tests/testthat/test-scriptBifac.R
In jsakaluk/dySEM: Dyadic Structural Equation Modeling
# Configural -------------------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "none",
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+sat.g.1_2+sat.g.1_3+sat.g.1_4+sat.g.1_5\nSat2=~NA*sat.g.2_1+sat.g.2_2+sat.g.2_3+sat.g.2_4+sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ 1*Sat1\nSat2 ~~ 1*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ 0*1\nSat2 ~ 0*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac 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 <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "none",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #lavaan::fitmeasures requires convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 45
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
45
)
}
)
test_that(
"scriptBifac 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 <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "none",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 45
#calculated df (should be 20)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
20
)
}
)
# ---- MV ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = "none",
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~1*sat.g.1_1+sat.g.1_2+sat.g.1_3+sat.g.1_4+sat.g.1_5\nSat2=~1*sat.g.2_1+sat.g.2_2+sat.g.2_3+sat.g.2_4+sat.g.2_5\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 ~ 0*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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ NA*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ 0*1"
)
}
)
# ---- FF vs. MV ----
test_that(#failure
"scriptBifac produces same df for constr_dy_meas = none, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script configural invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "none", constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script configural invariance model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "none", constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#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(#pass (but fails in `equal_identical()`)
"scriptBifac produces same chisq for constr_dy_meas = none, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script configural invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "none", constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its df
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script configural invariance model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "none", constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#get its df
chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
#scale setting should have no impact on chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
# Loading ---------------------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ 0*1\nSat2 ~ 0*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac 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 <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 37
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
37
)
}
)
test_that(
"scriptBifac 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 loading invariance model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 37
#calculated df (should be 28)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
28
)
}
)
# ---- MV ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 ~ 0*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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ NA*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ 0*1"
)
}
)
# ---- FF vs. MV ----
test_that(
"scriptBifac produces same df for constr_dy_meas = loadings, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script loading invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "loadings", constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script loading invariance and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "loadings", constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#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(
"scriptBifac produces same chisq for constr_dy_meas = loadings, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script model w/ constr_dy_meas = "loadings" and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "loadings", constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script model w/ constr_dy_meas = "loadings" and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "loadings", constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
#scale setting should have no impact on chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
# Intercept --------------------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac 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 intercept invariance model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 34
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
34
)
}
)
test_that(
"scriptBifac 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 intercept invariance model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 37
#calculated df (should be 31)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
31
)
}
)
# ---- MV ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ NA*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ NA*1"
)
}
)
# ---- FF vs. MV ----
test_that(#failure
"scriptBifac produces same df for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script model w/ constr_dy_meas = c("loadings", "intercepts") and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script model w/ constr_dy_meas = c("loadings", "intercepts") and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#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(#pass (but fails in `equal_identical()`)
"scriptBifac produces same chisq for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script intercept invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script intercept invariance model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
#scale setting should have no impact on chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
# Residual ----------------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"),
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac 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 residual invariance model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"),
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 29
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
29
)
}
)
test_that(
"scriptBifac 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 residual invariance model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"),
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 29
#calculated df (should be 36)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
36
)
}
)
# ---- MV ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"),
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ NA*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ NA*1"
)
}
)
# ---- FF vs. MV ----
test_that(#failure
"scriptBifac produces same df for constr_dy_meas = c(loadings, intercepts, residuals), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script residual invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script residual invariance and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#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(#pass (but fails in `equal_identical()`)
"scriptBifac produces same chisq for constr_dy_meas = c(loadings, intercepts, residuals), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script residual invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script residual invariance model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
