tests/testthat/test-contingencytables.R
In jasp-stats/jaspFrequencies: Frequencies Module for JASP
context("Contingency Tables")
# does not test
# - row/column order (ascending/descending)
test_that("Main table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$counts <- "facFifty"
options$layers <- list(list(
name = "Layer 1",
variables = "facGender"
))
options$countsExpected <- TRUE
options$percentagesRow <- TRUE
options$percentagesColumn <- TRUE
options$percentagesTotal <- TRUE
options$residualsUnstandardized <- TRUE
options$residualsPearson <- TRUE
options$residualsStandardized <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabMain"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.489296636085627, 320, 394.529977794227, -3.75224327419637, 0.392638036809816,
-8.29368531337046, 0.236861584011843, -74.5299777942265, 0.710186513629842,
495, 420.470022205773, 3.63465733359156, 0.607361963190184,
8.29368531337046, 0.36639526276832, 74.5299777942265, "control",
"f", 0.603256846780163, 815, 815, 1, 0.603256846780163, " % within column",
"Count", "Expected count", "Pearson residuals", " % within row",
"Standardized residuals", " % of total", "Unstandardized residuals",
0.510703363914373, 334, 259.470022205773, 4.62687106461469,
0.623134328358209, 8.29368531337046, 0.247224278312361, 74.5299777942265,
0.289813486370158, 202, 276.529977794227, -4.48187647166511,
0.376865671641791, -8.29368531337046, 0.149518874907476, -74.5299777942265,
"experimental", "f", 0.396743153219837, 536, 536, 1, 0.396743153219837,
" % within column", "Count", "Expected count", "Pearson residuals",
" % within row", "Standardized residuals", " % of total", "Unstandardized residuals",
1, 654, 654, 0.484085862324204, 0.484085862324204, 1, 697, 697,
0.515914137675796, 0.515914137675796, "Total", "f", 1, 1351,
1351, 1, 1, " % within column", "Count", "Expected count", " % within row",
" % of total", 0.338688085676037, 253, 271.013344453711, -1.09420580859867,
0.581609195402299, -2.23255569694362, 0.211009174311927, -18.0133444537115,
0.402654867256637, 182, 163.986655546289, 1.40666311404823,
0.418390804597701, 2.23255569694361, 0.151793160967473, 18.0133444537114,
"control", "m", 0.362802335279399, 435, 435, 1, 0.3628023352794,
" % within column", "Count", "Expected count", "Pearson residuals",
" % within row", "Standardized residuals", " % of total", "Unstandardized residuals",
0.661311914323963, 494, 475.986655546289, 0.82565186283563,
0.646596858638743, 2.23255569694362, 0.412010008340284, 18.0133444537115,
0.597345132743363, 270, 288.013344453711, -1.06142191109686,
0.353403141361257, -2.23255569694362, 0.225187656380317, -18.0133444537115,
"experimental", "m", 0.6371976647206, 764, 764, 1, 0.6371976647206,
" % within column", "Count", "Expected count", "Pearson residuals",
" % within row", "Standardized residuals", " % of total", "Unstandardized residuals",
1, 747, 747, 0.62301918265221, 0.62301918265221, 1, 452, 452,
0.37698081734779, 0.37698081734779, "Total", "m", 1, 1199, 1199,
1, 1, " % within column", "Count", "Expected count", " % within row",
" % of total", 0.408993576017131, 573, 686.764705882353, -4.34113772648338,
0.4584, -9.05757740964558, 0.224705882352941, -113.764705882353,
0.589208006962576, 677, 563.235294117647, 4.79360911772348,
0.5416, 9.05757740964558, 0.265490196078431, 113.764705882353,
"control", "Total", 0.490196078431373, 1250, 1250, 1, 0.490196078431373,
" % within column", "Count", "Expected count", "Pearson residuals",
" % within row", "Standardized residuals", " % of total", "Unstandardized residuals",
0.591006423982869, 828, 714.235294117647, 4.25683576510241,
0.636923076923077, 9.05757740964558, 0.324705882352941, 113.764705882353,
0.410791993037424, 472, 585.764705882353, -4.70052046765545,
0.363076923076923, -9.05757740964558, 0.185098039215686, -113.764705882353,
"experimental", "Total", 0.509803921568627, 1300, 1300, 1, 0.509803921568627,
" % within column", "Count", "Expected count", "Pearson residuals",
