tests/testthat/testthat.R
In wjschne/simstandard: Generate Standardized Data
library(testthat)
library(simstandard)
test_that("path greater than 1",
code = {
m <- "A ~~ -0.99 * B
C ~ 1.01 * A + 0.1 * B"
expect_warning(
sim_standardized_matrices(m))
})
test_that("correlation model",
code = {
m <- "x ~~ 0.5 * y"
expect_equal(sim_standardized_matrices(m)$Correlations$R[1, 2], 0.5)
})
test_that("composite threshold",
code = {
m <- "A =~ 0.8 * A1 + 0.8 * A2 + 0.2 * A3"
expect_silent(
sim_standardized_matrices(m, composite_threshold = 0.3))
})
test_that("no formative variables", {
m <- "A <~ 0.8 * A1 + 0.8 * A2 + 0.8 * A3"
expect_error(
sim_standardized_matrices(m),
paste0("Formative variables \\(defined with <~\\) ",
"are not allowed for this function.")
)
})
test_that("user-set variances",
code = {
m <- "A =~ 0.8 * A1 + 0.8 * A2 + 0.8 * A3
A ~~ 2 * A"
expect_error(
sim_standardized_matrices(m),
paste0("All variances are set automatically to create ",
"standardized data. You may not set variances manually. ",
"Remove the following parameter:\nA ~~ 2 \\* A"
))
})
test_that(
"unset paths",
code = {
m <- "A =~ A1 + 0.8 * A2 + 0.8 * A3"
expect_warning(
sim_standardized_matrices(m),
"Because the following relationship was not set, it is assumed to be 0:
A =~ A1")
})
test_that("v_error_y",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B ~ 0.5 * A
"
expect_silent(sim_standardized_matrices(m))
})
test_that("v_latent_endogenous",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B =~ 0.5 * B1 + 0.8 * B2 + 0.8 * B3
B ~ 0.5 * A
"
expect_silent(sim_standardized_matrices(m))
})
test_that("simstandardized",
code = {
m <- "
A ~ 0.5 * B
"
expect_silent(sim_standardized(m))
})
test_that("model_complete",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B =~ 0.5 * B1 + 0.8 * B2 + 0.8 * B3
B ~ 0.5 * A
"
expect_silent(model_complete(m))
})
test_that("add_factor_scores and add_composite_scores",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
"
d <- data.frame(A1 = 1, A2 = 2, A3 = 0)
expect_silent(add_factor_scores(d, m, CI = T))
expect_silent(add_composite_scores(d, m))
})
test_that("matrix2lavaan",
code = {
m <- data.frame(X = c(0.7, 0.8, 0, 0),
Y = c(0, 0, 0.8, 0.9))
rownames(m) <- c("A", "B", "C", "D")
s <- matrix(0.5,
nrow = 1,
ncol = 1,
dimnames = list("Y", "X"))
sigma <- matrix(
c(1, 0.3,
0.3, 1),
nrow = 2,
ncol = 2,
dimnames = list(c("B", "C"),
c("B", "C"))
)
expect_silent(matrix2lavaan(
measurement_model = m,
structural_model = s,
covariances = sigma
))
})
test_that("lav2ram", code = {
m <- "
A =~ 0.7 * A1 + 0.8 * A2 + 0.9 * A3 + 0.3 * B1
B =~ 0.7 * B1 + 0.8 * B2 + 0.9 * B3
B ~ 0.6 * A"
# Make model m free
m_free <- fixed2free(m)
# Generate data based on model m
d <- sim_standardized(m,
n = 100000,
latent = FALSE,