#scale setting should have no impact on chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
# Loadings: Source --------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac produces correct output for constr_dy_meas = loadings_source, 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings_source",
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx6*sat.g.2_1+lx7*sat.g.2_2+lx8*sat.g.2_3+lx9*sat.g.2_4+lx10*sat.g.2_5\nSatDy=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5+lx6*sat.g.2_1+lx7*sat.g.2_2+lx8*sat.g.2_3+lx9*sat.g.2_4+lx10*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 (Orthogonal Structure)\nSat1 ~~ 1*Sat1\nSat2 ~~ 1*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ 0*1\nSat2 ~ 0*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac produces correct number of parameter estimates for consrt_dy_meas = loadings_source, 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 source model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings_source",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 35
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
35
)
}
)
test_that(
"scriptBifac produces correct df for constr_dy_meas = loadings_source, 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 source model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings_source",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 35
#calculated df (should be 30)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
30
)
}
)
# ---- MV ----
# ---- FF vs. MV ----
# Loadings: Mutuality --------------------------------------------------------
# ---- FF ----
# ---- MV ----
# ---- FF vs. MV ----
# Latent Variances --------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac produces correct output for constr_dy_meas = loadings, constr_dy_struct = variances 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "variances"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ psix*Sat1\nSat2 ~~ psix*Sat2\nSatDy ~~ NA*SatDy + psix*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ 0*1\nSat2 ~ 0*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac produces correct number of parameter estimates for constr_dy_meas = loadings, constr_dy_struct = variances 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 latent variance equality model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "variances"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 36
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
36
)
}
)
test_that(
"scriptBifac produces correct df for constr_dy_meas = loadings, constr_dy_struct = variances 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 latent variance equality model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "variances"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 36
#calculated df (should be 29)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
29
)
}
)
# ---- MV ----
test_that(
"scriptBifac produces correct output for constr_dy_meas = loadings, constr_dy_struct = variances, 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "variances"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 ~ 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 ~ 0*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 (Orthogonal Structure)\nSat1 ~~ psix*Sat1\nSat2 ~~ psix*Sat2\nSatDy ~~ psix*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ 0*1"
)
}
)
# ---- FF vs. MV ----
test_that(#failure
"scriptBifac produces same df for constr_dy_meas = loadings, constr_dy_struct = variances when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script lvars equality model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = "loadings", constr_dy_struct = "variances", scaleset = "FF")
#Fit model w FF
mod_ff <- suppressWarnings(lavaan::cfa(script_ff, data = commitmentQ))
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script lvars equality model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = "loadings", constr_dy_struct = "variances", scaleset = "MV")
#Fit model w MV
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(#failure
"scriptBifac produces same chisq for constr_dy_meas = loadings, constr_dy_struct = variances when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script lvars equality model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = "loadings", constr_dy_struct = "variances", scaleset = "FF")
#Fit model w FF
mod_ff <- suppressWarnings(lavaan::cfa(script_ff, data = commitmentQ))
#get its chisq
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script lvars equality model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = "loadings", constr_dy_struct = "variances", scaleset = "MV")
#Fit model w MV
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 chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
# Latent Means --------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac produces correct output for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "means"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ alphax*1\nSat2 ~ alphax*1\nSatDy ~ 0*1 + alphax*1"
)
}
)
test_that(#failure
"scriptBifac produces correct number of parameter estimates for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means 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 latent means equality model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "means"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 32
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
32
)
}
)
test_that(#failure
"scriptBifac produces correct df for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means 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 latent means equality model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "means"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 32
#calculated df (should be 33)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
33
)
}
)
# ---- MV ----
test_that("scriptBifac produces correct output for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means, 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "means"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ NA*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ alphax*1\nSat2 ~ alphax*1\nSatDy ~ alphax*1"
)
}
)
# ---- FF vs. MV ----
test_that(#failure
"scriptBifac produces same df for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script lmeans equality model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "means", scaleset = "FF")
#Fit model w FF
mod_ff <- suppressWarnings(lavaan::cfa(script_ff, data = commitmentQ))
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script lmeans equality model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "means", scaleset = "MV")
#Fit model w MV
mod_mv <- suppressWarnings(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(#pass (but fails in `equal_identical()`)
"scriptBifac produces same chisq for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script lmeans equality model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "means", scaleset = "FF")
#Fit model w FF
mod_ff <- suppressWarnings(lavaan::cfa(script_ff, data = commitmentQ))
#get its chisq
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script lmeans equality model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "means", scaleset = "MV")
#Fit model w MV
mod_mv <- suppressWarnings(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 chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
jsakaluk/dySEM documentation built on Feb. 24, 2025, 5:05 p.m.
