Nothing
test_that("Single-group mean-based SSM results are correct", {
skip_on_cran()
data("aw2009")
set.seed(12345)
res <- ssm_analyze(aw2009, PA:NO, octants())
# Test the output object
expect_type(res, "list")
expect_s3_class(res, "circumplex_ssm")
# Test the results subobject
expect_equal(round(res$results$e_est, 3), 0.423)
expect_equal(round(res$results$x_est, 3), 0.945)
expect_equal(round(res$results$y_est, 3), -0.264)
expect_equal(round(res$results$a_est, 3), 0.981)
expect_equal(round(res$results$d_est, 1), as_degree(344.4))
expect_equal(round(res$results$fit_est, 3), 0.954)
expect_equal(res$results$label, "All")
expect_equal(round(res$results$e_lci, 3), 0.129)
expect_equal(round(res$results$e_uci, 3), 0.708)
expect_equal(round(res$results$x_lci, 3), 0.654)
expect_equal(round(res$results$x_uci, 3), 1.251)
expect_equal(round(res$results$y_lci, 3), -0.946)
expect_equal(round(res$results$y_uci, 3), 0.300)
expect_equal(round(res$results$a_lci, 3), 0.662)
expect_equal(round(res$results$a_uci, 3), 1.403)
expect_equal(round(res$results$d_lci, 3), as_degree(316.480))
expect_equal(round(res$results$d_uci, 3), as_degree(17.191))
# Test the scores subobject
expect_equal(round(res$scores$PA, 3), 0.374)
expect_equal(round(res$scores$BC, 3), -0.572)
expect_equal(round(res$scores$DE, 3), -0.520)
expect_equal(round(res$scores$FG, 3), 0.016)
expect_equal(round(res$scores$HI, 3), 0.688)
expect_equal(round(res$scores$JK, 3), 1.142)
expect_equal(round(res$scores$LM, 3), 1.578)
expect_equal(round(res$scores$NO, 3), 0.678)
expect_equal(res$scores$label, "All")
# Test the details subobject
expect_equal(res$details$boots, 2000)
expect_equal(res$details$interval, 0.95)
expect_true(res$details$listwise)
expect_equal(res$details$angles, octants(), ignore_attr = TRUE)
expect_equal(res$details$score_type, "Mean")
expect_equal(res$details$results_type, "Profile")
})
test_that("Multiple-group mean-based SSM results are correct", {
skip_on_cran()
data("jz2017")
set.seed(12345)
res <- ssm_analyze(jz2017, PA:NO, octants(), grouping = Gender)
# Test the output object
expect_type(res, "list")
expect_s3_class(res, "circumplex_ssm")
# Test the results subobject
expect_equal(round(res$results$e_est, 3), c(0.946, 0.884))
expect_equal(round(res$results$x_est, 3), c(0.459, 0.227))
expect_equal(round(res$results$y_est, 3), c(-0.310, -0.186))
expect_equal(round(res$results$a_est, 3), c(0.554, 0.294))
expect_equal(round(res$results$d_est, 3), as_degree(c(325.963, 320.685)))
expect_equal(round(res$results$fit_est, 3), c(0.889, 0.824))
expect_equal(res$results$label, c("Female", "Male"))
expect_equal(round(res$results$e_lci, 3), c(0.907, 0.839))
expect_equal(round(res$results$e_uci, 3), c(0.984, 0.928))
expect_equal(round(res$results$x_lci, 3), c(0.422, 0.191))
expect_equal(round(res$results$x_uci, 3), c(0.498, 0.262))
expect_equal(round(res$results$y_lci, 3), c(-0.357, -0.225))
expect_equal(round(res$results$y_uci, 3), c(-0.266, -0.147))
expect_equal(round(res$results$a_lci, 3), c(0.511, 0.256))
expect_equal(round(res$results$a_uci, 3), c(0.600, 0.330))
expect_equal(round(res$results$d_lci, 3), as_degree(c(321.834, 313.386)))
expect_equal(round(res$results$d_uci, 3), as_degree(c(329.805, 327.985)))
# Test the scores subobject
expect_equal(round(res$scores$PA, 3), c(0.519, 0.585))
expect_equal(round(res$scores$BC, 3), c(0.504, 0.674))
expect_equal(round(res$scores$DE, 3), c(0.589, 0.664))
