融合数据框中每个子组的单向方差分析 [英] One-way ANOVA for each sub-group in a melted data frame
本文介绍了融合数据框中每个子组的单向方差分析的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有一个非常大的数据集，需要针对 4 种不同处理的多个物种进行单独的单向方差分析，并进行多次测量.通常我只是为每个物种制作单独的 excel 电子表格，并在每个物种上运行方差分析，循环遍历每个测量列，但这非常耗时.是否可以使用单个电子表格并对融化的数据运行方差分析?或者也许我可以使用另一个重塑选项?
数据:
structure(list(Species = c("A", "A", "A", "A", "A", "A", "A",A"、A"、A"、B"、B"、B"、B"、B"、B"、B"、B"、B"、B""，B"、C"、C"、C"、C"、C"、C"、C"、C"、C"、C"、C"、D""，"D", "D", "D", "D", "D", "D", "D", "D", "D"), 治疗 = c(1,1, 1, 2, 2, 2, 3, 3, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 1,1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4),`CLASS 1` = c(9.3993303987076, 8.14588087483834, 8.44889021858093,28.2773809415175, 9.49156649568952, 37.406663111623, 8.42458221212166,35.529904738035、33.1401135085753、8.26977630375797、7.87786527094827、7.83020300515061、35.1465417538538、10.5560853720815、7.54702433773332、7.15030081390987、7.73624654623485、33.6461639540039、10.3098164094602、7.79017325570062, 8.47473750173462, 8.37179798600773, 31.7364310355766,2.00147496567679, 9.30194886619568, 7.87886829173726, 7.93445694220837,9.10020522660375, 8.81542855137005, 7.83313314713951, 7.84449591023115,23.6150030864875、9.3452854347794、8.91047098149179、7.76031738257155、9.79467065201063, 24.7592334362831, 8.54842834366722, 7.60436112798701,8.93480758329653, 8.72406315335014, 9.49850179222777), `CLASS 2` = c(10.8069912074565,4.52426389123869, 8.13120921128287, 52.3870196313339, 7.17369219206721,45.7325224336886, 17.8345921677786, 38.4579761235057, 37.5916934855387,6.28803058195647、3.72788988807285、4.64744990904241、29.7689968962103、7.08515103144071、6.44277647222835、5.71017728280462、6.28290843412007、45.6123170472575、6.98431855663527、8.03809625184267、4.76656440828616、6.74640254081232, 31.3243238213156, 45.1287867136276, 7.6308508343969,4.0127554151831, 9.11910102221636, 6.1658394708941, 10.4617259648364,6.07502685224869, 8.08373642262043, 48.588633863193, 10.2160085507338,7.52606530219909、5.66373884014351、8.51992766801391、25.9109062123364、5.74498954209992, 5.56377323143979, 7.76698847227212, 7.05016373786876,7.99745310894107), `CLASS 3` = c(3.96856956332584, 2.76052305637364,6.92715392916015, 0.687821057043984, 2.30154255462355, 2.61089063893911,11.2199145273738, 10.7058533354417, 1.90691767773411, 3.93488282297868,1.7034110083142、1.69310511636903、1.54005861925764、50.436990190291、3.93233520754151、4.06684782901502、6.10592204678281、0.675086986967025、3.94018776658881、5.74129993338595、2.02845185559621、4.10963382465756、14.9264019576272、12.9672579626868、5.1049208042632、1.37282635713804、3.00088572108344, 4.78878116348504, 4.79564218319094, 3.03836532949481,3.48474205480686、1.09218910757234、6.2830307568812、3.06784943090836、4.89376208853059、6.6321148581705、1.01356027363186、3.15439940439419、40.8141653079423、2.52825000616702、1.65382018138259、1.81173455682492), `CLASS 4` = c(0, 13.4274810838142, 10.9876140536356, 3.15424686759082,15.2632739415738, 0, 0, 2.39525969535064, 4.19386122886851,13.15599261724, 14.5421891905919, 14.4542067660843, 0, 0,12.2276086827261、12.7527880016103、12.1436697242409、3.79216208516423、0, 12.2283190622827, 16.0271803699645, 14.035876401479, 4.24556176551009,0, 0, 14.4993393432366, 13.6722412691012, 0, 14.0027443968931,13.7579074961889, 12.9935353616471, 4.66128854387559, 0,15.1941922851023, 11.6990009190362, 0, 7.99399142573694,14.5041748372822, 0, 15.0674109079436, 13.7134908002476,15.4194201146961), `CLASS 5` = c(7.82638584740367, 