Блог

Вот сценарий, над которым я работал (несколько непрерывных переменных под рукой, категориальная группирующая переменная всего с 2 группами): прикрепить(myo.продолжение)

 # Define a Function for the t.test  my.t.test lt;- function(x) {  ## here gr_arrythmia is the grouping variable and myo.cont the dataframe of interest  test lt;- t.test(x ~ gr_arrythmia, data=myo.cont,  alternative='two.sided',  conf.level=0.95,  paired=FALSE,  na.rm=TRUE,   var.equal=TRUE)  res lt;- c(test$statistic, test$parameter, round(test$p.value, 3), round(p.adjust(test$p.value, "bonferroni"), 3))  names(res) lt;- c("t.value", "df", "p-value.t", "adjusted.p-Value.t")  res }  # Define a function for the Mann-Whithney,  my.u.test lt;- function(x) {    test lt;- wilcox.test(x ~ gr_arrythmia, data=myo.cont,  alternative='two.sided',  conf.level=0.95,  paired=FALSE,  na.rm=TRUE, )  res lt;- c(test$statistic, round(test$p.value, 3))  names(res) lt;- c("Wix", "p-value.u")  res   }  # Define A Function for the Brown-Mood's Median Test   require(coin) # install.packages("coin") if you have to  my.m.test lt;- function(x) {    test lt;- median_test(x ~ gr_arrythmia, data=myo.cont)  res lt;- c(round(pvalue(test), 3))  names(res) lt;- c("p-value.m")  res   }  TtestTable lt;- as.data.frame(t(apply(myo.cont[,-2], 2, my.t.test))) #dataframe with tTest analysis, transposed  # for conveniency # here gr_Arrythmia (the grouping variable) is the second variable / column, so  # [,-2] option in apply tell the program to ignore that variable  # while the second "2" telle apply to "apply" the function column by column in the whole  # dataframe  # "my.t.test" is the function, to be applied to the whole dataframe, column by column   # NOTE: If you have even 1 categorical variable in the dataframe the loop will break  # AND you will not know what is the culprit variable   UtestTable lt;- as.data.frame(t(apply(myo.cont[,-2], 2, my.u.test))) # Dataframe for the U Test, Transposed  MtestTable lt;- as.data.frame(apply(myo.cont[,-2], 2, my.m.test)) # Dataframe for the Mood's Test, NOT Transposed  Comparisons lt;- cbind(TtestTable, UtestTable, MtestTable)   write.csv(TtestTable, file="Comparisons.txt", row.names = TRUE) # Exported in working directory (get() to know it)  detach(myo.cont)   

Теперь приведенный выше код работает почти так, как ожидалось, единственная проблема заключается в следующей строке

 MtestTable lt;- as.data.frame(apply(myo.cont[,-2], 2, my.m.test)) # Dataframe for the Mood's Test, NOT Transposed  

По какой-то причине столбец создан правильно с ожидаемыми значениями p, но заголовок столбца, который должен быть «p-значение.m», заменен «применить(myo.cont[,-2], 2, мой.m.тест», и я понятия не имею, почему, я думаю, проблема с пакетом монет и созданным объектом класса S4.

После фрагмента ввода данных myo.cont в случае, если вы хотите воспроизвести проблему.

