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Type 'q()' to quit R. > ### Regression tests for mid-p confidence intervals and mid-p-values > > set.seed(290875) > library("coin") Loading required package: survival > isequal <- coin:::isequal > options(useFancyQuotes = FALSE) > > > ### > ### Mid-p confidence intervals > ### > > > ### Berry and Armitage (1995, p. 420) > mpci <- coin:::confint_binom(5, 20, level = 0.95, method = "mid-p") > stopifnot(isequal(round(mpci, 3), c(0.098, 0.470))) > > > ### Agresti and Gottard (2001, p. 369) > mpci <- coin:::confint_binom(4, 10, level = 0.95, method = "mid-p") > stopifnot(isequal(round(mpci, 3), c(0.142, 0.709))) > > mpci <- coin:::confint_binom(0, 10, level = 0.95, method = "mid-p") > stopifnot(isequal(round(mpci[1], 3), 0)) > > mpci <- coin:::confint_binom(10, 10, level = 0.95, method = "mid-p") > stopifnot(isequal(round(mpci[2], 3), 1)) > > > ### Newcombe (1998, p. 861, Tab. I) > mpci <- coin:::confint_binom(81, 263, level = 0.95, method = "mid-p") > stopifnot(isequal(round(mpci, 4), c(0.2544, 0.3658))) > > mpci <- coin:::confint_binom(15, 148, level = 0.95, method = "mid-p") > stopifnot(isequal(round(mpci, 4), c(0.0601, 0.1581))) > > mpci <- coin:::confint_binom(0, 20, level = 0.95, method = "mid-p") > stopifnot(isequal(round(mpci, 4), c(0.0000, 0.1391))) > > mpci <- coin:::confint_binom(1, 29, level = 0.95, method = "mid-p") > stopifnot(isequal(round(mpci, 4), c(0.0017, 0.1585))) > > > ### > ### Mid-p-pvalues > ### > > > ### Data from Hwang and Yang (2001, p. 810) > tea <- matrix(c(3, 1, + 1, 3), + nrow = 2, byrow = TRUE) > > ### Results from Hwang and Yang (2001, p. 810) > ct_e <- chisq_test(as.table(tea), + distribution = "exact") > stopifnot(isequal(round(pvalue(ct_e), 3), 0.486)) > stopifnot(isequal(round(midpvalue(ct_e), 3), 0.257)) > > it_e_s <- independence_test(as.table(tea), + distribution = "exact", + teststat = "scalar") > stopifnot(isequal(pvalue(it_e_s), pvalue(ct_e))) > stopifnot(isequal(midpvalue(it_e_s), midpvalue(ct_e))) > > it_e_q <- independence_test(as.table(tea), + distribution = "exact", + teststat = "quad") > stopifnot(isequal(pvalue(it_e_q), pvalue(ct_e))) > stopifnot(isequal(midpvalue(it_e_q), midpvalue(ct_e))) > > it_e_s_gr <- independence_test(as.table(tea), + distribution = "exact", + teststat = "scalar", + alternative = "greater") > stopifnot(isequal(round(pvalue(it_e_s_gr), 3), 0.243)) > stopifnot(isequal(round(midpvalue(it_e_s_gr), 4), 0.1286)) > # p = 0.1285 according to Hwang and Yang (2001) > > ### Additional results: Monte Carlo > set.seed(290875) > ct_m <- chisq_test(as.table(tea), + distribution = "approximate") > (p <- pvalue(ct_m)) [1] 0.4835 99 percent confidence interval: 0.4705882 0.4964275 > pci <- attr(p, "conf.int") > stopifnot(pci[1] < 0.486 & pci[2] > 0.486) > (mp <- midpvalue(ct_m)) [1] 0.25755 99 percent confidence interval: 0.2463534 0.2688772 > mpci <- attr(mp, "conf.int") > stopifnot(mpci[1] < 0.257 & mpci[2] > 0.257) > > set.seed(290875) > it_m_s <- independence_test(as.table(tea), + distribution = approximate(nresample = 10000), + teststat = "scalar") > stopifnot(isequal(pvalue(it_m_s), pvalue(ct_m))) > stopifnot(isequal(midpvalue(it_m_s), midpvalue(ct_m))) > > set.seed(290875) > it_m_q <- independence_test(as.table(tea), + distribution = approximate(nresample = 10000), + teststat = "quad") > stopifnot(isequal(pvalue(it_m_q), pvalue(ct_m))) > stopifnot(isequal(midpvalue(it_m_q), midpvalue(ct_m))) > > set.seed(290875) > it_m_s_gr <- independence_test(as.table(tea), + distribution = approximate(nresample = 10000), + teststat = "scalar", + alternative = "greater") > (p <- pvalue(it_m_s_gr)) [1] 0.2419 99 percent confidence interval: 0.2309447 0.2531023 > pci <- attr(p, "conf.int") > stopifnot(pci[1] < 0.243 & pci[2] > 0.243) > (mp <- midpvalue(it_m_s_gr)) [1] 0.1288 99 percent confidence interval: 0.1203477 0.1376053 > mpci <- attr(mp, "conf.int") > stopifnot(mpci[1] < 0.1286 & mpci[2] > 0.1286) > > > ### Data from Lydersen and Laake (2003, p. 3862) > davis <- matrix(c(3, 6, + 2, 19), + nrow = 2, byrow = TRUE) > > ### Results from Lydersen and Laake (2003, p. 3863, Tab. II) > ct_e <- chisq_test(as.table(davis), + distribution = "exact") > stopifnot(isequal(round(pvalue(ct_e), 4), 0.2860)) > stopifnot(isequal(round(midpvalue(ct_e), 4), 0.1527)) > > it_e_s <- independence_test(as.table(davis), + distribution = "exact", + teststat = "scalar") > stopifnot(isequal(pvalue(it_e_s), pvalue(ct_e))) > stopifnot(isequal(midpvalue(it_e_s), midpvalue(ct_e))) > > it_e_q <- independence_test(as.table(davis), + distribution = "exact", + teststat = "quad") > stopifnot(isequal(pvalue(it_e_q), pvalue(ct_e))) > stopifnot(isequal(midpvalue(it_e_q), midpvalue(ct_e))) > > ### Additional results: Monte Carlo > set.seed(290875) > ct_m <- chisq_test(as.table(davis), + distribution = "approximate") > (p <- pvalue(ct_m)) [1] 0.2847 99 percent confidence interval: 0.273131 0.296475 > pci <- attr(p, "conf.int") > stopifnot(pci[1] < 0.2860 & pci[2] > 0.2860) > (mp <- midpvalue(ct_m)) [1] 0.15175 99 percent confidence interval: 0.1427229 0.1612083 > mpci <- attr(mp, "conf.int") > stopifnot(mpci[1] < 0.1527 & mpci[2] > 0.1527) > > set.seed(290875) > it_m_s <- independence_test(as.table(davis), + distribution = approximate(nresample = 10000), + teststat = "scalar") > stopifnot(isequal(pvalue(it_m_s), pvalue(ct_m))) > stopifnot(isequal(midpvalue(it_m_s), midpvalue(ct_m))) > > set.seed(290875) > it_m_q <- independence_test(as.table(davis), + distribution = approximate(nresample = 10000), + teststat = "quad") > stopifnot(isequal(pvalue(it_m_q), pvalue(ct_m))) > stopifnot(isequal(midpvalue(it_m_q), midpvalue(ct_m))) > > > ### Data from Lydersen, Fagerland and Laake (2009, p. 1160, Tab. I) > cardiac <- matrix(c(1, 33, + 7, 27), + nrow = 2, byrow = TRUE) > > ### Results from Lydersen, Fagerland and Laake (2009, pp. 1171--1172) > ct_e <- chisq_test(as.table(cardiac), + distribution = "exact") > stopifnot(isequal(round(pvalue(ct_e), 4), 0.0544)) > stopifnot(isequal(round(midpvalue(ct_e), 4), 0.0297)) > > it_e_s <- independence_test(as.table(cardiac), + distribution = "exact", + teststat = "scalar") > stopifnot(isequal(pvalue(it_e_s), pvalue(ct_e))) > stopifnot(isequal(midpvalue(it_e_s), midpvalue(ct_e))) > > it_e_q <- independence_test(as.table(cardiac), + distribution = "exact", + teststat = "quad") > stopifnot(isequal(pvalue(it_e_q), pvalue(ct_e))) > stopifnot(isequal(midpvalue(it_e_q), midpvalue(ct_e))) > > ### Additional results: Monte Carlo > set.seed(290875) > ct_m <- chisq_test(as.table(cardiac), + distribution = approximate(nresample = 10000)) > (p <- pvalue(ct_m)) [1] 0.0557 99 percent confidence interval: 0.04995533 0.06187217 > pci <- attr(p, "conf.int") > stopifnot(pci[1] < 0.0544 & pci[2] > 0.0544) > (mp <- midpvalue(ct_m)) [1] 0.0302 99 percent confidence interval: 0.02601100 0.03483585 > mpci <- attr(mp, "conf.int") > stopifnot(mpci[1] < 0.0297 & mpci[2] > 0.0297) > > set.seed(290875) > it_m_s <- independence_test(as.table(cardiac), + distribution = approximate(nresample = 10000), + teststat = "scalar") > stopifnot(isequal(pvalue(it_m_s), pvalue(ct_m))) > stopifnot(isequal(midpvalue(it_m_s), midpvalue(ct_m))) > > set.seed(290875) > it_m_q <- independence_test(as.table(cardiac), + distribution = approximate(nresample = 10000), + teststat = "quad") > stopifnot(isequal(pvalue(it_m_q), pvalue(ct_m))) > stopifnot(isequal(midpvalue(it_m_q), midpvalue(ct_m))) > > > ### Data from Lydersen, Fagerland and Laake (2009, p. 1160, Tab. II) > exfoliative <- matrix(c( 0, 16, + 15, 57), + nrow = 2, byrow = TRUE) > > ### Results from Lydersen, Fagerland and Laake (2009, p. 1173) > ct_e <- chisq_test(as.table(exfoliative), + distribution = "exact") > stopifnot(isequal(round(pvalue(ct_e), 4), 0.0629)) > stopifnot(isequal(round(midpvalue(ct_e), 4), 0.0447)) > > it_e_s <- independence_test(as.table(exfoliative), + distribution = "exact", + teststat = "scalar") > stopifnot(isequal(pvalue(it_e_s), pvalue(ct_e))) > stopifnot(isequal(midpvalue(it_e_s), midpvalue(ct_e))) > > it_e_q <- independence_test(as.table(exfoliative), + distribution = "exact", + teststat = "quad") > stopifnot(isequal(pvalue(it_e_q), pvalue(ct_e))) > stopifnot(isequal(midpvalue(it_e_q), midpvalue(ct_e))) > > ### Additional results: Monte Carlo > set.seed(290875) > ct_m <- chisq_test(as.table(exfoliative), + distribution = approximate(nresample = 10000)) > (p <- pvalue(ct_m)) [1] 0.0643 99 percent confidence interval: 0.05814061 0.07087830 > pci <- attr(p, "conf.int") > stopifnot(pci[1] < 0.0629 & pci[2] > 0.0629) > (mp <- midpvalue(ct_m)) [1] 0.0444 99 percent confidence interval: 0.03930789 0.04992546 > mpci <- attr(mp, "conf.int") > stopifnot(mpci[1] < 0.0447 & mpci[2] > 0.0447) > > set.seed(290875) > it_m_s <- independence_test(as.table(exfoliative), + distribution = approximate(nresample = 10000), + teststat = "scalar") > stopifnot(isequal(pvalue(it_m_s), pvalue(ct_m))) > stopifnot(isequal(midpvalue(it_m_s), midpvalue(ct_m))) > > set.seed(290875) > it_m_q <- independence_test(as.table(exfoliative), + distribution = approximate(nresample = 10000), + teststat = "quad") > stopifnot(isequal(pvalue(it_m_q), pvalue(ct_m))) > stopifnot(isequal(midpvalue(it_m_q), midpvalue(ct_m))) > > > ### Data from Fagerland, Lydersen and Laake (2013, p. 2, Tab. 1) > ahr <- matrix(c(1, 1, + 7, 12), + nrow = 2, byrow = TRUE) > > ### Results from Fagerland, Lydersen and Laake (2013, p. 7, Tab. 6) > mt_e <- mh_test(as.table(ahr), + distribution = "exact") > stopifnot(isequal(round(pvalue(mt_e), 4), 0.0703)) > stopifnot(isequal(round(midpvalue(mt_e), 4), 0.0391)) > > st_e_s <- symmetry_test(as.table(ahr), + distribution = "exact", + teststat = "scalar") > stopifnot(isequal(pvalue(st_e_s), pvalue(mt_e))) > stopifnot(isequal(midpvalue(st_e_s), midpvalue(mt_e))) > > st_e_q <- symmetry_test(as.table(ahr), + distribution = "exact", + teststat = "quad") > stopifnot(isequal(pvalue(st_e_q), pvalue(mt_e))) > stopifnot(isequal(midpvalue(st_e_q), midpvalue(mt_e))) > > ### Additional results: Monte Carlo > set.seed(290875) > mt_m <- mh_test(as.table(ahr), + distribution = approximate(nresample = 10000)) > (p <- pvalue(mt_m)) [1] 0.0746 99 percent confidence interval: 0.06798612 0.08162261 > pci <- attr(p, "conf.int") > stopifnot(pci[1] < 0.0703 & pci[2] > 0.0703) > (mp <- midpvalue(mt_m)) [1] 0.04125 99 percent confidence interval: 0.03629541 0.04654098 > mpci <- attr(mp, "conf.int") > stopifnot(mpci[1] < 0.0391 & mpci[2] > 0.0391) > > set.seed(290875) > st_m_s <- symmetry_test(as.table(ahr), + distribution = approximate(nresample = 10000), + teststat = "scalar") > stopifnot(isequal(pvalue(st_m_s), pvalue(mt_m))) > stopifnot(isequal(midpvalue(st_m_s), midpvalue(mt_m))) > > set.seed(290875) > st_m_q <- symmetry_test(as.table(ahr), + distribution = approximate(nresample = 10000), + teststat = "quad") > stopifnot(isequal(pvalue(st_m_q), pvalue(mt_m))) > stopifnot(isequal(midpvalue(st_m_q), midpvalue(mt_m))) > > > ### Data from Fagerland, Lydersen and Laake (2013, p. 2, Tab. 2) > therapy <- matrix(c(59, 6, + 16, 80), + nrow = 2, byrow = TRUE) > > ### Results from Fagerland, Lydersen and Laake (2013, p. 7, Tab. 6) > mt_e <- mh_test(as.table(therapy), + distribution = "exact") > stopifnot(isequal(round(pvalue(mt_e), 4), 0.0525)) > stopifnot(isequal(round(midpvalue(mt_e), 4), 0.0347)) > > st_e_s <- symmetry_test(as.table(therapy), + distribution = "exact", + teststat = "scalar") > stopifnot(isequal(pvalue(st_e_s), pvalue(mt_e))) > stopifnot(isequal(midpvalue(st_e_s), midpvalue(mt_e))) > > st_e_q <- symmetry_test(as.table(therapy), + distribution = "exact", + teststat = "quad") > stopifnot(isequal(pvalue(st_e_q), pvalue(mt_e))) > stopifnot(isequal(midpvalue(st_e_q), midpvalue(mt_e))) > > ### Additional results: Monte Carlo > set.seed(290875) > mt_m <- mh_test(as.table(therapy), + distribution = approximate(nresample = 10000)) > (p <- pvalue(mt_m)) [1] 0.0504 99 percent confidence interval: 0.04493012 0.05630272 > pci <- attr(p, "conf.int") > stopifnot(pci[1] < 0.0525 & pci[2] > 0.0525) > (mp <- midpvalue(mt_m)) [1] 0.03345 99 percent confidence interval: 0.02898979 0.03825402 > mpci <- attr(mp, "conf.int") > stopifnot(mpci[1] < 0.0347 & mpci[2] > 0.0347) > > set.seed(290875) > st_m_s <- symmetry_test(as.table(therapy), + distribution = approximate(nresample = 10000), + teststat = "scalar") > stopifnot(isequal(pvalue(st_m_s), pvalue(mt_m))) > stopifnot(isequal(midpvalue(st_m_s), midpvalue(mt_m))) > > set.seed(290875) > st_m_q <- symmetry_test(as.table(therapy), + distribution = approximate(nresample = 10000), + teststat = "quad") > stopifnot(isequal(pvalue(st_m_q), pvalue(mt_m))) > stopifnot(isequal(midpvalue(st_m_q), midpvalue(mt_m))) > > > ### Data from Barnard (1989, p. 1470) > barnard50 <- matrix(c(5, 0, + 0, 5), + nrow = 2, byrow = TRUE) > > barnard51 <- matrix(c(5, 0, + 1, 4), + nrow = 2, byrow = TRUE) > > barnard52 <- matrix(c(5, 0, + 2, 3), + nrow = 2, byrow = TRUE) > > barnard53 <- matrix(c(5, 0, + 3, 2), + nrow = 2, byrow = TRUE) > > barnard54 <- matrix(c(5, 0, + 4, 1), + nrow = 2, byrow = TRUE) > > ## barnard55 <- matrix(c(5, 0, > ## 5, 0), > ## nrow = 2, byrow = TRUE) > > barnard40 <- matrix(c(4, 1, + 0, 5), + nrow = 2, byrow = TRUE) > > barnard41 <- matrix(c(4, 1, + 1, 4), + nrow = 2, byrow = TRUE) > > barnard42 <- matrix(c(4, 1, + 2, 3), + nrow = 2, byrow = TRUE) > > barnard43 <- matrix(c(4, 1, + 3, 2), + nrow = 2, byrow = TRUE) > > barnard44 <- matrix(c(4, 1, + 4, 1), + nrow = 2, byrow = TRUE) > > barnard45 <- matrix(c(4, 1, + 5, 0), + nrow = 2, byrow = TRUE) > > barnard30 <- matrix(c(3, 2, + 0, 5), + nrow = 2, byrow = TRUE) > > barnard31 <- matrix(c(3, 2, + 1, 4), + nrow = 2, byrow = TRUE) > > barnard32 <- matrix(c(3, 2, + 2, 3), + nrow = 2, byrow = TRUE) > > barnard33 <- matrix(c(3, 2, + 3, 2), + nrow = 2, byrow = TRUE) > > barnard34 <- matrix(c(3, 2, + 4, 1), + nrow = 2, byrow = TRUE) > > barnard35 <- matrix(c(3, 2, + 5, 0), + nrow = 2, byrow = TRUE) > > barnard20 <- matrix(c(2, 3, + 0, 5), + nrow = 2, byrow = TRUE) > > barnard21 <- matrix(c(2, 3, + 1, 4), + nrow = 2, byrow = TRUE) > > barnard22 <- matrix(c(2, 3, + 2, 3), + nrow = 2, byrow = TRUE) > > barnard23 <- matrix(c(2, 3, + 3, 2), + nrow = 2, byrow = TRUE) > > barnard24 <- matrix(c(2, 3, + 4, 1), + nrow = 2, byrow = TRUE) > > barnard25 <- matrix(c(2, 3, + 5, 0), + nrow = 2, byrow = TRUE) > > barnard10 <- matrix(c(1, 4, + 0, 5), + nrow = 2, byrow = TRUE) > > barnard11 <- matrix(c(1, 4, + 1, 4), + nrow = 2, byrow = TRUE) > > barnard12 <- matrix(c(1, 4, + 2, 3), + nrow = 2, byrow = TRUE) > > barnard13 <- matrix(c(1, 4, + 3, 2), + nrow = 2, byrow = TRUE) > > barnard14 <- matrix(c(1, 4, + 4, 1), + nrow = 2, byrow = TRUE) > > barnard15 <- matrix(c(1, 4, + 5, 0), + nrow = 2, byrow = TRUE) > > ## barnard00 <- matrix(c(0, 5, > ## 0, 5), > ## nrow = 2, byrow = TRUE) > > barnard01 <- matrix(c(0, 5, + 1, 4), + nrow = 2, byrow = TRUE) > > barnard02 <- matrix(c(0, 5, + 2, 3), + nrow = 2, byrow = TRUE) > > barnard03 <- matrix(c(0, 5, + 3, 2), + nrow = 2, byrow = TRUE) > > barnard04 <- matrix(c(0, 5, + 4, 1), + nrow = 2, byrow = TRUE) > > barnard05 <- matrix(c(0, 5, + 5, 0), + nrow = 2, byrow = TRUE) > > ### Results from Barnard (1989, p. 1471, Tab. III; p. 1474, Tab. IV) > it50_e <- independence_test(as.table(barnard50), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it50_e), 4), 0.0040)) > stopifnot(isequal(round(midpvalue(it50_e), 4), 0.0020)) > > it51_e <- independence_test(as.table(barnard51), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it51_e), 4), 0.0238)) > stopifnot(isequal(round(midpvalue(it51_e), 4), 0.0119)) > > it52_e <- independence_test(as.table(barnard52), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it52_e), 4), 0.0833)) > stopifnot(isequal(round(midpvalue(it52_e), 4), 