" % within row", "Standardized residuals", " % of total", "Unstandardized residuals",
1, 1401, 1401, 0.549411764705882, 0.549411764705882, 1, 1149,
1149, 0.450588235294118, 0.450588235294118, "Total", "Total",
1, 2550, 2550, 1, 1, " % within column", "Count", "Expected count",
" % within row", " % of total"))
})
test_that("Multiple row and column variables give multiple main tables", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- c("facExperim", "facGender")
options$columns <- c("contBinom", "facFive")
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table1 <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabMain"]][["data"]]
table2 <- results[["results"]][["container_facExperim_facFive"]][["collection"]][["container_facExperim_facFive_crossTabMain"]][["data"]]
table3 <- results[["results"]][["container_facGender_contBinom"]][["collection"]][["container_facGender_contBinom_crossTabMain"]][["data"]]
table4 <- results[["results"]][["container_facGender_facFive"]][["collection"]][["container_facGender_facFive_crossTabMain"]][["data"]]
expect_is(table1, "list", label = "facExperim-contBinom table")
expect_is(table2, "list", label = "facExperim-Facfive table")
expect_is(table3, "list", label = "facGender-contBinom table")
expect_is(table4, "list", label = "facGender-facFive table")
})
test_that("Chi-Squared test table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$counts <- "facFifty"
options$chiSquared <- TRUE
options$chiSquaredContinuityCorrection <- TRUE
options$likelihoodRatio <- TRUE
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabChisq"]][["data"]]
jaspTools::expect_equal_tables(table,
list("", 44468347240355080, 63462127883801120, "<unicode>", "", 1,
1, 1, "", 1.91958529099645e-19, 1.33379878452991e-19, 0, "N",
"<unicode><unicode> continuity correction", "<unicode><unicode>",
"Likelihood ratio", 2550, 81.3201582621313, 82.0397085317219,
82.4643894680383))
})
test_that("Nominal table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$contingencyCoefficient <- TRUE
options$phiAndCramersV <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabNominal"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contingency coefficient", 0.0807792391722019, "Phi-coefficient",
-0.0810440898473108, "Cramer's V ", 0.0810440898473108)
)
})
test_that("Log Odds Ratio table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$oddsRatio <- TRUE
options$oddsRatioCiLevel <- 0.90
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabLogOdds"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Odds ratio", -0.329205575243527, -0.998167649205055, 0.339756498718001,"",
"Fisher's exact test ", -0.325882968750928, -1.07370478788709,
0.415368461868818, 0.5435617)
)
})
test_that("Ordinal Gamma table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$gamma <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabGamma"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.163132137030995, 0.197938461395245, -0.551084392520947, 0.224820118458957)
)
})
test_that("Kendall's Tau table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$kendallsTauB <- TRUE
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabKendallTau"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.0810440898473108, 0.420024632711394, 1, -0.806378512498144)
)
})
test_that("Goodman-Kruskal lambda and Cramer's V results match", {
# test based on example data from p. 1130
# Kvålseth, T. O. (2018) Measuring association between nominal categorical variables: an alternative to the Goodman–Kruskal lambda, Journal of Applied Statistics, 45:6,1118-1132, DOI: 10.1080/02664763.2017.1346066
comb <- expand.grid(var1=letters[1:3], var2=letters[1:2], KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE)
counts <- c(40, 15, 5, 5, 25, 10)
df <- data.frame(var1 = rep(comb$var1, counts), var2 = rep(comb$var2, counts))
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "var1"
options$columns <- "var2"
options$lambda <- TRUE
