# one_sample_test parametric works Code select(df1, -expression) Output # A tibble: 1 x 14 mu statistic df.error p.value method alternative effectsize 1 120 -2.67 78 0.00910 One Sample t-test two.sided Hedges' g estimate conf.level conf.low conf.high conf.method conf.distribution n.obs 1 -0.298 0.95 -0.520 -0.0738 ncp t 79 --- Code df1[["expression"]] Output [[1]] list(italic("t")["Student"] * "(" * 78 * ")" == "-2.67496", italic(p) == "0.00910", widehat(italic("g"))["Hedges"] == "-0.29805", CI["95%"] ~ "[" * "-0.52046", "-0.07382" * "]", italic("n")["obs"] == "79") --- Code select(df2, -expression) Output # A tibble: 1 x 14 mu statistic df.error p.value method alternative effectsize 1 120 -2.67 78 0.00910 One Sample t-test two.sided Cohen's d estimate conf.level conf.low conf.high conf.method conf.distribution n.obs 1 -0.301 0.9 -0.489 -0.111 ncp t 79 --- Code df2[["expression"]] Output [[1]] list(italic("t")["Student"] * "(" * 78 * ")" == "-2.6750", italic(p) == "0.0091", widehat(italic("d"))["Cohen"] == "-0.3010", CI["90%"] ~ "[" * "-0.4893", "-0.1108" * "]", italic("n")["obs"] == "79") # one_sample_test non-parametric works Code select(df1, -expression) Output # A tibble: 1 x 11 statistic p.value method alternative effectsize 1 754. 0.323 Wilcoxon signed rank test two.sided r (rank biserial) estimate conf.level conf.low conf.high conf.method n.obs 1 -0.149 0.95 -0.416 0.143 normal 60 --- Code df1[["expression"]] Output [[1]] list(italic("V")["Wilcoxon"] == "753.5000", italic(p) == "0.3227", widehat(italic("r"))["biserial"]^"rank" == "-0.1486", CI["95%"] ~ "[" * "-0.4162", "0.1427" * "]", italic("n")["obs"] == "60") --- Code select(df2, -expression) Output # A tibble: 1 x 11 statistic p.value method alternative effectsize 1 262 0.0000125 Wilcoxon signed rank test two.sided r (rank biserial) estimate conf.level conf.low conf.high conf.method n.obs 1 -0.672 0.95 -0.806 -0.472 normal 56 --- Code df2[["expression"]] Output [[1]] list(italic("V")["Wilcoxon"] == "262.0000", italic(p) == "1.2527e-05", widehat(italic("r"))["biserial"]^"rank" == "-0.6717", CI["95%"] ~ "[" * "-0.8058", "-0.4720" * "]", italic("n")["obs"] == "56") # one_sample_test robust works Code select(df1, -expression) Output # A tibble: 1 x 9 statistic p.value n.obs method effectsize 1 0.787 0.455 11 Bootstrap-t method for one-sample test Trimmed mean estimate conf.level conf.low conf.high 1 9 0.9 6.55 11.5 --- Code df1[["expression"]] Output [[1]] list(italic("t")["bootstrapped"] == "0.7866", italic(p) == "0.4550", widehat(mu)["trimmed"] == "9.0000", CI["90%"] ~ "[" * "6.5487", "11.4513" * "]", italic("n")["obs"] == "11") --- Code select(df2, -expression) Output # A tibble: 1 x 9 statistic p.value n.obs method effectsize 1 -3.81 0.04 56 Bootstrap-t method for one-sample test Trimmed mean estimate conf.level conf.low conf.high 1 0.0390 0.99 -0.0669 0.145 --- Code df2[["expression"]] Output [[1]] list(italic("t")["bootstrapped"] == "-3.8075", italic(p) == "0.0400", widehat(mu)["trimmed"] == "0.0390", CI["99%"] ~ "[" * "-0.0669", "0.1448" * "]", italic("n")["obs"] == "56") # one_sample_test bayesian works Code names(df_results) Output [1] "term" "effectsize" "estimate" [4] "conf.level" "conf.low" "conf.high" [7] "pd" "prior.distribution" "prior.location" [10] "prior.scale" "bf10" "method" [13] "conf.method" "log_e_bf10" "n.obs" [16] "expression" --- Code df1[["expression"]] Output [[1]] list(log[e] * (BF["01"]) == "-47.84", widehat(delta)["difference"]^"posterior" == "-1.76", CI["90%"]^ETI ~ "[" * "-1.99", "-1.51" * "]", italic("r")["Cauchy"]^"JZS" == "0.99") --- Code df2[["expression"]] Output [[1]] list(log[e] * (BF["01"]) == "2.125", widehat(delta)["difference"]^"posterior" == "0.018", CI["95%"]^ETI ~ "[" * "-0.234", "0.274" * "]", italic("r")["Cauchy"]^"JZS" == "0.900")