0.0417)) > > it53_e <- independence_test(as.table(barnard53), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it53_e), 4), 0.2222)) > stopifnot(isequal(round(midpvalue(it53_e), 4), 0.1111)) > > it54_e <- independence_test(as.table(barnard54), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it54_e), 4), 0.5000)) > stopifnot(isequal(round(midpvalue(it54_e), 4), 0.2500)) > > ## it55_e <- independence_test(as.table(barnard55), > ## distribution = "exact", > ## alternative = "greater") > ## stopifnot(isequal(round(pvalue(it55_e), 4), 1.0000)) > ## stopifnot(isequal(round(midpvalue(it55_e), 4), 0.5000)) > > it40_e <- independence_test(as.table(barnard40), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it40_e), 4), 0.0238)) > stopifnot(isequal(round(midpvalue(it40_e), 4), 0.0119)) > > it41_e <- independence_test(as.table(barnard41), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it41_e), 4), 0.1032)) > stopifnot(isequal(round(midpvalue(it41_e), 4), 0.0536)) > > it42_e <- independence_test(as.table(barnard42), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it42_e), 4), 0.2619)) > stopifnot(isequal(round(midpvalue(it42_e), 4), 0.1429)) > > it43_e <- independence_test(as.table(barnard43), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it43_e), 4), 0.5000)) > stopifnot(isequal(round(midpvalue(it43_e), 4), 0.2917)) > > it44_e <- independence_test(as.table(barnard44), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it44_e), 4), 0.7778)) > stopifnot(isequal(round(midpvalue(it44_e), 4), 0.5000)) > > it45_e <- independence_test(as.table(barnard45), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it45_e), 4), 1.0000)) > stopifnot(isequal(round(midpvalue(it45_e), 4), 0.7500)) > > it30_e <- independence_test(as.table(barnard30), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it30_e), 4), 0.0833)) > stopifnot(isequal(round(midpvalue(it30_e), 4), 0.0417)) > > it31_e <- independence_test(as.table(barnard31), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it31_e), 4), 0.2619)) > stopifnot(isequal(round(midpvalue(it31_e), 4), 0.1429)) > > it32_e <- independence_test(as.table(barnard32), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it32_e), 4), 0.5000)) > stopifnot(isequal(round(midpvalue(it32_e), 4), 0.3016)) > > it33_e <- independence_test(as.table(barnard33), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it33_e), 4), 0.7381)) > stopifnot(isequal(round(midpvalue(it33_e), 4), 0.5000)) > > it34_e <- independence_test(as.table(barnard34), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it34_e), 4), 0.9167)) > stopifnot(isequal(round(midpvalue(it34_e), 4), 0.7083)) > > it35_e <- independence_test(as.table(barnard35), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it35_e), 4), 1.0000)) > stopifnot(isequal(round(midpvalue(it35_e), 4), 0.8889)) > > it20_e <- independence_test(as.table(barnard20), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it20_e), 4), 0.2222)) > stopifnot(isequal(round(midpvalue(it20_e), 4), 0.1111)) > > it21_e <- independence_test(as.table(barnard21), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it21_e), 4), 0.5000)) > stopifnot(isequal(round(midpvalue(it21_e), 