options$phiAndCramersV <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", df, options)
table <- results[["results"]][["container_var1_var2"]][["collection"]][["container_var1_var2_crossTabNominal"]][["data"]][[1]]
# reported 0.3636
testthat::expect_equal(table[["value[LambdaC]"]], 0.363636363636364)
# reported 0.37
testthat::expect_equal(table[["value[LambdaS]"]], 0.369318181818182)
# reported 0.53
testthat::expect_equal(table[["value[CramerV]"]], 0.534135681195261)
})
test_that("Analysis handles errors", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$counts <- "contNormal"
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
errorMsg <- results[["results"]][["errorMessage"]]
expect_is(errorMsg, "character")
})
options <- analysisOptions("ContingencyTables")
options$columns <- "V2"
options$contingencyCoefficient <- TRUE
options$phiAndCramersV <- TRUE
options$rows <- "V1"
set.seed(1)
# data is a random sample from https://github.com/jasp-stats/jasp-issues/issues/811
dataset <- structure(list(V1 = c(1L, 2L, 1L, 2L, 1L, 0L, 2L, 0L, 0L, 1L), V2 = c(0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L)), row.names = c(NA, -10L), class = "data.frame")
results <- jaspTools::runAnalysis("ContingencyTables", dataset, options)
test_that("Phi coefficient is only available for 2 by 2 contingency tables", {
tb <- results[["results"]][["container_V1_V2"]][["collection"]][["container_V1_V2_crossTabNominal"]]
table <- tb[["data"]]
jaspTools::expect_equal_tables(
test = table,
ref = list("Contingency coefficient", "Cramer's V ", "Phi-coefficient", 0.638284738504225, 0.82915619758885, "NaN"),
label = "values in the table"
)
footnote <- tb[["footnotes"]]
jaspTools::expect_equal_tables(
test = footnote,
ref = list("value[PhiCoef]", 15, 0, "Phi coefficient is only available for 2 by 2 contingency Tables"),
label = "footnote under the table"
)
})
jasp-stats/jaspFrequencies documentation built on April 5, 2025, 3:53 p.m.
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context("Contingency Tables")
# does not test
# - row/column order (ascending/descending)
test_that("Main table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$counts <- "facFifty"
options$layers <- list(list(
name = "Layer 1",
variables = "facGender"
))
options$countsExpected <- TRUE
options$percentagesRow <- TRUE
options$percentagesColumn <- TRUE
options$percentagesTotal <- TRUE
options$residualsUnstandardized <- TRUE
options$residualsPearson <- TRUE
options$residualsStandardized <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabMain"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.489296636085627, 320, 394.529977794227, -3.75224327419637, 0.392638036809816,
-8.29368531337046, 0.236861584011843, -74.5299777942265, 0.710186513629842,
495, 420.470022205773, 3.63465733359156, 0.607361963190184,
8.29368531337046, 0.36639526276832, 74.5299777942265, "control",
"f", 0.603256846780163, 815, 815, 1, 0.603256846780163, " % within column",
"Count", "Expected count", "Pearson residuals", " % within row",
"Standardized residuals", " % of total", "Unstandardized residuals",
0.510703363914373, 334, 259.470022205773, 4.62687106461469,
0.623134328358209, 8.29368531337046, 0.247224278312361, 74.5299777942265,
0.289813486370158, 202, 276.529977794227, -4.48187647166511,
0.376865671641791, -8.29368531337046, 0.149518874907476, -74.5299777942265,
"experimental", "f", 0.396743153219837, 536, 536, 1, 0.396743153219837,
" % within column", "Count", "Expected count", "Pearson residuals",
" % within row", "Standardized residuals", " % of total", "Unstandardized residuals",
1, 654, 654, 0.484085862324204, 0.484085862324204, 1, 697, 697,
0.515914137675796, 0.515914137675796, "Total", "f", 1, 1351,
1351, 1, 1, " % within column", "Count", "Expected count", " % within row",
" % of total", 0.338688085676037, 253, 271.013344453711, -1.09420580859867,
0.581609195402299, -2.23255569694362, 0.211009174311927, -18.0133444537115,
0.402654867256637, 182, 163.986655546289, 1.40666311404823,