6.56112678542475,6.95253086439919、2.06445951884762、7.17086660532553、2.58627258328855、7.83400556063298、1.77053879587063、2.65292759651742、6.94701807830366、6.85309102458439、6.71505104532983、4.06818278652755、5.79906266122279、6.62064468061089、6.88365856613044、7.68403751285005、2.38479005191691、9.07405520739349, 7.65785587918449, 7.4385885335047, 7.30144390122309,2.41680929257195、4.18258704279641、7.8906816661241、6.75678558060943、6.67150537517493, 8.24794113296791, 7.67443442992891, 6.89357008866252,6.45444668132533、2.98342694785768、8.704729108357、7.14382850099481、7.15233553294014, 9.14001781571836, 3.98831954045444, 7.54093786042356,5.79029360470226、6.82793163574773、6.48049736162586、7.18554914992982), `CLASS 6` = c(20.6189597026452, 22.8728557858066, 23.0767150659042,4.99832103176657、24.1726463550235、5.56104550736533、31.8124013284184、4.31653191057476, 8.4695331411828, 20.63468068931, 19.7369752322083,19.6902616040991, 11.6648564225744, 0, 25.2321582223958,22.2981543181678, 29.3198455372777, 5.88723409877159, 30.1474816315191,28.0835788057802, 24.0430626320328, 25.1446564854412, 5.78713327050339,30.6155806819949, 23.8853696442419, 20.1783872969561, 17.5935515655693,23.4169038776536, 21.1986239116884, 19.6931330316831, 22.2658181144794,7.38944654414811, 32.1897387187698, 23.6398829158785, 25.3561697324352,30.4118856020653、12.6822088903071、26.300118251779、11.7338836812169、23.8624555097246, 20.1037712460599, 21.8478004507985), `CLASS 7` = c(15.9129851563051,15.2250454288061、13.5577123002506、2.9902563940573、15.4408266617369、2.67511425705514, 8.17164465017573, 2.23047357314211, 4.01010767344732,13.4046459481448, 15.3008244637288, 15.3885729336047, 7.81496654756214,17.8194559247092, 12.7823202355514, 13.7684066964868, 15.378473991847,3.75026919344972、18.2880822635935、14.7412162942703、17.5270089738067、16.799718650752, 4.33839497916674, 2.21937177530762, 15.0315149187176,15.3632530721031、14.1580725482114、16.4215442147509、15.5113323256627、14.3349000132624、12.8504657216928、5.06281347160092、15.5075336560533、15.9392345541138, 13.3981839319596, 16.6700105346756, 8.10398633871805,15.958090408468、16.5733149488757、15.1802203155931、14.2236219296677、16.2095182295187), `CLASS 8` = c(19.9174685533413, 16.6755018156139,13.9892072522183、3.35339208579287、18.98558519396、3.42749146804023、10.4801793890691、2.97802997775506、5.11270635117451、17.0372757040089、18.7865491767228, 18.3992789502607, 9.99639697401416, 0,15.9270550696003、17.1615519869107、15.3488962066467、4.25197658246908、21.2560581648095、15.7194605175531、19.6944057250743、17.4904702096271、5.22494387772846, 2.88494085790995, 19.1038328534942, 19.0183655117756,17.533290326259、19.92632149392、17.5400682364295、17.664926273487、16.3075864395099, 6.6071984352649, 17.7536737744256, 18.5784760293114,14.706720581834, 18.8313728693457, 9.73353207739478, 18.2488613518859,8.53356517614357、19.8319355692553、17.4801581342745、20.0300225970631), `CLASS 9` = c(11.5493095708147, 9.80732127808386, 7.92896710456816,2.08710247204941, 0, 0, 4.22268016442976, 1.61543185032431,2.92213933696131、10.3276972542995、11.4712047448286、11.1818706700593、0, 8.30325482025479, 9.28807709161222, 10.2081145049644,0, 0, 0, 0, 0, 0, 0, 0, 12.0508804125665, 10.9194191312608,10.316895230176, 11.9324634197247, 0, 10.709037767833, 9.7151732936871,0, 0, 0, 9.36977099054923, 0, 5.81426180513736, 0, 3.38664292169246,0, 10.5704134555229, 0), `CLASS 10` = c(44.7938508721352,51.7310046920715, 57.5715824785637, 89.5047895292528, 58.4027215389776,91.3111216916161、69.2914902356924、91.4055258029079、85.3021190418994、52.2833630152431、47.5883305901355、48.3152264007455、78.1204536918961、68.0782265938132、55.3819029226251、51.9782682455077、61.5885922886562、89.6129641721643, 