 dput(myo.cont) structure(list(pid = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,  13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,  29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43),   gr_arrythmia = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,   1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,   1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,   1L, 1L, 1L, 2L, 1L), .Label = c("0", "1"), class = "factor"),   age = c(42, 45, 19, 40, 19, 58, 17, 25, 15, 68, 32, 24, 50,   41, 40, 43, 21, 20, 37, 20, 23, 40, 27, 57, 22, 34, 17, 26,   40, 41, 23, 28, 20, 32, 53, 1, 18, 48, 27, 22, 31, 17, 31  ), bmi = c(27.68, 26, 26.2, 27.2, 26.15, NA, NA, 29, 24,   NA, 28, 27.8, 23, NA, NA, 29.5, NA, 23, 30.1, 28, 19, 26,   24, NA, 23, 24, 21, 20, 31, 25, 24.5, 24, 23.8, 30.5, 31,   23, 22.8, 24, 23.67, 30.3, 23, 16.33, 24), bsa = c(1.94,   2.065, 2.014, 2.13, 1.99, 2, NA, 2, 1.8, NA, 1.9, 2.1, 1.7,   NA, NA, 2.4, NA, 1.86, 1.99, 2.1, 1.75, 1.9, 1.7, NA, 1.87,   1.87, NA, 1.6, 2, 2, 1.9, 2, 1.6, 2.38, 2.2, 1.9, 1.8, 1.8,   1.9, 1.78, 1.7, 1.6, 1.5), cmr_deds = c(6.5, 9.8, 4, 6.5,   4.8, 4.8, NA, NA, 7.7, 24.5, 6.5, 1.5, NA, 2.1, NA, 1.6,   1.5, 2.5, 1.4, 1.4, 1.9, 1.7, 5.6, 2.2, 1.3, 2.3, 1.8, 2.9,   3.2, 8.2, 4.2, 11.2, 5.4, 4.9, 9.2, 3.9, 7.6, 3.3, 11.8,   7.1, 5.8, 8.3, 13.4), cmr_demass_pc = c(15.3960396039604,   6.91752577319588, 18.6725663716814, 11.5107913669065, 4.62962962962963,   24.4444444444444, 0, 0, 17.7049180327869, 15.0342465753425,   11.4646464646465, 36.3565891472868, 0, 12.775, 0, 9.6304347826087,   6.14285714285714, 3.890625, 22.9916666666667, 35.2539682539683,   9.41284403669725, 4.46212121212121, 33.5287356321839, 1.28125,   13.4525547445255, 6.86821705426357, 14.3939393939394, 6.93,   3.30769230769231, 12.4342105263158, 8.21374045801527, 8.91891891891892,   4.66292134831461, 3.83673469387755, 3.67, 20.0324675324675,   4.71428571428571, 10.7777777777778, 18.49, 0.51, 11.3048780487805,   21.0344827586207, 23.77), cmr_demass = c(15.55, 6.71, 21.1,   16, 6.25, 22, NA, NA, 10.8, 21.95, 11.35, 46.9, NA, 15.33,   NA, 13.29, 8.17, 7.47, 27.59, 44.42, 10.26, 5.89, 29.17,   1.23, 18.43, 8.86, 19, 6.93, 4.3, 18.9, 10.76, 9.9, 4.15,   5.64, 4.52, 30.85, 6.27, 9.7, 19.6, 0.6, 9.27, 18.3, 21.63  ), cmr_demean = c(14.7, 12.4, 22.6, 17.4, 13, 38, NA, NA,   14.4, 55, 15, 5.1, NA, 6.3, NA, 7.2, 7.5, 11.6, 8.9, 7.4,   11, 10.3, 8.7, 10.8, 7.1, 11.4, 9.7, 16.5, 20, 23.8, 11.2,   36.4, 21.9, 14.7, 36.5, 10.6, 23.1, 26.2, 34.8, 36.4, 53,   28.6, 47.9), cmr_edemass_pc = c(30.1683168316832, 20, 6.63716814159292,   15.2014388489209, 2.8962962962963, 5.14444444444444, 18.0246913580247,   12.496062992126, 