errors = FALSE)
# Evaluate the fit of model m_free on data d
library(lavaan)
lav_results <- sem(model = m_free,
data = d)
expect_silent(lav2ram(lav_results))
})
test_that("add_factor_scores and add_composite_scores",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
"
d <- data.frame(A1 = 1, A2 = 2, A3 = 0)
expect_silent(add_composite_scores(d, m))
})
test_that("check_matrix2lavaan",
code = {
m <- data.frame(X = c(0.7, 0.8, 0, 0),
Y = c(0, 0, 0.8, 0.9))
s <- matrix(0.5,
nrow = 1,
ncol = 1,
dimnames = list("Y", "X"))
sigma <- matrix(
c(1, 0.3,
0.3, 1),
nrow = 2,
ncol = 2,
dimnames = list(c("B", "C"),
c("B", "C"))
)
expect_error(matrix2lavaan(
measurement_model = m,
structural_model = s,
covariances = sigma
))
rownames(m) <- c("A", "B", "C", "D")
m <- as.matrix(m)
m_no_colnames <- m
colnames(m_no_colnames) <- NULL
m_no_rownames <- m
rownames(m_no_rownames) <- NULL
expect_error(
matrix2lavaan(
measurement_model = m_no_colnames,
structural_model = s,
covariances = sigma
)
)
expect_error(
matrix2lavaan(
measurement_model = m_no_rownames,
structural_model = s,
covariances = sigma
)
)
m_as_list <- as.list(m)
expect_error(
matrix2lavaan(
measurement_model = m_as_list,
structural_model = s,
covariances = sigma
)
)
})
test_that("add_factor_scores and add_composite_scores",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
"
expect_silent(sim_standardized(
m,
factor_scores = TRUE,
composites = TRUE,
matrices = TRUE
))
})
test_that("max_iterations",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
"
expect_error(sim_standardized_matrices(m, max_iterations = 0))
})
test_that(
"missing composite data",
code = {
m <- "A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3"
d <- data.frame(A1 = 1, A2 = 2)
expect_error(
add_composite_scores(d, m),
paste0("Some observed variables specified in the model",
" are missing from the data\\.")
)
})
test_that("composite with mu and sigma specified",
code = {
m <- "
A =~ 0.8 * A1 + 0.8 * A2 + 0.8 * A3
"
d_ss <- data.frame(A1 = 115, A2 = 115, A3 = 115) %>%
add_composite_scores(m, mu = 100, sigma = 15) %>%
add_factor_scores(m, mu = 100, sigma = 15)
d_z <- data.frame(A1 = 1, A2 = 1, A3 = 1) %>%
add_composite_scores(m) %>%
add_factor_scores(m)
expect_equal(d_ss$A_Composite, d_z$A_Composite * 15 + 100)
expect_equal(d_ss$A_FS, d_z$A_FS * 15 + 100)
})
test_that("composite and factor scores with names suffixes",
code = {
m <- "
A =~ 0.8 * A1 + 0.8 * A2 + 0.8 * A3
"
d <- data.frame(A1 = 115, A2 = 115, A3 = 115)
v_composite <-
colnames(add_composite_scores(d,
m,
names_suffix = "_cs"))[4]
v_fs <-
colnames(add_factor_scores(d,
m,
names_suffix = "_factor_score"))[4]
expect_equal(v_composite, "A_cs")
expect_equal(v_fs, "A_factor_score")
})
test_that("get model-implied matrix from sim_standardized_matrices output.",
