# contingency_table works Code select(df1, -expression) Output # A tibble: 1 x 12 statistic df p.value method effectsize estimate 1 8.74 2 0.0126 Pearson's Chi-squared test Cramer's V (adj.) 0.464 conf.level conf.low conf.high conf.method conf.distribution n.obs 1 0.99 0 0.937 ncp chisq 32 --- Code df1[["expression"]] Output [[1]] list(chi["Pearson"]^2 * "(" * 2 * ")" == "8.74073", italic(p) == "0.01265", widehat(italic("V"))["Cramer"] == "0.46431", CI["99%"] ~ "[" * "0.00000", "0.93683" * "]", italic("n")["obs"] == "32") --- Code select(df2, -expression) Output # A tibble: 1 x 12 statistic df p.value method effectsize 1 457. 1 2.30e-101 Pearson's Chi-squared test Cramer's V (adj.) estimate conf.level conf.low conf.high conf.method conf.distribution n.obs 1 0.455 0.95 0.413 0.497 ncp chisq 2201 --- Code df2[["expression"]] Output [[1]] list(chi["Pearson"]^2 * "(" * 1 * ")" == "456.87", italic(p) == "2.30e-101", widehat(italic("V"))["Cramer"] == "0.46", CI["95%"] ~ "[" * "0.41", "0.50" * "]", italic("n")["obs"] == "2,201") --- Code select(df3, -expression) Output # A tibble: 1 x 12 statistic df p.value method effectsize estimate 1 15.8 15 0.399 Pearson's Chi-squared test Cramer's V (adj.) 0.0558 conf.level conf.low conf.high conf.method conf.distribution n.obs 1 0.99 0 0.252 ncp chisq 52 --- Code df3[["expression"]] Output [[1]] list(chi["Pearson"]^2 * "(" * 15 * ")" == "15.75", italic(p) == "0.40", widehat(italic("V"))["Cramer"] == "0.06", CI["99%"] ~ "[" * "0.00", "0.25" * "]", italic("n")["obs"] == "52") # paired contingency_table works Code select(df1, -expression) Output # A tibble: 1 x 11 statistic df p.value method effectsize estimate 1 13.3 1 0.000261 McNemar's Chi-squared test Cohen's g 0.333 conf.level conf.low conf.high conf.method n.obs 1 0.95 0.164 0.427 binomial 100 --- Code df1[["expression"]] Output [[1]] list(chi["McNemar"]^2 * "(" * 1 * ")" == "13.33333", italic(p) == "0.00026", widehat(italic("g"))["Cohen"] == "0.33333", CI["95%"] ~ "[" * "0.16436", "0.42663" * "]", italic("n")["pairs"] == "100") --- Code select(df2, -expression) Output # A tibble: 1 x 11 statistic df p.value method effectsize estimate 1 13.3 1 0.000261 McNemar's Chi-squared test Cohen's g 0.333 conf.level conf.low conf.high conf.method n.obs 1 0.9 0.229 0.5 binomial 95 --- Code df2[["expression"]] Output [[1]] list(chi["McNemar"]^2 * "(" * 1 * ")" == "13.333", italic(p) == "2.607e-04", widehat(italic("g"))["Cohen"] == "0.333", CI["90%"] ~ "[" * "0.229", "0.500" * "]", italic("n")["pairs"] == "95") # Goodness of Fit contingency_table works without counts Code select(df1, -expression) Output # A tibble: 1 x 12 statistic df p.value method effectsize 1 1.12 1 0.289 Chi-squared test for given probabilities Pearson's C estimate conf.level conf.low conf.high conf.method conf.distribution n.obs 1 0.184 0.99 0 0.541 ncp chisq 32 --- Code df1[["expression"]] Output [[1]] list(chi["gof"]^2 * "(" * 1 * ")" == "1.12500", italic(p) == "0.28884", widehat(italic("C"))["Pearson"] == "0.18429", CI["99%"] ~ "[" * "0.00000", "0.54074" * "]", italic("n")["obs"] == "32") --- Code select(df2, -expression) Output # A tibble: 1 x 12 statistic df p.value method effectsize 1 722. 1 3.92e-159 Chi-squared test for given probabilities Pearson's C estimate conf.level conf.low conf.high conf.method conf.distribution n.obs 1 0.497 0.95 0.474 1 ncp chisq 2201 --- Code df2[["expression"]] Output [[1]] list(chi["gof"]^2 * "(" * 1 * ")" == "722.45", italic(p) == "3.92e-159", widehat(italic("C"))["Pearson"] == "0.50", CI["95%"] ~ "[" * "0.47", "1.00" * "]", italic("n")["obs"] == "2,201") --- Code select(df3, -expression) Output # A tibble: 1 x 12 statistic df p.value method 1 33.8 3 0.000000223 Chi-squared test for given probabilities effectsize estimate conf.level conf.low conf.high conf.method 1 Pearson's C 0.555 0.95 0.385 0.658 ncp conf.distribution n.obs 1 chisq 76 --- Code df3[["expression"]] Output [[1]] list(chi["gof"]^2 * "(" * 3 * ")" == "33.76", italic(p) == "2.23e-07", widehat(italic("C"))["Pearson"] == "0.55", CI["95%"] ~ "[" * "0.38", "0.66" * "]", italic("n")["obs"] == "76") # bayesian (proportion test) Code select(df1, -expression) Output # A tibble: 1 x 3 bf10 prior.scale method 1 0.247 1 Bayesian one-way contingency table analysis --- Code df1[["expression"]] Output [[1]] list(log[e] * (BF["01"]) == "1.40", italic("a")["Gunel-Dickey"] == "1.00") --- Code select(df2, -expression) Output # A tibble: 1 x 3 bf10 prior.scale method 1 0.579 10 Bayesian one-way contingency table analysis --- Code df2[["expression"]] Output [[1]] list(log[e] * (BF["01"]) == "0.55", italic("a")["Gunel-Dickey"] == "10.00") # bayesian (contingency tab) Code df1[["expression"]] Output [[1]] list(log[e] * (BF["01"]) == "-2.82", widehat(italic("V"))["Cramer"]^"posterior" == "0.41", CI["95%"]^ETI ~ "[" * "0.00", "0.68" * "]", italic("a")["Gunel-Dickey"] == "1.00") --- Code df2[["expression"]] Output [[1]] list(log[e] * (BF["01"]) == "3.29", widehat(italic("V"))["Cramer"]^"posterior" == "0.00", CI["95%"]^ETI ~ "[" * "0.00", "0.26" * "]", italic("a")["Gunel-Dickey"] == "1.00")