0.418390804597701, 2.23255569694361, 0.151793160967473, 18.0133444537114,
"control", "m", 0.362802335279399, 435, 435, 1, 0.3628023352794,
" % within column", "Count", "Expected count", "Pearson residuals",
" % within row", "Standardized residuals", " % of total", "Unstandardized residuals",
0.661311914323963, 494, 475.986655546289, 0.82565186283563,
0.646596858638743, 2.23255569694362, 0.412010008340284, 18.0133444537115,
0.597345132743363, 270, 288.013344453711, -1.06142191109686,
0.353403141361257, -2.23255569694362, 0.225187656380317, -18.0133444537115,
"experimental", "m", 0.6371976647206, 764, 764, 1, 0.6371976647206,
" % within column", "Count", "Expected count", "Pearson residuals",
" % within row", "Standardized residuals", " % of total", "Unstandardized residuals",
1, 747, 747, 0.62301918265221, 0.62301918265221, 1, 452, 452,
0.37698081734779, 0.37698081734779, "Total", "m", 1, 1199, 1199,
1, 1, " % within column", "Count", "Expected count", " % within row",
" % of total", 0.408993576017131, 573, 686.764705882353, -4.34113772648338,
0.4584, -9.05757740964558, 0.224705882352941, -113.764705882353,
0.589208006962576, 677, 563.235294117647, 4.79360911772348,
0.5416, 9.05757740964558, 0.265490196078431, 113.764705882353,
"control", "Total", 0.490196078431373, 1250, 1250, 1, 0.490196078431373,
" % within column", "Count", "Expected count", "Pearson residuals",
" % within row", "Standardized residuals", " % of total", "Unstandardized residuals",
0.591006423982869, 828, 714.235294117647, 4.25683576510241,
0.636923076923077, 9.05757740964558, 0.324705882352941, 113.764705882353,
0.410791993037424, 472, 585.764705882353, -4.70052046765545,
0.363076923076923, -9.05757740964558, 0.185098039215686, -113.764705882353,
"experimental", "Total", 0.509803921568627, 1300, 1300, 1, 0.509803921568627,
" % within column", "Count", "Expected count", "Pearson residuals",
" % within row", "Standardized residuals", " % of total", "Unstandardized residuals",
1, 1401, 1401, 0.549411764705882, 0.549411764705882, 1, 1149,
1149, 0.450588235294118, 0.450588235294118, "Total", "Total",
1, 2550, 2550, 1, 1, " % within column", "Count", "Expected count",
" % within row", " % of total"))
})
test_that("Multiple row and column variables give multiple main tables", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- c("facExperim", "facGender")
options$columns <- c("contBinom", "facFive")
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table1 <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabMain"]][["data"]]
table2 <- results[["results"]][["container_facExperim_facFive"]][["collection"]][["container_facExperim_facFive_crossTabMain"]][["data"]]
table3 <- results[["results"]][["container_facGender_contBinom"]][["collection"]][["container_facGender_contBinom_crossTabMain"]][["data"]]
table4 <- results[["results"]][["container_facGender_facFive"]][["collection"]][["container_facGender_facFive_crossTabMain"]][["data"]]
expect_is(table1, "list", label = "facExperim-contBinom table")
expect_is(table2, "list", label = "facExperim-Facfive table")
expect_is(table3, "list", label = "facGender-contBinom table")
expect_is(table4, "list", label = "facGender-facFive table")
})
test_that("Chi-Squared test table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$counts <- "facFifty"
options$chiSquared <- TRUE
options$chiSquaredContinuityCorrection <- TRUE
options$likelihoodRatio <- TRUE
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabChisq"]][["data"]]
jaspTools::expect_equal_tables(table,
list("", 44468347240355080, 63462127883801120, "<unicode>", "", 1,
1, 1, "", 1.91958529099645e-19, 1.33379878452991e-19, 0, "N",
"<unicode><unicode> continuity correction", "<unicode><unicode>",
"Likelihood ratio", 2550, 81.3201582621313, 82.0397085317219,
82.4643894680383))
})
test_that("Nominal table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$contingencyCoefficient <- TRUE