4), 0.2917)) > > it22_e <- independence_test(as.table(barnard22), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it22_e), 4), 0.7381)) > stopifnot(isequal(round(midpvalue(it22_e), 4), 0.5000)) > > it23_e <- independence_test(as.table(barnard23), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it23_e), 4), 0.8968)) > stopifnot(isequal(round(midpvalue(it23_e), 4), 0.6984)) > > it24_e <- independence_test(as.table(barnard24), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it24_e), 4), 0.9762)) > stopifnot(isequal(round(midpvalue(it24_e), 4), 0.8571)) > > it25_e <- independence_test(as.table(barnard25), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it25_e), 4), 1.0000)) > stopifnot(isequal(round(midpvalue(it25_e), 4), 0.9583)) > > it10_e <- independence_test(as.table(barnard10), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it10_e), 4), 0.5000)) > stopifnot(isequal(round(midpvalue(it10_e), 4), 0.2500)) > > it11_e <- independence_test(as.table(barnard11), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it11_e), 4), 0.7778)) > stopifnot(isequal(round(midpvalue(it11_e), 4), 0.5000)) > > it12_e <- independence_test(as.table(barnard12), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it12_e), 4), 0.9167)) > stopifnot(isequal(round(midpvalue(it12_e), 4), 0.7083)) > > it13_e <- independence_test(as.table(barnard13), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it13_e), 4), 0.9762)) > stopifnot(isequal(round(midpvalue(it13_e), 4), 0.8571)) > > it14_e <- independence_test(as.table(barnard14), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it14_e), 4), 0.9960)) > stopifnot(isequal(round(midpvalue(it14_e), 4), 0.9464)) > > it15_e <- independence_test(as.table(barnard15), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it15_e), 4), 1.0000)) > stopifnot(isequal(round(midpvalue(it15_e), 4), 0.9881)) > > ## it00_e <- independence_test(as.table(barnard00), > ## distribution = "exact", > ## alternative = "greater") > ## stopifnot(isequal(round(pvalue(it00_e), 4), 1.0000)) > ## stopifnot(isequal(round(midpvalue(it00_e), 4), 0.5000)) > > it01_e <- independence_test(as.table(barnard01), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it01_e), 4), 1.0000)) > stopifnot(isequal(round(midpvalue(it01_e), 4), 0.7500)) > > it02_e <- independence_test(as.table(barnard02), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it02_e), 4), 1.0000)) > stopifnot(isequal(round(midpvalue(it02_e), 4), 0.8889)) > > it03_e <- independence_test(as.table(barnard03), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it03_e), 4), 1.0000)) > stopifnot(isequal(round(midpvalue(it03_e), 4), 0.9583)) > > it04_e <- independence_test(as.table(barnard04), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it04_e), 4), 1.0000)) > stopifnot(isequal(round(midpvalue(it04_e), 4), 0.9881)) > > it05_e <- independence_test(as.table(barnard05), + distribution = "exact", + alternative = "greater") > stopifnot(isequal(round(pvalue(it05_e), 4), 1.0000)) > stopifnot(isequal(round(midpvalue(it05_e), 4), 0.9980)) > > proc.time() user system elapsed 1.62 0.14 1.78
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