21.4754098360656, 12.9931506849315, 5.43434343434343,   41.2945736434109, 3.97222222222222, 10.9, 17.0324675324675,   37.1594202898551, 11.8045112781955, 10.375, 31.7083333333333,   51.1349206349206, 13.9816513761468, 6.17424242424242, 34.1609195402299,   3.95833333333333, 11.8613138686131, 25.1317829457364, 16.6212121212121,   8.06, 14.7076923076923, NA, 16.83, 2.07207207207207, 0, 7.41496598639456,   NA, 25.2597402597403, 0, 24.7, 8.4, 2.29, 0, 0, 15.2197802197802  ), cmr_edemass = c(30.47, 19.4, 7.5, 21.13, 3.91, 4.63, 14.6,   15.87, 13.1, 18.97, 5.38, 53.27, 4.29, 13.08, 26.23, 51.28,   15.7, 19.92, 38.05, 64.43, 15.24, 8.15, 29.72, 3.8, 16.25,   32.42, 21.94, 8.06, 19.12, NA, 22.06, 2.3, 0, 10.9, NA, 38.9,   0, 22.23, 9.08, 2.7, 0, 0, 13.85), cmr_lvedv = c(115, 107,   167, 126, 111, 126, 133, 130, 96, 106, 90, 210, 117, 183,   177, 163, NA, 203, 139, 204, 137, 182, NA, 118, 183, NA,   182, 174, 185, 133, 113, 156, 120, 211, 165, 186, 168, 88,   104, 119, 156, NA, 160), cmr_lvedvi = c(59, 51, 82, 59, 55,   62, 73, 61, 54, 58, 46, 100, 71, 88, 76, 69, NA, 114, 66,   96, 77, 96, NA, 63, 97, NA, 96, 104, 89, 67, 58, 76, 74,   93, 74, 95, 91, 48, 53, 54, 90, 99, 97), cmr_lvef = c(76,   69, 62, 66, 61, 77, 61, 60, 65, 50, 66, 43, 37, 62, 70, 61,   NA, 49, 53, 61, 72, 56, NA, 65, 58, NA, 56, 53, 53, 54, 68,   73, 69, 56, 61, 55, 62, 69, 70, 60, 55, 53, 43), cmr_lvesv = c(27,   33, 63, 42, 43, 51, 51, 51, 33, 52, 30, 119, NA, 68, 53,   63, NA, 104, 64, 79, 38, 79, NA, 40, 76, NA, 79, 81, 86,   61, 36, 40, 37, 93, 65, 71, 65, 27, 31, 47, 71, NA, 91),   cmr_lvesvi = c(13, 16, 31, 19, 21, 51, 28, 24, 18, 28, 15,   57, NA, 32, 23, 26, NA, 54, 30, 37, NA, 41, NA, 22, 40, NA,   41, 48, 41, 31, 18, 20, 23, 66, 28, 36, 35, 14, 15, 21, 40.8,   46, 55), cmr_lvmass = c(101, 97, 113, 139, 135, 90, 81, 127,   61, 146, 99, 129, 108, 120, 154, 138, 133, 192, 120, 126,   109, 132, 87, 96, 137, 129, 132, 100, 130, 152, 131, 111,   89, 147, 123, 154, 133, 90, 106, 118, 82, 87, 91), cmr_lvmassi = c(51,   47, 56, 65, 67, 44, 44, 59, 34, 79, 51, NA, 66, 58, 65, NA,   70, 108, 57, 59, 61, 70, NA, 51, 73, 69, 70, 60, 62, 77,   68, 54, 55, 66, 54, 79, 72, 49, 54, 54, 47, 54, 55), cmr_strokevol = c(88,   74, 104, 84, 68, 75, 82, 79, 63, 54, 60, 90, 71, 115, 124,   99, NA, 99, 75, 125, 99, 103, NA, 77, 107, NA, 103, 93, 99,   72, 77, 115, 83, 118, 103, 109, 103, 61, 73, 72, 85, 82,   69), de_10minf = c(18.82, 23.73, 20.15, 0, 0, 65.29, 0, 0,   0, 0, 12.35, 0, 0, 38.82, 0, 0, 0, 0, 13, 41.76, 8.23, 6.47,   51.18, 0, 16, 8, 11.41, 2.645, 1.177, 12.4975, 1.88, 14.12,   9.41, 5.69, 26.47, 19.55, 0, 1.37, 11.77, 0, 3.54, 47, 45.88  ), de_11milat = c(22, 0, 41.32, 