code = {
m <- "
A =~ 0.8 * A1 + 0.8 * A2 + 0.8 * A3
"
fit <- sim_standardized_matrices(m)
expect_equal(get_model_implied_correlations(m),
get_model_implied_correlations(fit))
})
test_that("get model-implied matrix from sim_standardized_matrices output.",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B =~ 0.5 * B1 + 0.8 * B2 + 0.8 * B3
B ~ 0.5 * A
"
fit <- sim_standardized_matrices(m)
get_factor_score_coefficients(m, latent = F, errors = T)
expect_equal(get_model_implied_correlations(m),
get_model_implied_correlations(fit))
})
# Model of Reading
m_reading <- "
Ga =~ 0.83 * Ga1 + 0.92 * Ga2 + 0.95 * Ga3
Gc =~ 0.88 * Gc1 + 0.71 * Gc2 + 0.85 * Gc3
RD =~ 0.93 * RD1 + 0.87 * RD2 + 0.85 * RD3
RC =~ 0.91 * RC1 + 0.86 * RC2 + 0.90 * RC3
Ga ~~ 0.68 * Gc
RD ~ 0.57 * Ga + 0.33 * Gc
RC ~ 0.05 * Ga + 0.40 * Gc + 0.43 * RD
"
test_that("Number of factor scores",
code = {
m_names <- get_model_names(m_reading)
expect_equal(m_names$v_factor_score,
paste0(c("Ga", "Gc", "RD", "RC"), "_FS")
)
})
test_that("parameters greater than 1",
code = {
m <- "x ~~ 1.1 * y"
expect_warning(sim_standardized_matrices(m))
}
)
test_that("not keeping observed scores",
code = {
# lavaan model
m = "
X =~ 0.9 * X1 + 0.8 * X2 + 0.7 * X3
"
# Make data.frame for two cases
d <- data.frame(
X1 = c(1.2, -1.2),
X2 = c(1.5, -1.8),
X3 = c(1.8, -1.1))
# Compute composite scores for two cases
d_composite <- add_composite_scores(d, m, keep_observed_scores = FALSE, names_suffix = "")
expect_equal(colnames(d_composite), "X")
}
)
test_that("non-symmetric covariance",
code = {
m <- data.frame(Indicators = c("A", "B", "C", "D"),
X = c(0.7,0.8,0,0),
Y = c(0,0,0.8,0.9))
s <- matrix(0.5, nrow = 1, ncol = 1, dimnames = list("Y", "X"))
Sigma <- matrix(
c(1, 0.31,
0.3, 1),
nrow = 2,
ncol = 2,
dimnames = list(c("B", "C"),
c("B", "C"))
)
expect_error(matrix2lavaan(
measurement_model = m,
structural_model = s,
covariances = Sigma
), regexp = "covariances must be symmetric.")
}
)
wjschne/simstandard documentation built on April 13, 2024, 4:44 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(testthat)
library(simstandard)
test_that("path greater than 1",
code = {
m <- "A ~~ -0.99 * B
C ~ 1.01 * A + 0.1 * B"
expect_warning(
sim_standardized_matrices(m))
})
test_that("correlation model",
code = {
m <- "x ~~ 0.5 * y"
expect_equal(sim_standardized_matrices(m)$Correlations$R[1, 2], 0.5)
})
test_that("composite threshold",
code = {
m <- "A =~ 0.8 * A1 + 0.8 * A2 + 0.2 * A3"
expect_silent(
sim_standardized_matrices(m, composite_threshold = 0.3))
})
test_that("no formative variables", {
m <- "A <~ 0.8 * A1 + 0.8 * A2 + 0.8 * A3"
expect_error(
sim_standardized_matrices(m),
paste0("Formative variables \\(defined with <~\\) ",
"are not allowed for this function.")