51.3818043642034, 61.8814673089921, 55.3399967676143,58.4083672383978, 88.0198518505328, 90.713100323986, 45.9230901490977,47.942176704251, 51.3202365201787, 43.4717297386365, 59.2741650079789,50.3975658567551、54.6723278637849、85.3465611452765、58.0340634611641、58.33846091558、55.372988962717、55.3585987802603、72.3599002382954、58.2521103792226、65.716183348586、58.1599124794039、51.2453091189091、56.5749100234884), `CLASS 11` = c(55.2061491278648, 48.2689953079285,42.4284175214362、10.4952104707472、41.5972784610224、8.68887830838393、30.7085097643076, 8.59447419709211, 14.6978809581006, 47.7166369847569,52.4116694098645、51.6847735992545、21.8795463081039、31.9217734061868、44.6180970773749, 48.0217317544923, 38.4114077113438, 10.3870358278357,48.6181956357966, 38.1185326910079, 44.6600032323857, 41.5916327616022,11.9801481494672, 9.28689967601398, 54.0769098509023, 52.0578232957489,48.6797634798213, 56.5282702613635, 40.7258349920211, 49.6024341432449,45.3276721362151、14.6534388547235、41.9659365388359、41.66153908442、44.627011037283, 44.6414012197397, 27.6400997617046, 41.7478896207774,34.283816651414、41.8400875205961、48.7546908810909、43.4250899765116), `CLASS 12` = c(0.811392418775427, 1.07172325344784, 1.35691090645737,8.52815575054215、1.40400342762093、10.5089654211764、2.25642633809048、10.6353831202186, 5.80370185913679, 1.09570511081795, 0.907972043744494,0.934805805194479, 3.57047868323309, 2.13265803649301, 1.24124305047309,1.08239054166649, 1.60339326148851, 8.62738568129464, 1.05684309531167,1.62339583767845, 1.23914000811097, 1.40432975000493, 7.34714218491929,9.76785617252635, 0.849218090969217, 0.920940862853288, 1.05424169822542,0.769026356858985, 1.45544382379371, 1.01603009463636, 1.20615785649631,5.82433666195463、1.38288498357373、1.40029538508808、1.24079537651438、1.24007305478085、2.61793194894868、1.3953306600253、1.91682810629766、1.39005236188319, 1.05108468934595, 1.30281618424025)), class = c("tbl_df","tbl", "data.frame"), row.names = c(NA, -42L), .Names = c("Species",治疗"、1 类"、2 类"、3 类"、4 类"、5 类"，"CLASS 6", "CLASS 7", "CLASS 8", "CLASS 9", "CLASS 10", "CLASS 11",第 12 类"))图书馆(重塑2)融化 <- 融化(例如，id=c("TREATMENT","Species"), value.name="Percentage", variable.name = "Class") 解决方案
您可以使用 dplyr 包对 Species 和 的每个组合运行方差分析类.
库(reshape2)图书馆(dplyr)图书馆(扫帚)datm <-melt(example, id=c("TREATMENT","Species"), value.name="Percentage", variable.name = "Class")现在我们使用 group_by 函数对 Species 和 Class 的每个组合运行方差分析，将数据分解为所需的分组.这将返回一个数据框，其中前两列是 Species 和 Class，第三列的每个元素是一个具有 aov 模型的列表输出.
res = datm %>% group_by(Species, Class) %>%做(模型= aov(百分比~治疗，数据=.))在整洁的数据框中获取结果.
tidy(res, Model)<块引用>
Species Class term df sumsq meanq statistic p.value1 A 类 1 治疗 1 1.660188e+02 166.01875057 0.9697826054 0.353576792 A 类 1 残差 8 1.369534e+03 171.19171827 NA NA3 A 类 2 处理 1 2.227081e+02 222.70809890 0.6192884560 0.453991254 A 2 类残差 8 2.876955e+03 359.61932885 NA NA...93 D 类 11 治疗 1 7.653510e+00 7.65350993 0.1968918980 0.6689974894 D 类 11 残差 8 3.109731e+02 38.87163469 NA NA95 D 12 级处理 1 2.799650e-02 0.02799650 0.1232651091 0.7345865796 D 类 12 残差 8 1.816994e+00 0.22712429 NA NAI have a very large data set that requires individual one-way ANOVAs for multiple species on 4 different treatments with several measurements. Usually I just make separate excel spread sheets for each species and run ANOVAs on each, looping through each measurement column, but this is very time consuming. Is it possible to use a single spread sheet and run ANOVAs on the melted data? Or perhaps there is another reshape option I could use?