0, 0, 59.02, 0, 0, 0, 27.64,   0, 0, 0, 18.52, 0, 23.92, 23.92, 0, 0, 25.88, 14.31, 8.5,   74.3, 15.88, 10, 6, 19.3, 0, 14.11, 2.795, 8.83, 11.58, 9.02,   3.26, 0.59, 38.53, 1.47, 1.57, 26.08, 0.295, 11.18, 18.63,   19.02), de_12malat = c(24, 0, 24.79, 0, 0, 0, 0, 0, 0, 28,   0, 0, 0, 23.43, 0, 14.16, 12.7, 0, 0, 34.06, 9.8, 0, 31.45,   0, 0, 0, 6.12, 0.31, 6.25, 0.9375, 7.88, 8.96, 0.83, 2.5,   2.81, 30.937, 0, 3.57, 14.06, 0.155, 5.32, 41.25, 38.96),   de_13aant = c(13.23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,   0, 0, 0, 0, 0, 58.2, 45.29, 0, 0, 68.23, 0, 0, 7.5, 20.24,   0.59, 0, 0.586666666666667, 4.12, 0.39, 3.92, 0, 0, 60.19,   0, 0, 42.16, 0, 5.88, 0, 7.65), de_14aspt = c(15, 0, 0, 0,   0, 38.25, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 4, 39.4, 14, 0,   0, 68.8, 0, 0, 0, 0, 7.17666666666667, 8.3, 0.856666666666667,   14.52, 1.39, 8.5, 0.21, 0, 10.255, 0, 0, 33.15, 0, 8.36,   1.15, 20.64), de_15ainf = c(13.23, 1.18, 0, 0, 0, 89.11,   0, 0, 0, 0, 25.88, 0, 0, 0, 0, 0, 0, 0, 0, 60, 0, 0, 72,   0, 0, 0, 0, 14.7033333333333, 2.06, 8.23666666666667, 29.02,   7.25, 7.45, 1.96, 0, 49.02, 0, 0, 31.57, 0, 1.37, 5.88, 36.08  ), de_16alat = c(0, 0, 0, 29.45, 0, 90.88, 0, 0, 0, 0, 0,   0, 0, 31.58, 0, 0, 11.34, 0, 30.14, 44.43, 18, 0, 88.61,   0, 0, 13, 29.6, 2.035, 5.92, 1.17666666666667, 49.57, 11.21,   9.5, 3.67, 0, 57.92, 0, 0, 24.18, 0.686666666666667, 3.73,   0.81, 19.41), de_1bant = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,   0, 59.12, 0, 0, 0, 0, 0, NA, 0, 25.8, 0, 0, 27.06, 0, 0,   0, 0, 11.1766666666667, 0.59, 10.5875, 3.73, 6.18, 0, 4.12,   0, 13.33, 30, 13.97, 7.35, 2.94, 42.36, 47.06, 37.65), de_2baspt = c(11.76,   0, 33.53, 0, 0, 0, 0, 0, 0, 0, 0, 80.88, 0, 0, 0, 12.09,   0, 0, 96.17, 48.23, 0, 0, 0, 0, 0, 0, 0, 18.04, 1.765, 30.5875,   11.18, 15.89, 0, 10.59, 27.94, 35.61, 5.88, 58.97, 6.32,   0.295, 32.36, 22.94, 30.89), de_3bispt = c(0, 0, 0, 0, 0,   0, 0, 0, 0, 0, 23.96, 48.12, 0, 0, 0, 0, 0, 0, 58, 48.7,   0, 0, 0, 0, 0, 0, 0, 8.54, 7.19, 20.935, 6.06, 26.25, 0,   5.62, 30.94, 4.7933, 6.46, 34.52, 25.41, 0, 5.94, 25, 42.82  ), de_4binf = c(10.59, 8.82, 26.17, 41.47, 30.98, 0, 0, 0,   13.92, 48.23, 19.8, 30.5, 0, 58.23, 0, 0, 17.65, 0, 0, 56.27,   36.6, 5, 40.88, 0, 31.12, 13, 29.1, 5.95, 2.06, 25.735, 4.71,   22.65, 0, 10.39, 58.235, 19.61, 3.37, 10.58, 20, 0.885, 23.53,   39.12, 70), de_5bilat = c(0, 0, 0, 0, 0, 0, 0, 0, 17.06,   89.41, 23.14, 57.35, 0, 31.17, 0, 16.86, 35.29, NA, 0, 35.49,   12.15, 4, 53.23, 0, 34.71, 10, 8.23, 18.6266666666667, 3.53,   31.615, 16.86, 46.47, 0, 11.17, 0, 41.96, 1.37, 28.67, 37.79,   1.765, 40.89, 46.47, 44.41), de_6balat = c(0, 0, 35, 18.12,   