.2_1\nsat.g.2_2 ~~ thx2*sat.g.2_2\nsat.g.2_3 ~~ thx3*sat.g.2_3\nsat.g.2_4 ~~ thx4*sat.g.2_4\nsat.g.2_5 ~~ thx5*sat.g.2_5\n\n#Residual Covariances\nsat.g.1_1 ~~ sat.g.2_1\nsat.g.1_2 ~~ sat.g.2_2\nsat.g.1_3 ~~ sat.g.2_3\nsat.g.1_4 ~~ sat.g.2_4\nsat.g.1_5 ~~ sat.g.2_5\n\n#Structural Model\n\n#Latent (Co)Variances\nSatDy ~~ NA*SatDy\n\n#Latent Means\nSatDy ~ NA*1"  
  )
  
}
)

# ---- FF vs. MV ----
test_that("scriptUni produces same df for constr_dy_meas = c(loadings, intercepts, residuals), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #Script residual invariance model w/ scaleset = FF
  script_ff <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = c("loadings", "intercepts", "residuals"), 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_ff <- lavaan::cfa(script_ff, data = commitmentQ)
  
  #get its df
  df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
  
  #Script residual invariance model w/ scaleset = MV
  script_mv <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = c("loadings", "intercepts", "residuals"), 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_mv <- lavaan::cfa(script_mv, data = commitmentQ)
  
  #get its df
  df_mv <- as.double(lavaan::fitmeasures(mod_mv, "df"))
  
  #scale setting should have no impact on df
  expect_equal(df_ff,
               df_mv
  )
  
}
)
test_that("scriptUni produces same chisq for constr_dy_meas = c(loadings, intercepts, residuals), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
  
  dvn <- scrapeVarCross(
    commitmentQ, 
    x_order = "spi", 
    x_stem = "sat.g", 
    x_delim1 = ".",
    x_delim2="_", 
    distinguish_1="1", 
    distinguish_2="2"
  )
  
  #Script residual invariance model w/ scaleset = FF
  script_ff <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = c("loadings", "intercepts", "residual"), 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_ff <- lavaan::cfa(script_ff, data = commitmentQ)
  
  #get its chisq
  chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
  
  #Script residual invariance model w/ scaleset = MV
  script_mv <- scriptUni(
    dvn, 
    scaleset = "MV",
    lvname = "Sat", 
    constr_dy_meas = c("loadings", "intercepts", "residual"), 
    constr_dy_struct = "none"
  )
  
  #Fit model w FF
  mod_mv <- lavaan::cfa(script_mv, data = commitmentQ)
  
  #get its chisq
  chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
  
  #scale setting should have no impact on df
  expect_equal(chisq_ff,
               chisq_mv
  )
  
}
)