expect_equal(round(res$scores$FG, 3), c(0.685, 0.856))
expect_equal(round(res$scores$HI, 3), c(1.330, 1.075))
expect_equal(round(res$scores$JK, 3), c(1.361, 1.047))
expect_equal(round(res$scores$LM, 3), c(1.645, 1.300))
expect_equal(round(res$scores$NO, 3), c(0.933, 0.868))
expect_equal(res$scores$label, c("Female", "Male"))
# Test the details subobject
expect_equal(res$details$boots, 2000)
expect_equal(res$details$interval, 0.95)
expect_true(res$details$listwise)
expect_equal(res$details$angles, octants(), ignore_attr = TRUE)
expect_equal(res$details$contrast, "none")
expect_equal(res$details$score_type, "Mean")
expect_equal(res$details$results_type, "Profile")
})
test_that("Multiple-group mean-based SSM contrast is correct", {
skip_on_cran()
data("jz2017")
set.seed(12345)
res <- ssm_analyze(jz2017, PA:NO, octants(),
grouping = Gender,
contrast = "model"
)
# Test the output object
expect_type(res, "list")
expect_s3_class(res, "circumplex_ssm")
# Test the results subobject
expect_equal(round(res$results$e_est, 3), -0.062)
expect_equal(round(res$results$x_est, 3), -0.232)
expect_equal(round(res$results$y_est, 3), 0.124)
expect_equal(round(res$results$a_est, 3), 0.263)
expect_equal(round(res$results$d_est, 3), as_degree(151.858))
expect_equal(round(res$results$fit_est, 3), 0.855)
expect_equal(res$results$label, "Male - Female")
expect_equal(round(res$results$e_lci, 3), -0.122)
expect_equal(round(res$results$e_uci, 3), -0.002)
expect_equal(round(res$results$x_lci, 3), -0.285)
expect_equal(round(res$results$x_uci, 3), -0.180)
expect_equal(round(res$results$y_lci, 3), 0.067)
expect_equal(round(res$results$y_uci, 3), 0.183)
expect_equal(round(res$results$a_lci, 3), 0.210)
expect_equal(round(res$results$a_uci, 3), 0.322)
expect_equal(round(res$results$d_lci, 3), as_degree(140.130))
expect_equal(round(res$results$d_uci, 3), as_degree(164.067))
# Test the details subobject
expect_equal(res$details$boots, 2000)
expect_equal(res$details$interval, 0.95)
expect_true(res$details$listwise)
expect_equal(res$details$angles, octants(), ignore_attr = TRUE)
expect_equal(res$details$contrast, "model")
expect_equal(res$details$score_type, "Mean")
expect_equal(res$details$results_type, "Contrast")
})
test_that("Providing one group throws error", {
data("aw2009")
expect_error(
ssm_analyze(aw2009, PA:NO, octants(), contrast = "model"),
"Without specifying measures or grouping, *"
)
data("jz2017")
expect_error(ssm_analyze(jz2017, PA:NO, octants(),
measures = PARPD,
contrast = "test"
), "No valid contrasts were possible*")
})
test_that("Providing more than two groups throws error", {
data("jz2017")
expect_error(ssm_analyze(jz2017, PA:NO, octants(),
grouping = PARPD,
contrast = "test"
), "Only two groups can be contrasted at a time.*")
})
test_that("Single-group correlation-based SSM results are correct", {
skip_on_cran()
data("jz2017")
set.seed(12345)
res <- ssm_analyze(jz2017, PA:NO, octants(), measures = PARPD)
# Test the output object
expect_type(res, "list")
expect_s3_class(res, "circumplex_ssm")
# Test the results subobject
expect_equal(round(res$results$e_est, 3), 0.250)
expect_equal(round(res$results$x_est, 3), -0.094)
expect_equal(round(res$results$y_est, 3), 0.117)
expect_equal(round(res$results$a_est, 3), 0.150)
expect_equal(round(res$results$d_est, 1), as_degree(128.9))
expect_equal(round(res$results$fit_est, 3), 0.802)
expect_equal(res$scores$Group, factor("All"))
expect_equal(res$scores$Measure, "PARPD")