Data:
structure(list(Species = c("A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "D",
"D", "D", "D", "D", "D", "D", "D", "D", "D"), TREATMENT = c(1,
1, 1, 2, 2, 2, 3, 3, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 1,
1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4),
`CLASS 1` = c(9.3993303987076, 8.14588087483834, 8.44889021858093,
28.2773809415175, 9.49156649568952, 37.406663111623, 8.42458221212166,
35.529904738035, 33.1401135085753, 8.26977630375797, 7.87786527094827,
7.83020300515061, 35.1465417538538, 10.5560853720815, 7.54702433773332,
7.15030081390987, 7.73624654623485, 33.6461639540039, 10.3098164094602,
7.79017325570062, 8.47473750173462, 8.37179798600773, 31.7364310355766,
2.00147496567679, 9.30194886619568, 7.87886829173726, 7.93445694220837,
9.10020522660375, 8.81542855137005, 7.83313314713951, 7.84449591023115,
23.6150030864875, 9.3452854347794, 8.91047098149179, 7.76031738257155,
9.79467065201063, 24.7592334362831, 8.54842834366722, 7.60436112798701,
8.93480758329653, 8.72406315335014, 9.49850179222777), `CLASS 2` = c(10.8069912074565,
4.52426389123869, 8.13120921128287, 52.3870196313339, 7.17369219206721,
45.7325224336886, 17.8345921677786, 38.4579761235057, 37.5916934855387,
6.28803058195647, 3.72788988807285, 4.64744990904241, 29.7689968962103,
7.08515103144071, 6.44277647222835, 5.71017728280462, 6.28290843412007,
45.6123170472575, 6.98431855663527, 8.03809625184267, 4.76656440828616,
6.74640254081232, 31.3243238213156, 45.1287867136276, 7.6308508343969,
4.0127554151831, 9.11910102221636, 6.1658394708941, 10.4617259648364,
6.07502685224869, 8.08373642262043, 48.588633863193, 10.2160085507338,
7.52606530219909, 5.66373884014351, 8.51992766801391, 25.9109062123364,
5.74498954209992, 5.56377323143979, 7.76698847227212, 7.05016373786876,
7.99745310894107), `CLASS 3` = c(3.96856956332584, 2.76052305637364,
6.92715392916015, 0.687821057043984, 2.30154255462355, 2.61089063893911,
11.2199145273738, 10.7058533354417, 1.90691767773411, 3.93488282297868,
1.7034110083142, 1.69310511636903, 1.54005861925764, 50.436990190291,
3.93233520754151, 4.06684782901502, 6.10592204678281, 0.675086986967025,
3.94018776658881, 5.74129993338595, 2.02845185559621, 4.10963382465756,
14.9264019576272, 12.9672579626868, 5.1049208042632, 1.37282635713804,
3.00088572108344, 4.78878116348504, 4.79564218319094, 3.03836532949481,
3.48474205480686, 1.09218910757234, 6.2830307568812, 3.06784943090836,
4.89376208853059, 6.6321148581705, 1.01356027363186, 3.15439940439419,
40.8141653079423, 2.52825000616702, 1.65382018138259, 1.81173455682492
), `CLASS 4` = c(0, 13.4274810838142, 10.9876140536356, 3.15424686759082,
15.2632739415738, 0, 0, 2.39525969535064, 4.19386122886851,
13.15599261724, 14.5421891905919, 14.4542067660843, 0, 0,
12.2276086827261, 12.7527880016103, 12.1436697242409, 3.79216208516423,
0, 12.2283190622827, 16.0271803699645, 14.035876401479, 4.24556176551009,
0, 0, 14.4993393432366, 13.6722412691012, 0, 14.0027443968931,
13.7579074961889, 12.9935353616471, 4.66128854387559, 0,
15.1941922851023, 11.6990009190362, 0, 7.99399142573694,
14.5041748372822, 0, 15.0674109079436, 13.7134908002476,
15.4194201146961), `CLASS 5` = c(7.82638584740367, 6.56112678542475,
6.95253086439919, 2.06445951884762, 7.17086660532553, 2.58627258328855,
7.83400556063298, 1.77053879587063, 2.65292759651742, 6.94701807830366,
6.85309102458439, 6.71505104532983, 4.06818278652755, 5.79906266122279,
6.62064468061089, 6.88365856613044, 7.68403751285005, 2.38479005191691,
9.07405520739349, 7.65785587918449, 7.4385885335047, 7.30144390122309,