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# Configural -------------------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "none",
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+sat.g.1_2+sat.g.1_3+sat.g.1_4+sat.g.1_5\nSat2=~NA*sat.g.2_1+sat.g.2_2+sat.g.2_3+sat.g.2_4+sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ 1*Sat1\nSat2 ~~ 1*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ 0*1\nSat2 ~ 0*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac 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 <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "none",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #lavaan::fitmeasures requires convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 45
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
45
)
}
)
test_that(
"scriptBifac 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 <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "none",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 45
#calculated df (should be 20)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
20
)
}
)
# ---- MV ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = "none",
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~1*sat.g.1_1+sat.g.1_2+sat.g.1_3+sat.g.1_4+sat.g.1_5\nSat2=~1*sat.g.2_1+sat.g.2_2+sat.g.2_3+sat.g.2_4+sat.g.2_5\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 ~ 0*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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ NA*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ 0*1"
)
}
)
# ---- FF vs. MV ----
test_that(#failure
"scriptBifac produces same df for constr_dy_meas = none, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script configural invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "none", constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script configural invariance model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "none", constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#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(#pass (but fails in `equal_identical()`)
"scriptBifac produces same chisq for constr_dy_meas = none, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script configural invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "none", constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its df
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script configural invariance model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "none", constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#get its df
chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
#scale setting should have no impact on chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
# Loading ---------------------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ 0*1\nSat2 ~ 0*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac 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 <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 37
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
37
)
}
)
test_that(
"scriptBifac 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 loading invariance model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 37
#calculated df (should be 28)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
28
)
}
)
# ---- MV ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 ~ 0*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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ NA*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ 0*1"
)
}
)
# ---- FF vs. MV ----
test_that(
"scriptBifac produces same df for constr_dy_meas = loadings, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script loading invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "loadings", constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script loading invariance and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "loadings", constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#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(
"scriptBifac produces same chisq for constr_dy_meas = loadings, constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script model w/ constr_dy_meas = "loadings" and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "loadings", constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script model w/ constr_dy_meas = "loadings" and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = "loadings", constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
#scale setting should have no impact on chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
# Intercept --------------------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac 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 intercept invariance model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 34
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
34
)
}
)
test_that(
"scriptBifac 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 intercept invariance model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 37
#calculated df (should be 31)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
31
)
}
)
# ---- MV ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ NA*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ NA*1"
)
}
)
# ---- FF vs. MV ----
test_that(#failure
"scriptBifac produces same df for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script model w/ constr_dy_meas = c("loadings", "intercepts") and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script model w/ constr_dy_meas = c("loadings", "intercepts") and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#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(#pass (but fails in `equal_identical()`)
"scriptBifac produces same chisq for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script intercept invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script intercept invariance model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
#scale setting should have no impact on chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
# Residual ----------------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"),
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac 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 residual invariance model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"),
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 29
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
29
)
}
)
test_that(
"scriptBifac 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 residual invariance model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"),
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 29
#calculated df (should be 36)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
36
)
}
)
# ---- MV ----
test_that(
"scriptBifac 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"),
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ NA*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ NA*1"
)
}
)
# ---- FF vs. MV ----
test_that(#failure
"scriptBifac produces same df for constr_dy_meas = c(loadings, intercepts, residuals), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script residual invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script residual invariance and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#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(#pass (but fails in `equal_identical()`)
"scriptBifac produces same chisq for constr_dy_meas = c(loadings, intercepts, residuals), constr_dy_struct = none when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "com", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script residual invariance model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none", scaleset = "FF")
#Fit model w FF
mod_ff <- lavaan::cfa(
script_ff,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script residual invariance model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Com", constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none", scaleset = "MV")
#Fit model w MV
mod_mv <- lavaan::cfa(
script_mv,
data = commitmentQ,
missing = "fiml"
)
#get its chisq
chisq_mv <- as.double(lavaan::fitmeasures(mod_mv, "chisq"))
#scale setting should have no impact on chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