options$phiAndCramersV <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabNominal"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contingency coefficient", 0.0807792391722019, "Phi-coefficient",
-0.0810440898473108, "Cramer's V ", 0.0810440898473108)
)
})
test_that("Log Odds Ratio table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$oddsRatio <- TRUE
options$oddsRatioCiLevel <- 0.90
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabLogOdds"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Odds ratio", -0.329205575243527, -0.998167649205055, 0.339756498718001,"",
"Fisher's exact test ", -0.325882968750928, -1.07370478788709,
0.415368461868818, 0.5435617)
)
})
test_that("Ordinal Gamma table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$gamma <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabGamma"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.163132137030995, 0.197938461395245, -0.551084392520947, 0.224820118458957)
)
})
test_that("Kendall's Tau table results match", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$kendallsTauB <- TRUE
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
table <- results[["results"]][["container_facExperim_contBinom"]][["collection"]][["container_facExperim_contBinom_crossTabKendallTau"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.0810440898473108, 0.420024632711394, 1, -0.806378512498144)
)
})
test_that("Goodman-Kruskal lambda and Cramer's V results match", {
# test based on example data from p. 1130
# Kvålseth, T. O. (2018) Measuring association between nominal categorical variables: an alternative to the Goodman–Kruskal lambda, Journal of Applied Statistics, 45:6,1118-1132, DOI: 10.1080/02664763.2017.1346066
comb <- expand.grid(var1=letters[1:3], var2=letters[1:2], KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE)
counts <- c(40, 15, 5, 5, 25, 10)
df <- data.frame(var1 = rep(comb$var1, counts), var2 = rep(comb$var2, counts))
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "var1"
options$columns <- "var2"
options$lambda <- TRUE
options$phiAndCramersV <- TRUE
results <- jaspTools::runAnalysis("ContingencyTables", df, options)
table <- results[["results"]][["container_var1_var2"]][["collection"]][["container_var1_var2_crossTabNominal"]][["data"]][[1]]
# reported 0.3636
testthat::expect_equal(table[["value[LambdaC]"]], 0.363636363636364)
# reported 0.37
testthat::expect_equal(table[["value[LambdaS]"]], 0.369318181818182)
# reported 0.53
testthat::expect_equal(table[["value[CramerV]"]], 0.534135681195261)
})
test_that("Analysis handles errors", {
options <- jaspTools::analysisOptions("ContingencyTables")
options$rows <- "facExperim"
options$columns <- "contBinom"
options$counts <- "contNormal"
results <- jaspTools::runAnalysis("ContingencyTables", "test.csv", options)
errorMsg <- results[["results"]][["errorMessage"]]
expect_is(errorMsg, "character")
})
options <- analysisOptions("ContingencyTables")
options$columns <- "V2"
options$contingencyCoefficient <- TRUE
options$phiAndCramersV <- TRUE
options$rows <- "V1"
set.seed(1)
# data is a random sample from https://github.com/jasp-stats/jasp-issues/issues/811
dataset <- structure(list(V1 = c(1L, 2L, 1L, 2L, 1L, 0L, 2L, 0L, 0L, 1L), V2 = c(0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L)), row.names = c(NA, -10L), class = "data.frame")
results <- jaspTools::runAnalysis("ContingencyTables", dataset, options)
test_that("Phi coefficient is only available for 2 by 2 contingency tables", {
tb <- results[["results"]][["container_V1_V2"]][["collection"]][["container_V1_V2_crossTabNominal"]]
table <- tb[["data"]]
jaspTools::expect_equal_tables(
test = table,
ref = list("Contingency coefficient", "Cramer's V ", "Phi-coefficient", 0.638284738504225, 0.82915619758885, "NaN"),
label = "values in the table"
)
footnote <- tb[["footnotes"]]
jaspTools::expect_equal_tables(
test = footnote,
ref = list("value[PhiCoef]", 15, 0, "Phi coefficient is only available for 2 by 2 contingency Tables"),
label = "footnote under the table"
)
})
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