2.41680929257195, 4.18258704279641, 7.8906816661241, 6.75678558060943,
6.67150537517493, 8.24794113296791, 7.67443442992891, 6.89357008866252,
6.45444668132533, 2.98342694785768, 8.704729108357, 7.14382850099481,
7.15233553294014, 9.14001781571836, 3.98831954045444, 7.54093786042356,
5.79029360470226, 6.82793163574773, 6.48049736162586, 7.18554914992982
), `CLASS 6` = c(20.6189597026452, 22.8728557858066, 23.0767150659042,
4.99832103176657, 24.1726463550235, 5.56104550736533, 31.8124013284184,
4.31653191057476, 8.4695331411828, 20.63468068931, 19.7369752322083,
19.6902616040991, 11.6648564225744, 0, 25.2321582223958,
22.2981543181678, 29.3198455372777, 5.88723409877159, 30.1474816315191,
28.0835788057802, 24.0430626320328, 25.1446564854412, 5.78713327050339,
30.6155806819949, 23.8853696442419, 20.1783872969561, 17.5935515655693,
23.4169038776536, 21.1986239116884, 19.6931330316831, 22.2658181144794,
7.38944654414811, 32.1897387187698, 23.6398829158785, 25.3561697324352,
30.4118856020653, 12.6822088903071, 26.300118251779, 11.7338836812169,
23.8624555097246, 20.1037712460599, 21.8478004507985), `CLASS 7` = c(15.9129851563051,
15.2250454288061, 13.5577123002506, 2.9902563940573, 15.4408266617369,
2.67511425705514, 8.17164465017573, 2.23047357314211, 4.01010767344732,
13.4046459481448, 15.3008244637288, 15.3885729336047, 7.81496654756214,
17.8194559247092, 12.7823202355514, 13.7684066964868, 15.378473991847,
3.75026919344972, 18.2880822635935, 14.7412162942703, 17.5270089738067,
16.799718650752, 4.33839497916674, 2.21937177530762, 15.0315149187176,
15.3632530721031, 14.1580725482114, 16.4215442147509, 15.5113323256627,
14.3349000132624, 12.8504657216928, 5.06281347160092, 15.5075336560533,
15.9392345541138, 13.3981839319596, 16.6700105346756, 8.10398633871805,
15.958090408468, 16.5733149488757, 15.1802203155931, 14.2236219296677,
16.2095182295187), `CLASS 8` = c(19.9174685533413, 16.6755018156139,
13.9892072522183, 3.35339208579287, 18.98558519396, 3.42749146804023,
10.4801793890691, 2.97802997775506, 5.11270635117451, 17.0372757040089,
18.7865491767228, 18.3992789502607, 9.99639697401416, 0,
15.9270550696003, 17.1615519869107, 15.3488962066467, 4.25197658246908,
21.2560581648095, 15.7194605175531, 19.6944057250743, 17.4904702096271,
5.22494387772846, 2.88494085790995, 19.1038328534942, 19.0183655117756,
17.533290326259, 19.92632149392, 17.5400682364295, 17.664926273487,
16.3075864395099, 6.6071984352649, 17.7536737744256, 18.5784760293114,
14.706720581834, 18.8313728693457, 9.73353207739478, 18.2488613518859,
8.53356517614357, 19.8319355692553, 17.4801581342745, 20.0300225970631
), `CLASS 9` = c(11.5493095708147, 9.80732127808386, 7.92896710456816,
2.08710247204941, 0, 0, 4.22268016442976, 1.61543185032431,
2.92213933696131, 10.3276972542995, 11.4712047448286, 11.1818706700593,
0, 8.30325482025479, 9.28807709161222, 10.2081145049644,
0, 0, 0, 0, 0, 0, 0, 0, 12.0508804125665, 10.9194191312608,
10.316895230176, 11.9324634197247, 0, 10.709037767833, 9.7151732936871,
0, 0, 0, 9.36977099054923, 0, 5.81426180513736, 0, 3.38664292169246,
0, 10.5704134555229, 0), `CLASS 10` = c(44.7938508721352,
51.7310046920715, 57.5715824785637, 89.5047895292528, 58.4027215389776,
91.3111216916161, 69.2914902356924, 91.4055258029079, 85.3021190418994,
52.2833630152431, 47.5883305901355, 48.3152264007455, 78.1204536918961,
68.0782265938132, 55.3819029226251, 51.9782682455077, 61.5885922886562,
89.6129641721643, 51.3818043642034, 61.8814673089921, 55.3399967676143,