expect_equal(res$scores$label, "PARPD")
expect_equal(round(res$results$e_lci, 3), 0.218)
expect_equal(round(res$results$e_uci, 3), 0.282)
expect_equal(round(res$results$x_lci, 3), -0.128)
expect_equal(round(res$results$x_uci, 3), -0.062)
expect_equal(round(res$results$y_lci, 3), 0.081)
expect_equal(round(res$results$y_uci, 3), 0.153)
expect_equal(round(res$results$a_lci, 3), 0.113)
expect_equal(round(res$results$a_uci, 3), 0.189)
expect_equal(round(res$results$d_lci, 3), as_degree(117.261))
expect_equal(round(res$results$d_uci, 3), as_degree(141.596))
# Test the scores subobject
expect_equal(round(res$scores$PA, 3), 0.329)
expect_equal(round(res$scores$BC, 3), 0.494)
expect_equal(round(res$scores$DE, 3), 0.329)
expect_equal(round(res$scores$FG, 3), 0.203)
expect_equal(round(res$scores$HI, 3), 0.102)
expect_equal(round(res$scores$JK, 3), 0.143)
expect_equal(round(res$scores$LM, 3), 0.207)
expect_equal(round(res$scores$NO, 3), 0.193)
expect_equal(res$scores$Group, factor("All"))
expect_equal(res$scores$Measure, "PARPD")
expect_equal(res$scores$label, "PARPD")
# Test the details subobject
expect_equal(res$details$boots, 2000)
expect_equal(res$details$interval, 0.95)
expect_true(res$details$listwise)
expect_equal(res$details$angles, octants())
expect_match(res$details$score_type, "Correlation")
expect_match(res$details$results_type, "Profile")
})
test_that("Pairwise and listwise scores are the same with no missingness", {
skip_on_cran()
# Single-group mean
data("jz2017")
res_lw <- ssm_analyze(jz2017, PA:NO, octants(), listwise = TRUE)
res_pw <- ssm_analyze(jz2017, PA:NO, octants(), listwise = FALSE)
expect_equal(res_lw$scores, res_pw$scores)
# Single-group correlation
res_lw <- ssm_analyze(jz2017, PA:NO, octants(),
measures = PARPD,
listwise = TRUE
)
res_pw <- ssm_analyze(jz2017, PA:NO, octants(),
measures = PARPD,
listwise = FALSE
)
expect_equal(res_lw$scores, res_pw$scores)
# Multiple-group mean
res_lw <- ssm_analyze(jz2017, PA:NO, octants(),
grouping = Gender,
listwise = TRUE
)
res_pw <- ssm_analyze(jz2017, PA:NO, octants(),
grouping = Gender,
listwise = FALSE
)
expect_equal(res_lw$scores, res_pw$scores)
# Multiple-group correlation
res_lw <- ssm_analyze(jz2017, PA:NO, octants(),
measures = PARPD,
grouping = Gender, listwise = TRUE
)
res_pw <- ssm_analyze(jz2017, PA:NO, octants(),
measures = PARPD,
grouping = Gender, listwise = FALSE
)
expect_equal(res_lw$scores, res_pw$scores)
})
test_that("Measure-contrast correlation-based SSM results are correct", {
skip_on_cran()
data("jz2017")
set.seed(12345)
res <- ssm_analyze(jz2017, PA:NO, octants(),
measures = c(ASPD, NARPD),
contrast = "test"
)
# Test the output object
expect_type(res, "list")
expect_s3_class(res, "circumplex_ssm")
# Test the results subobject
expect_equal(round(res$results$e_est, 3), 0.079)
expect_equal(round(res$results$x_est, 3), 0.037)
expect_equal(round(res$results$y_est, 3), -0.024)
expect_equal(round(res$results$a_est, 3), -0.037)
expect_equal(round(res$results$d_est, 1), as_degree(-7.0))
expect_equal(round(res$results$fit_est, 3), -0.007)
expect_equal(res$results$label, "NARPD - ASPD")
expect_equal(round(res$results$e_lci, 3), 0.042)
expect_equal(round(res$results$e_uci, 3), 0.117)
expect_equal(round(res$results$x_lci, 3), -0.001)
expect_equal(round(res$results$x_uci, 3), 0.075)
expect_equal(round(res$results$y_lci, 3), -0.063)
expect_equal(round(res$results$y_uci, 3), 0.014)