0, 0, 0, 0, 0, 57.49, 0, 49, 0, 41.25, 0, 17.08, 0, 0, 0,   0, 0, 0, 35, 0, 18.12, 0, 0, 2.91666666666667, 0.94, 10.935,   8.13, 10.63, 0, 3.33, 7.81, 36.43, 10.84, 30.47, 17.35, 0,   9.69, 52.81, 8.75), de_7mant = c(0, 0, 0, 0, 0, 0, 0, 0,   0, 0, 0, 0, 0, 0, 0, 0, 16.27, 0, 0, 45.59, 0, 0, 24.41,   0, 0, 0, 0, 0.88, 1.373, 13.09, 2, 0, 0.98, 0, 0, 39.85,   24.41, 0, 8.04, 0, 17.06, 50.24, 20.59), de_8maspt = c(0,   0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9.7, 58,   38.53, 0, 0, 0, 0, 0, 0, 0, 16.47, 0, 17.06, 2.82, 3.73,   0.59, 0.59, 0.88, 2.05, 12.06, 0, 4.22, 0.1475, 3.24, 33.53,   44.12), de_9mispt = c(0, 0, 0, 0, 0, 51.04, 0, 0, 0, 0, 0,   0, 0, 0, 0, 0, 0, 14.37, 62.5, 39, 0, 0, 0, 0, 25.21, 0,   0, 2.185, 1.46, 9.53, 0.87, 5.42, 7.92, 0.62, 2.19, 3.595,   0, 0, 5, 0, 3.91, 25.63, 46.46), echo_aalat = c(0.1, 0.09,   0.05, 0.08, 0.05, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,   NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.06, 0.07, 0.1,   0.08, 0.12, 0.1, 0.0555555555555554, 0.0624999999999998,   0.0694444444444442, 0.0624999999999998, 0.12, 0.13, 0.08,   0.1, 0.1, 0.05, 0.07), echo_aasept = c(0.11, 0.1, 0.07, 0.08,   0.06, NA, NA, NA, NA, NA, NA, NA, NA, 0.08, NA, NA, NA, NA,   NA, NA, NA, NA, NA, NA, NA, NA, 0.07, 0.07, 0.1, 0.0416666666666665,   0.12, 0.1, 0.0763888888888886, 0.1, 0.0624999999999998, 0.0555555555555554,   0.11, 0.1, 0.11, 0.06, 0.11, 0.05, 0.09), echo_asept = c(8,   8, 9, 10, 10, 11, NA, 9, NA, 11, NA, 9, 10, NA, NA, 13, NA,   11, 12, 9, 9, 13, 10, 12, 10, 11, 9, 9, 10, 12, 9, 9, 10,   10, 12, 12, 7, 9, 11, 9, 8, 8, 8), echo_avel = c(0.57, 0.55,   0.52, 0.41, 0.69, 0.85, NA, NA, NA, NA, 0.34, 0.48, NA, 0.43,   0.85, 0.43, NA, 0.29, 0.53, NA, 0.41, 0.53, 0.64, 0.8, 0.37,   0.49, 0.33, 0.67, 0.52, 0.41, 0.74, 0.395833333333333, 0.361111111111111,   0.62, 0.444444444444444, 0.0465277777777777, 0.57, 0.86,   0.68, 0.45, 0.57, 0.45, 0.71), echo_ealat = c(0.18, 0.17,   0.17, 0.13, 0.2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,   NA, NA, NA, NA, NA, 0.15, 0.12, 0.13, 0.09, 0.14, 0.14, 0.18,   0.18, 0.14, 0.0624999999999998, 0.16, 0.0902777777777775,   0.131944444444444, 0.0902777777777775, 0.0486111111111109,   0.118055555555555, 0.18, 0.13, 0.16, 0.25, 0.14, 0.11, 0.1  ), echo_easept = c(0.13, 0.07, 0.13, 0.12, 0.13, NA, NA,   NA, NA, NA, NA, NA, NA, 0.11, NA, NA, NA, NA, 0.06, NA, 0.11,   0.07, 0.08, 0.07, 0.13, 0.12, 0.14, 0.14, 0.09, 0.1, 0.15,   0.1, 0.1, 0.0763888888888886, 0.0277777777777777, 0.15, 0.15,   0.1, 0.14, 0.12, 0.13, 0.08, 0.0486111111111111), echo_edect = c(163,   141, 182, 153, 180, 280, NA, 190, NA, NA, 200, 128, NA, NA,   100, 200, NA, 196, 