)
})
test_that("user-set variances",
code = {
m <- "A =~ 0.8 * A1 + 0.8 * A2 + 0.8 * A3
A ~~ 2 * A"
expect_error(
sim_standardized_matrices(m),
paste0("All variances are set automatically to create ",
"standardized data. You may not set variances manually. ",
"Remove the following parameter:\nA ~~ 2 \\* A"
))
})
test_that(
"unset paths",
code = {
m <- "A =~ A1 + 0.8 * A2 + 0.8 * A3"
expect_warning(
sim_standardized_matrices(m),
"Because the following relationship was not set, it is assumed to be 0:
A =~ A1")
})
test_that("v_error_y",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B ~ 0.5 * A
"
expect_silent(sim_standardized_matrices(m))
})
test_that("v_latent_endogenous",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B =~ 0.5 * B1 + 0.8 * B2 + 0.8 * B3
B ~ 0.5 * A
"
expect_silent(sim_standardized_matrices(m))
})
test_that("simstandardized",
code = {
m <- "
A ~ 0.5 * B
"
expect_silent(sim_standardized(m))
})
test_that("model_complete",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B =~ 0.5 * B1 + 0.8 * B2 + 0.8 * B3
B ~ 0.5 * A
"
expect_silent(model_complete(m))
})
test_that("add_factor_scores and add_composite_scores",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
"
d <- data.frame(A1 = 1, A2 = 2, A3 = 0)
expect_silent(add_factor_scores(d, m, CI = T))
expect_silent(add_composite_scores(d, m))
})
test_that("matrix2lavaan",
code = {
m <- data.frame(X = c(0.7, 0.8, 0, 0),
Y = c(0, 0, 0.8, 0.9))
rownames(m) <- c("A", "B", "C", "D")
s <- matrix(0.5,
nrow = 1,
ncol = 1,
dimnames = list("Y", "X"))
sigma <- matrix(
c(1, 0.3,
0.3, 1),
nrow = 2,
ncol = 2,
dimnames = list(c("B", "C"),
c("B", "C"))
)
expect_silent(matrix2lavaan(
measurement_model = m,
structural_model = s,
covariances = sigma
))
})
test_that("lav2ram", code = {
m <- "
A =~ 0.7 * A1 + 0.8 * A2 + 0.9 * A3 + 0.3 * B1
B =~ 0.7 * B1 + 0.8 * B2 + 0.9 * B3
B ~ 0.6 * A"
# Make model m free
m_free <- fixed2free(m)
# Generate data based on model m
d <- sim_standardized(m,
n = 100000,
latent = FALSE,
errors = FALSE)
# Evaluate the fit of model m_free on data d
library(lavaan)
lav_results <- sem(model = m_free,
data = d)
expect_silent(lav2ram(lav_results))
})
test_that("add_factor_scores and add_composite_scores",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
"
d <- data.frame(A1 = 1, A2 = 2, A3 = 0)
expect_silent(add_composite_scores(d, m))
})
test_that("check_matrix2lavaan",
code = {
m <- data.frame(X = c(0.7, 0.8, 0, 0),
Y = c(0, 0, 0.8, 0.9))
s <- matrix(0.5,
nrow = 1,
ncol = 1,
dimnames = list("Y", "X"))
sigma <- matrix(
c(1, 0.3,
0.3, 1),
nrow = 2,
ncol = 2,
dimnames = list(c("B", "C"),
c("B", "C"))
)
expect_error(matrix2lavaan(
measurement_model = m,
structural_model = s,
covariances = sigma
))
rownames(m) <- c("A", "B", "C", "D")
m <- as.matrix(m)
m_no_colnames <- m
colnames(m_no_colnames) <- NULL
m_no_rownames <- m
rownames(m_no_rownames) <- NULL
expect_error(
matrix2lavaan(
measurement_model = m_no_colnames,
structural_model = s,
covariances = sigma
)
)
expect_error(
matrix2lavaan(
measurement_model = m_no_rownames,
structural_model = s,
covariances = sigma
)
)
m_as_list <- as.list(m)
expect_error(
matrix2lavaan(
measurement_model = m_as_list,
structural_model = s,
covariances = sigma
)
)
})
test_that("add_factor_scores and add_composite_scores",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
"
expect_silent(sim_standardized(
m,
factor_scores = TRUE,
composites = TRUE,
matrices = TRUE
))
})
test_that("max_iterations",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
"
expect_error(sim_standardized_matrices(m, max_iterations = 0))
})
test_that(
"missing composite data",
code = {
m <- "A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3"
d <- data.frame(A1 = 1, A2 = 2)
expect_error(
add_composite_scores(d, m),
paste0("Some observed variables specified in the model",
" are missing from the data\\.")