expect_equal(round(res$results$a_lci, 3), -0.077)
expect_equal(round(res$results$a_uci, 3), 0.003)
expect_equal(round(res$results$d_lci, 3), as_degree(-17.384))
expect_equal(round(res$results$d_uci, 3), as_degree(3.245))
# Test the scores subobject
expect_equal(round(res$scores$PA, 3), c(0.368, 0.400))
expect_equal(round(res$scores$BC, 3), c(0.354, 0.385))
expect_equal(round(res$scores$DE, 3), c(0.187, 0.234))
expect_equal(round(res$scores$FG, 3), c(0.045, 0.108))
expect_equal(round(res$scores$HI, 3), c(-0.073, 0.051))
expect_equal(round(res$scores$JK, 3), c(-0.045, 0.058))
expect_equal(round(res$scores$LM, 3), c(-0.018, 0.084))
expect_equal(round(res$scores$NO, 3), c(0.173, 0.300))
expect_equal(res$scores$Group, factor(c("All", "All")))
expect_equal(res$scores$Measure, c("ASPD", "NARPD"))
expect_equal(res$scores$label, c("ASPD", "NARPD"))
# Test the details subobject
expect_equal(res$details$boots, 2000)
expect_equal(res$details$interval, 0.95)
expect_true(res$details$listwise)
expect_equal(res$details$angles, octants())
expect_equal(res$details$contrast, "test")
expect_equal(res$details$score_type, "Correlation")
expect_equal(res$details$results_type, "Contrast")
})
test_that("Group-contrast correlation-based SSM results are correct", {
skip_on_cran()
data("jz2017")
set.seed(12345)
res <- ssm_analyze(jz2017, PA:NO, octants(),
measures = NARPD,
grouping = Gender, contrast = "test"
)
# Test the output object
expect_type(res, "list")
expect_s3_class(res, "circumplex_ssm")
# Test the results subobject
expect_equal(round(res$results$e_est, 3), 0.072)
expect_equal(round(res$results$x_est, 3), 0.051)
expect_equal(round(res$results$y_est, 3), -0.056)
expect_equal(round(res$results$a_est, 3), -0.068)
expect_equal(round(res$results$d_est, 1), as_degree(-10.4))
expect_equal(round(res$results$fit_est, 3), -0.071)
expect_equal(res$results$label, "NARPD: Male - Female")
expect_equal(round(res$results$e_lci, 3), 0.005)
expect_equal(round(res$results$e_uci, 3), 0.142)
expect_equal(round(res$results$x_lci, 3), -0.015)
expect_equal(round(res$results$x_uci, 3), 0.111)
expect_equal(round(res$results$y_lci, 3), -0.120)
expect_equal(round(res$results$y_uci, 3), 0.006)
expect_equal(round(res$results$a_lci, 3), -0.133)
expect_equal(round(res$results$a_uci, 3), -0.003)
expect_equal(round(res$results$d_lci, 3), as_degree(-30.168))
expect_equal(round(res$results$d_uci, 3), as_degree(12.302))
# Test the scores subobject
expect_equal(round(res$scores$PA, 3), c(0.385, 0.415))
expect_equal(round(res$scores$BC, 3), c(0.377, 0.397))
expect_equal(round(res$scores$DE, 3), c(0.227, 0.240))
expect_equal(round(res$scores$FG, 3), c(0.083, 0.129))
expect_equal(round(res$scores$HI, 3), c(-0.010, 0.138))
expect_equal(round(res$scores$JK, 3), c(-0.007, 0.155))
expect_equal(round(res$scores$LM, 3), c(0.036, 0.158))
expect_equal(round(res$scores$NO, 3), c(0.283, 0.322))
expect_equal(res$scores$Group, factor(c("Female", "Male")))
expect_equal(res$scores$Measure, c("NARPD", "NARPD"))
expect_equal(res$scores$label, c("Female_NARPD", "Male_NARPD"))
# Test the details subobject
expect_equal(res$details$boots, 2000)
expect_equal(res$details$interval, 0.95)
expect_true(res$details$listwise)
expect_equal(res$details$angles, octants())
expect_equal(res$details$contrast, "test")
expect_equal(res$details$score_type, "Correlation")
expect_equal(res$details$results_type, "Contrast")
})
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