200, NA, 173, NA, 188, 185, 170, 168,   140, 150, 178, 206, 135, 133, 188, 160, 215, 102, 170, 223,   163, 177, 157, 216, 147), echo_eemed = c(0.31, 0.12, 0.3,   0.25, 0.33, 0.07, NA, 0.11, 0.1, NA, 0.11, NA, NA, NA, NA,   NA, NA, NA, NA, NA, 0.13, 0.1, 0.1, 0.08, 0.14, 0.13, 0.32,   0.16, 0.12, 0.09, 0.15, 0.083333333333333, 0.104166666666666,   0.083333333333333, 0.0347222222222221, 0.111111111111111,   0.16, 0.11, 0.15, 0.19, 0.13, 0.15, 0.09), echo_evel = c(1.13,   0.83, 0.85, 0.87, 1.29, 0.66, NA, 0.9, 0.6, NA, 0.7, 0.97,   NA, 0.88, 0.57, 0.63, NA, 0.96, 0.5, NA, 0.86, 0.44, 0.85,   0.7, 0.89, 0.76, 0.76, 0.77, 0.87, 0.57, 1.12, 0.368055555555555,   0.645833333333333, 0.8, 0.4, 0.680555555555555, 1.06, 0.86,   0.87, 0.96, 0.95, 0.58, 0.77), echo_laarea = c(17, 17, 16,   21, 15, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,   NA, NA, 15, 19, 17, 18, 19, 16, 18, 15, 19, 17, 19, 17, 17,   20, 17, 15, 19.2, 20, 17, 15, 17, 15, 15), echo_lavol = c(48,   48, 42, 60, 43, 65, NA, NA, NA, 70, NA, 60, 90, NA, 95, 85,   NA, NA, NA, NA, 35, 60, 46, 50, 54, 38, 48, 38, 54, 45, 58,   47, 47, 62, 52, 40, 57, 65, 52, 41, 44, 40, 40), echo_lvedv = c(95,   106, 110, 98, 89, 90, NA, NA, NA, 110, 90, 120, NA, NA, 135,   125, NA, 159, 95, 115, 88, 114, 107, 91, 109, 92, 95, 57,   125, 75, 125, 104, 65, 121, 117, 114, 116, 70, 85, 145, 80,   80, 100), echo_lvef = c(60, 60, 60, 58, 60, 65, NA, 50, 52,   49, 65, 38, 37, 60, 53, 65, NA, 50, 50, 58, 62, 60, 58, 60,   58, 58, 63, 62, 55, 60, 65, 70, 65, 65, 59, 65, 57, 65, 65,   65, 62, 60, 49), echo_post = c(8, 8, 9, NA, 8, 10, NA, 9,   NA, 9, NA, 8, NA, NA, NA, 11, NA, 11, 9, 7, 8, 8, 8, 10,   9, 7, 8, 7, 10, 10, 9, 9, 8, 10, 10, 12, 7, 9, 9, 9, 7, 7,   8), ed_10minf = c(35.29, 70.19, 0, 52.16, 0, 17.94, 17, 0,   12.35, 0, 0, 0, 0, 11, 48.43, 43.33, 0, 18.82, 0, 96.47,   0, 0, 44.41, 20, 0, 61, 23.72, 2.35, 20.44, NA, 11.65, 0,   0, 26.86, NA, 1.03, 0, 12.49, 5.295, 0.783333333333333, 0,   0, 29.26), ed_11milat = c(56.03, 28.23, 40, 32.94, 0, 16.47,   0, 0, 32.16, 43.72, 0, 0, 0, 14, 28.23, 83.53, 33.89, 10.44,   0, 54, 53.82, 3.53, 65, 30.3, 0, 20, 23.92, 1.765, 18.825,   NA, 16, 0, 0, 11.76, NA, 2.8, 0, 23.09, 10.735, 0, 0, 0,   21.77), ed_12malat = c(58.12, 0, 0, 0, 0, 0, 0, 10.12, 25.8,   15.4, 0, 0, 18.12, 18.75, 0, 57.5, 26.24, 0, 0, 0, 0, 0,   40, 0, 0, 0, 25.62, 0, 13.125, NA, 15.88, 0, 0, 10.41, NA,   49.53, 0, 54.37, 2.185, 0, 0, 0, 23.44), ed_13aant = c(0,   0, 0, 0, 0, 0, 0, 0, 36.9, 0, 0, 27.64, 0, 0, 0, 0, 0, 0,   95, 0, 52.35, 0, 81.57, 0, 0, 32, 0, 0, 97.45, NA, 38.04,   13.83, 0, 0, NA, 99.02, 0, 0.295, 53.7266666666667, 0, 0,   0, 10.59), ed_14aspt = c(0, 35.75, 0, 72.25, 0, 