)
})
test_that("composite with mu and sigma specified",
code = {
m <- "
A =~ 0.8 * A1 + 0.8 * A2 + 0.8 * A3
"
d_ss <- data.frame(A1 = 115, A2 = 115, A3 = 115) %>%
add_composite_scores(m, mu = 100, sigma = 15) %>%
add_factor_scores(m, mu = 100, sigma = 15)
d_z <- data.frame(A1 = 1, A2 = 1, A3 = 1) %>%
add_composite_scores(m) %>%
add_factor_scores(m)
expect_equal(d_ss$A_Composite, d_z$A_Composite * 15 + 100)
expect_equal(d_ss$A_FS, d_z$A_FS * 15 + 100)
})
test_that("composite and factor scores with names suffixes",
code = {
m <- "
A =~ 0.8 * A1 + 0.8 * A2 + 0.8 * A3
"
d <- data.frame(A1 = 115, A2 = 115, A3 = 115)
v_composite <-
colnames(add_composite_scores(d,
m,
names_suffix = "_cs"))[4]
v_fs <-
colnames(add_factor_scores(d,
m,
names_suffix = "_factor_score"))[4]
expect_equal(v_composite, "A_cs")
expect_equal(v_fs, "A_factor_score")
})
test_that("get model-implied matrix from sim_standardized_matrices output.",
code = {
m <- "
A =~ 0.8 * A1 + 0.8 * A2 + 0.8 * A3
"
fit <- sim_standardized_matrices(m)
expect_equal(get_model_implied_correlations(m),
get_model_implied_correlations(fit))
})
test_that("get model-implied matrix from sim_standardized_matrices output.",
code = {
m <- "
A =~ 0.5 * A1 + 0.8 * A2 + 0.8 * A3
B =~ 0.5 * B1 + 0.8 * B2 + 0.8 * B3
B ~ 0.5 * A
"
fit <- sim_standardized_matrices(m)
get_factor_score_coefficients(m, latent = F, errors = T)
expect_equal(get_model_implied_correlations(m),
get_model_implied_correlations(fit))
})
# Model of Reading
m_reading <- "
Ga =~ 0.83 * Ga1 + 0.92 * Ga2 + 0.95 * Ga3
Gc =~ 0.88 * Gc1 + 0.71 * Gc2 + 0.85 * Gc3
RD =~ 0.93 * RD1 + 0.87 * RD2 + 0.85 * RD3
RC =~ 0.91 * RC1 + 0.86 * RC2 + 0.90 * RC3
Ga ~~ 0.68 * Gc
RD ~ 0.57 * Ga + 0.33 * Gc
RC ~ 0.05 * Ga + 0.40 * Gc + 0.43 * RD
"
test_that("Number of factor scores",
code = {
m_names <- get_model_names(m_reading)
expect_equal(m_names$v_factor_score,
paste0(c("Ga", "Gc", "RD", "RC"), "_FS")
)
})
test_that("parameters greater than 1",
code = {
m <- "x ~~ 1.1 * y"
expect_warning(sim_standardized_matrices(m))
}
)
test_that("not keeping observed scores",
code = {
# lavaan model
m = "
X =~ 0.9 * X1 + 0.8 * X2 + 0.7 * X3
"
# Make data.frame for two cases
d <- data.frame(
X1 = c(1.2, -1.2),
X2 = c(1.5, -1.8),
X3 = c(1.8, -1.1))
# Compute composite scores for two cases
d_composite <- add_composite_scores(d, m, keep_observed_scores = FALSE, names_suffix = "")
expect_equal(colnames(d_composite), "X")
}
)
test_that("non-symmetric covariance",
code = {
m <- data.frame(Indicators = c("A", "B", "C", "D"),
X = c(0.7,0.8,0,0),
Y = c(0,0,0.8,0.9))
s <- matrix(0.5, nrow = 1, ncol = 1, dimnames = list("Y", "X"))
Sigma <- matrix(
c(1, 0.31,
0.3, 1),
nrow = 2,
ncol = 2,
dimnames = list(c("B", "C"),
c("B", "C"))
)
expect_error(matrix2lavaan(
measurement_model = m,
structural_model = s,
covariances = Sigma
), regexp = "covariances must be symmetric.")
}
)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.