58.4083672383978, 88.0198518505328, 90.713100323986, 45.9230901490977,
47.942176704251, 51.3202365201787, 43.4717297386365, 59.2741650079789,
50.3975658567551, 54.6723278637849, 85.3465611452765, 58.0340634611641,
58.33846091558, 55.372988962717, 55.3585987802603, 72.3599002382954,
58.2521103792226, 65.716183348586, 58.1599124794039, 51.2453091189091,
56.5749100234884), `CLASS 11` = c(55.2061491278648, 48.2689953079285,
42.4284175214362, 10.4952104707472, 41.5972784610224, 8.68887830838393,
30.7085097643076, 8.59447419709211, 14.6978809581006, 47.7166369847569,
52.4116694098645, 51.6847735992545, 21.8795463081039, 31.9217734061868,
44.6180970773749, 48.0217317544923, 38.4114077113438, 10.3870358278357,
48.6181956357966, 38.1185326910079, 44.6600032323857, 41.5916327616022,
11.9801481494672, 9.28689967601398, 54.0769098509023, 52.0578232957489,
48.6797634798213, 56.5282702613635, 40.7258349920211, 49.6024341432449,
45.3276721362151, 14.6534388547235, 41.9659365388359, 41.66153908442,
44.627011037283, 44.6414012197397, 27.6400997617046, 41.7478896207774,
34.283816651414, 41.8400875205961, 48.7546908810909, 43.4250899765116
), `CLASS 12` = c(0.811392418775427, 1.07172325344784, 1.35691090645737,
8.52815575054215, 1.40400342762093, 10.5089654211764, 2.25642633809048,
10.6353831202186, 5.80370185913679, 1.09570511081795, 0.907972043744494,
0.934805805194479, 3.57047868323309, 2.13265803649301, 1.24124305047309,
1.08239054166649, 1.60339326148851, 8.62738568129464, 1.05684309531167,
1.62339583767845, 1.23914000811097, 1.40432975000493, 7.34714218491929,
9.76785617252635, 0.849218090969217, 0.920940862853288, 1.05424169822542,
0.769026356858985, 1.45544382379371, 1.01603009463636, 1.20615785649631,
5.82433666195463, 1.38288498357373, 1.40029538508808, 1.24079537651438,
1.24007305478085, 2.61793194894868, 1.3953306600253, 1.91682810629766,
1.39005236188319, 1.05108468934595, 1.30281618424025)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -42L), .Names = c("Species",
"TREATMENT", "CLASS 1", "CLASS 2", "CLASS 3", "CLASS 4", "CLASS 5",
"CLASS 6", "CLASS 7", "CLASS 8", "CLASS 9", "CLASS 10", "CLASS 11",
"CLASS 12"))
library(Reshape2)
melt <- melt(example, id=c("TREATMENT","Species"), value.name="Percentage", variable.name = "Class")
解决方案
You can use the dplyr package to run an ANOVA on each combination of Species and Class.
library(reshape2)
library(dplyr)
library(broom)
datm <- melt(example, id=c("TREATMENT","Species"), value.name="Percentage", variable.name = "Class")
Now we run an ANOVA for each combination of Species and Class using the group_by function to break the data up into the desired groupings. This returns a data frame where the first two columns are the Species and Class and each element of the third column is a list with the aov model output.
res = datm %>% group_by(Species, Class) %>%
do(Model = aov(Percentage ~ TREATMENT, data=.))
Get results in a tidy data frame.
tidy(res, Model)
Species Class term df sumsq meansq statistic p.value 1 A CLASS 1 TREATMENT 1 1.660188e+02 166.01875057 0.9697826054 0.35357679 2 A CLASS 1 Residuals 8 1.369534e+03 171.19171827 NA NA 3 A CLASS 2 TREATMENT 1 2.227081e+02 222.70809890 0.6192884560 0.45399125 4 A CLASS 2 Residuals 8 2.876955e+03 359.61932885 NA NA ... 93 D CLASS 11 TREATMENT 1 7.653510e+00 7.65350993 0.1968918980 0.66899748 94 D CLASS 11 Residuals 8 3.109731e+02 38.87163469 NA NA 95 D CLASS 12 TREATMENT 1 2.799650e-02 0.02799650 0.1232651091 0.73458657 96 D CLASS 12 Residuals 8 1.816994e+00 0.22712429 NA NA
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