23.09, 0,   0, 34, 0, 0, 20, 0, 0, 0, 0, 0, 0, 71.61, 90, 54.34, 0, 49.37,   0, 0, 0, 0, 0, 100, NA, 42.3, 33.64, 0, 0, NA, 10.11, 0,   18.005, 32.03, 15.2775, 0, 0, 35.77), ed_15ainf = c(55.58,   36, 0, 58.8, 0, 82.94, 18.37, 0, 0, 0, 27.45, 0, 0, 7.65,   0, 50, 10.6, 0, 0, 84.41, 0, 0, 95, 0, 0, 0, 0, 0, 55.69,   NA, 18.63, 35.3, 0, 0, NA, 5.69, 0, NA, 19.41, 5.29, 0, 0,   51.18), ed_16alat = c(46.9, 38.23, 0, 50, 0, 61.76, 0, 39.58,   0, 6.25, 20.33, 0, 0, 33.51, 0, 37.44, 10.3, 0, 60, 56.72,   29.79, 0, 87.39, 0, 0, 44.5, 0, 0, 10.23, NA, 38.63, 16.76,   0, 0, NA, 41.225, 0, NA, 16.8616666666667, 2.3525, 0, 0,   30), ed_1bant = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 80.59,   0, 0, 0, 0, 0, 11.76, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17.45, 26.175,   NA, 17.45, 0, 0, 19.8, NA, 49.12, 0, 27.27, 9.41, 0, 0, 0,   26.47), ed_2baspt = c(0, 25, 0, 0, 0, 0, 0, 0, 0, 0, 5, 76.18,   0, 0, 0, 0, 0, 0, 73.53, 76, 0, 0, 0, 0, 0, 0, 0, 20.98,   61.03, NA, 14.9, 0, 0, 21.37, NA, 43.08, 0, 28.23, 3.53,   0, 0, 0, 1.96), ed_3bispt = c(0, 8.12, 0, 0, 0, 0, 0, 0,   0, 0, 0, 50.94, 0, 0, 0, 0, 0, 0, 69.38, 89, 0, 13.62, 0,   0, 48, 0, 48.12, 14.1666666666667, 61.41, NA, 1.46, 0, 0,   17.5, NA, 28.81, 0, 29.79, 0, 2.5, NA, 0, 8.96), ed_4binf = c(20.19,   0, 0, 60.29, 32.74, 0, 28.53, 19, 38.23, 31.76, 13.23, 0,   0, 19.75, 47.45, 45.49, 21.76, 51.76, 0, 93.23, 33.72, 0,   0, 0, 55, 76, 73.23, 1.76666666666667, 19.71, NA, 0.78, 0,   0, 39.21, NA, 1.18, 0, 0.98, 5, 2.355, 0, 0, 19.8), ed_5bilat = c(39.4,   0, 0, 49.71, 21.96, 0, 32.94, 33.72, 58.12, 74.94, 13.23,   0, 0, 28.82, 40.19, 83.53, 39.21, 26.47, 0, 48, 21, 0, 14.41,   0, 29, 43.3, 0, 29.8033333333333, 1.03, NA, 0.2, 0, 0, 26.67,   NA, 2.5, 0, 10.39, 5.59, 9.41, 0, 0, 31.57), ed_6balat = c(49.69,   0, 0, 10, 0, 0, 0, 0, 0, 45.41, 0, 53.12, 0, 12.2, 0, 57.5,   3, 0, 0, 0, 0, 0, 11.87, 0, 19, 0, 0, 10.6233333333333, 0.31,   NA, 0.21, 0, 0, 26.04, NA, 17.5, 0, 27.91, 0.31, 0, 0, 0,   15.42), ed_7mant = c(68.2, 0, 0, 0, 0, 0, 0, 0, 11.11, 0,   0, 90.1, 0, 0, 0, 0, 0, 0, NA, 41.7, 0, 0, 0, 0, 0, 0, 0,   5.295, 77.5, NA, 49.06, 0, 0, 6.27, NA, 94.42, 0, 57.2, 10.59,   0.196666666666667, 0, 0, 16.03), ed_8maspt = c(0, 57.35,   0, 0, 0, 0, 0, 0, 0, 13.33, 0, 82.94, 0, 0, 0, 0, 0, 11.61,   81.17, 96, 37.5, 0, 0, 0, 4.12, 0, 0, 22.06, 100, NA, 56,   0, 0, 5.49, NA, 57.65, 0, 54.85, 23.09, 5.29333333333333,   0, 0, 15), ed_9mispt = c(0, 0, 0, 42.71, 0, 0, 0, 0, 0, 0,   0, 44.79, 0, 0, 0, 0, 0, 22.34, 73.54, 94, 50.15, 16.46,   0, 0, 21, 0, 0, 35.94, 95.16, NA, 36.63, 0, 0, 4.58, NA,   22.18, 0, 46.72, 4.22, 10.6266666666667, 0, 0, 22.66), gls_eng = c(-18.5755555555556,   -15.71, -19.665, -20.0811111111111, -21.9578888888889, -18.1616666666667,   NA, NA, NA, NA, -19.965, -16.985, -13.9477777777778, -18.67,   -21.9777777777778, -22.1927777777778, -19.0308333333333,   -20.4211111111111, -15.775, NA, -18.7611111111111, -18.5455555555556,   -19.215, NA, -19.3366666666667, -21.1238888888889, -21.85,   -17.6694444444444, -17.2883333333333, -16.2805555555556,   -20.49, -16.49, -21.855, -22.2388888888889, NA, -19.295,   -23.1088888888889, -23.0561111111111, -20.8538888888889,   -21.6933333333333, -19.03, -18.4083333333333, -18.1138888888889  ), gls_epg = c(-14.6172222222222, -12.82, -16.67, -16.3127777777778,   -19.2451111111111, -14.8566666666667, NA, NA, NA, NA, -16.77,   -14.2055555555556, -11.7277777777778, -14.8544444444444,   -17.5972222222222, -19.4655555555556, -14.2008333333333,   -16.5311111111111, -14.5633333333333, NA, -14.9911111111111,   -15.2088888888889, -14.8833333333333, NA, -16.2894444444444,   -18.0616666666667, -16.2138888888889, -14.5733333333333,   -14.8372222222222, -12.045, -16.2166666666667, -13.0891666666667,   -16.9108333333333, -18.4455555555556, NA, -17.1444444444444,   -19.2238888888889, -18.45, -17.7694444444444, -18.1016666666667,   -14.2, -13.65, -14.8533333333333), gls_mg = c(-16.3827777777778,   -14.556, -18.5672222222222, -18.2722222222222, -21.0488888888889,   -16.7188888888889, NA, NA, NA, NA, -19.2972222222222, -15.958,   -13.3666666666667, -16.9172222222222, -20.0883333333333,   -21.6211111111111, -16.3191666666667, -19.13, -15.3377777777778,   NA, -17.3105555555556, -17.1533333333333, -17.5094444444444,   NA, -19.135, -17.3933333333333, -18.5644444444445, -16.6833333333333,   -16.6488888888889, -14.82, -18.1016666666667, -14.5141666666667,   -19.1308333333333, -20.8972222222222, NA, -18.9222222222222,   -21.8922222222222, -21.515, -20.0322222222222, -20.5566666666667,   -16.3191666666667, -15.6683333333333, -16.9472222222222),   glseng_2c = c(-19.0966666666667, -16.8222222222222, -20.6138888888889,   -20.8427777777778, -23.3438888888889, -19.3761111111111,   NA, NA, NA, NA, -22.0877777777778, -18.1972222222222, -15.4922222222222,   -19.6838888888889, -23.6444444444444, -24.4461111111111,   NA, -22.4844444444444, -16.5688888888889, NA, -20.5138888888889,   -19.8655555555556, -20.9505555555556, NA, -21.0172222222222,   -22.6288888888889, -21.8461111111111, -19.6283333333333,   -19.2, -18.4088888888889, -25.3683333333333, -26.3444444444444,   -25.5066666666667, -24.2161111111111, NA, -21.3722222222222,   -25.3933333333333, -25.3672222222222, -23.1533333333333,   -23.6388888888889, -22.6711111111111, NA, -19.6311111111111  ), row.names = c(NA, 43L), class = "data.frame")