# Loadings: Source --------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac produces correct output for constr_dy_meas = loadings_source, 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings_source",
constr_dy_struct = "none"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx6*sat.g.2_1+lx7*sat.g.2_2+lx8*sat.g.2_3+lx9*sat.g.2_4+lx10*sat.g.2_5\nSatDy=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5+lx6*sat.g.2_1+lx7*sat.g.2_2+lx8*sat.g.2_3+lx9*sat.g.2_4+lx10*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 (Orthogonal Structure)\nSat1 ~~ 1*Sat1\nSat2 ~~ 1*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ 0*1\nSat2 ~ 0*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac produces correct number of parameter estimates for consrt_dy_meas = loadings_source, 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 source model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings_source",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 35
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
35
)
}
)
test_that(
"scriptBifac produces correct df for constr_dy_meas = loadings_source, 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 source model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings_source",
constr_dy_struct = "none"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 35
#calculated df (should be 30)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
30
)
}
)
# ---- MV ----
# ---- FF vs. MV ----
# Loadings: Mutuality --------------------------------------------------------
# ---- FF ----
# ---- MV ----
# ---- FF vs. MV ----
# Latent Variances --------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac produces correct output for constr_dy_meas = loadings, constr_dy_struct = variances 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "variances"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ psix*Sat1\nSat2 ~~ psix*Sat2\nSatDy ~~ NA*SatDy + psix*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ 0*1\nSat2 ~ 0*1\nSatDy ~ 0*1"
)
}
)
test_that(
"scriptBifac produces correct number of parameter estimates for constr_dy_meas = loadings, constr_dy_struct = variances 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 latent variance equality model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "variances"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 36
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
36
)
}
)
test_that(
"scriptBifac produces correct df for constr_dy_meas = loadings, constr_dy_struct = variances 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 latent variance equality model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "variances"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 36
#calculated df (should be 29)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
29
)
}
)
# ---- MV ----
test_that(
"scriptBifac produces correct output for constr_dy_meas = loadings, constr_dy_struct = variances, 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = "loadings",
constr_dy_struct = "variances"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 ~ 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 ~ 0*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 (Orthogonal Structure)\nSat1 ~~ psix*Sat1\nSat2 ~~ psix*Sat2\nSatDy ~~ psix*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ NA*1\nSat2 ~ NA*1\nSatDy ~ 0*1"
)
}
)
# ---- FF vs. MV ----
test_that(#failure
"scriptBifac produces same df for constr_dy_meas = loadings, constr_dy_struct = variances when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script lvars equality model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = "loadings", constr_dy_struct = "variances", scaleset = "FF")
#Fit model w FF
mod_ff <- suppressWarnings(lavaan::cfa(script_ff, data = commitmentQ))
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script lvars equality model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = "loadings", constr_dy_struct = "variances", scaleset = "MV")
#Fit model w MV
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(#failure
"scriptBifac produces same chisq for constr_dy_meas = loadings, constr_dy_struct = variances when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script lvars equality model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = "loadings", constr_dy_struct = "variances", scaleset = "FF")
#Fit model w FF
mod_ff <- suppressWarnings(lavaan::cfa(script_ff, data = commitmentQ))
#get its chisq
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script lvars equality model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = "loadings", constr_dy_struct = "variances", scaleset = "MV")
#Fit model w MV
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 chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
# Latent Means --------------------------------------------------------
# ---- FF ----
test_that(
"scriptBifac produces correct output for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means 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(
scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "means"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ 1*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ alphax*1\nSat2 ~ alphax*1\nSatDy ~ 0*1 + alphax*1"
)
}
)
test_that(#failure
"scriptBifac produces correct number of parameter estimates for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means 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 latent means equality model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "means"
)
#fit model
mod <- lavaan::cfa(script, data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 32
lav_param <- as.double(
lavaan::fitmeasures(
mod,
"npar")
)
expect_equal(
lav_param,
32
)
}
)
test_that(#failure
"scriptBifac produces correct df for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means 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 latent means equality model w/ scaleset = FF
script <- scriptBifac(
dvn,
scaleset = "FF",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "means"
)
#fit model
mod <- lavaan::cfa(
script,
data = commitmentQ,
missing = "fiml" #for convergence
)
#manually calculated free parameters (see LINK)
#my_param <- 32
#calculated df (should be 33)
#my_df <- knowns-my_param
lav_df <- as.double(
lavaan::fitmeasures(
mod,
"df")
)
expect_equal(
lav_df,
33
)
}
)
# ---- MV ----
test_that("scriptBifac produces correct output for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means, 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(
scriptBifac(
dvn,
scaleset = "MV",
lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"),
constr_dy_struct = "means"
),
"#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\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 (Orthogonal Structure)\nSat1 ~~ NA*Sat1\nSat2 ~~ NA*Sat2\nSatDy ~~ NA*SatDy\n\nSatDy ~~ 0*Sat1\nSatDy ~~ 0*Sat2\nSat1 ~~ 0*Sat2\n\n#Latent Means\nSat1 ~ alphax*1\nSat2 ~ alphax*1\nSatDy ~ alphax*1"
)
}
)
# ---- FF vs. MV ----
test_that(#failure
"scriptBifac produces same df for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script lmeans equality model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "means", scaleset = "FF")
#Fit model w FF
mod_ff <- suppressWarnings(lavaan::cfa(script_ff, data = commitmentQ))
#get its df
df_ff <- as.double(lavaan::fitmeasures(mod_ff, "df"))
#Script lmeans equality model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "means", scaleset = "MV")
#Fit model w MV
mod_mv <- suppressWarnings(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(#pass (but fails in `equal_identical()`)
"scriptBifac produces same chisq for constr_dy_meas = c(loadings, intercepts), constr_dy_struct = means when scaleset= FF, and scaleset = MV", {
dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
x_delim2="_", distinguish_1="1", distinguish_2="2")
#Script lmeans equality model and scaleset = FF
script_ff <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "means", scaleset = "FF")
#Fit model w FF
mod_ff <- suppressWarnings(lavaan::cfa(script_ff, data = commitmentQ))
#get its chisq
chisq_ff <- as.double(lavaan::fitmeasures(mod_ff, "chisq"))
#Script lmeans equality model and scaleset = MV
script_mv <- scriptBifac(dvn, lvname = "Sat", constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "means", scaleset = "MV")
#Fit model w MV
mod_mv <- suppressWarnings(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 chisq
expect_equal(chisq_ff,
chisq_mv
)
}
)
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