名录应用t检验中的R

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我在R A列表对象与数据框名称为：名录应用t检验中的R

"pav_DJF_histo.csv"  "pav_DJF_rcp26_2040s.csv" 
"pav_DJF_rcp26_2080s.csv" 
"pav_DJF_rcp45_2040s.csv""pav_DJF_rcp45_2080s.csv" 
"pav_DJF_rcp85_2040s.csv" "pav_DJF_rcp85_2080s.csv" 



"pav_JJA_histo.csv" 
"pav_JJA_rcp26_2040s.csv" "pav_JJA_rcp26_2080s.csv" 
"pav_JJA_rcp45_2040s.csv" "pav_JJA_rcp45_2080s.csv" 
"pav_JJA_rcp85_2040s.csv" "pav_JJA_rcp85_2080s.csv"

等等...

我想申请的t检验在两者之间平均差异：

"pav_DJF_histo.csv" and all dataframes with names having "pav_DJF..."

同为

"pav_JJA_histo.csv" and all dataframes with names having "pav_JJA..."

等等...

我的列表中有84个数据框。将如上所述在所有84个数据帧上执行计算。

感谢您的建议。阿宋。

“pav_DJF_histo.csv”和所有名称均为“pav_DJF ...”的数据框的可重复使用的示例。

每个df有1行* 120列。

我会喜欢做的事：

t.test（pav_DJF_histo.csv，all_dataframes）

structure(list(pav_DJF_histo.csv = structure(list(G100_pav_DJF = 0.0314208328712871, 
    G101_pav_DJF = 0.0316052879207921, G102_pav_DJF = 0.0338115233663366, 
    G103_pav_DJF = 0.0349753320792079, G104_pav_DJF = 0.0340410627722772, 
    G105_pav_DJF = 0.0344961831683168, G106_pav_DJF = 0.0331672699009901, 
    G107_pav_DJF = 0.0335578704950495, G108_pav_DJF = 0.0318934661386139, 
    G109_pav_DJF = 0.0326319041584158, G110_pav_DJF = 0.0314491928712871, 
    G111_pav_DJF = 0.0295078394059406, G112_pav_DJF = 0.0312701207920792, 
    G113_pav_DJF = 0.0274926542574257, G114_pav_DJF = 0.0280412045544554, 
    G115_pav_DJF = 0.0308147467326733, G116_pav_DJF = 0.0276809968316832, 
    G117_pav_DJF = 0.0334523455445545, G118_pav_DJF = 0.0231678550495049, 
    G119_pav_DJF = 0.0329736546534653, G120_pav_DJF = 0.0293986465346535, 
    GG10_pav_DJF = 0.0272844384158416, GG11_pav_DJF = 0.0250696742574257, 
    GG12_pav_DJF = 0.0267913558415842, GG13_pav_DJF = 0.028447095049505, 
    GG14_pav_DJF = 0.0258856714851485, GG15_pav_DJF = 0.0284966926732673, 
    GG16_pav_DJF = 0.0259450320792079, GG17_pav_DJF = 0.0275422631683168, 
    GG18_pav_DJF = 0.0267659001980198, GG19_pav_DJF = 0.0239620502970297, 
    GG20_pav_DJF = 0.0235523667326733, GG21_pav_DJF = 0.0280495275247525, 
    GG22_pav_DJF = 0.0260046952475248, GG23_pav_DJF = 0.0245813871287129, 
    GG24_pav_DJF = 0.0225754382178218, GG25_pav_DJF = 0.0340031586138614, 
    GG26_pav_DJF = 0.0281897057425743, GG27_pav_DJF = 0.0290264237623762, 
    GG28_pav_DJF = 0.0387512556435644, GG29_pav_DJF = 0.0316748243564356, 
    GG30_pav_DJF = 0.0268749459405941, GG31_pav_DJF = 0.0306790738613861, 
    GG32_pav_DJF = 0.0265153081188119, GG33_pav_DJF = 0.0287865821782178, 
    GG34_pav_DJF = 0.0269848536633663, GG35_pav_DJF = 0.0237527348514851, 
    GG36_pav_DJF = 0.0264141081188119, GG37_pav_DJF = 0.0273517104950495, 
    GG38_pav_DJF = 0.0299628936633663, GG39_pav_DJF = 0.0275048685148515, 
    GG40_pav_DJF = 0.0196275314851485, GG41_pav_DJF = 0.0226415651485149, 
    GG42_pav_DJF = 0.0292957691089109, GG43_pav_DJF = 0.0240719154455446, 
    GG44_pav_DJF = 0.0264125300990099, GG45_pav_DJF = 0.0245377025742574, 
    GG46_pav_DJF = 0.0254801978217822, GG47_pav_DJF = 0.0264283477227723, 
    GG48_pav_DJF = 0.0221284198019802, GG49_pav_DJF = 0.0281992881188119, 
    GG50_pav_DJF = 0.0251214203960396, GG51_pav_DJF = 0.022804923960396, 
    GG52_pav_DJF = 0.0253265572277228, GG53_pav_DJF = 0.0248078140594059, 
    GG54_pav_DJF = 0.0229847805940594, GG55_pav_DJF = 0.0245689685148515, 
    GG56_pav_DJF = 0.024459103960396, GG57_pav_DJF = 0.0261233461386139, 
    GG58_pav_DJF = 0.0248389976237624, GG59_pav_DJF = 0.0238194382178218, 
    GG60_pav_DJF = 0.025920022970297, GG61_pav_DJF = 0.0232416265346535, 
    GG62_pav_DJF = 0.0254770396039604, GG63_pav_DJF = 0.0248223295049505, 
    GG64_pav_DJF = 0.0249457611881188, GG65_pav_DJF = 0.0237617085148515, 
    GG66_pav_DJF = 0.023653757029703, GG67_pav_DJF = 0.0225660194059406, 
    GG68_pav_DJF = 0.0209042742574257, GG69_pav_DJF = 0.0253348401980198, 
    GG70_pav_DJF = 0.0269268635643564, GG71_pav_DJF = 0.0257322499009901, 
    GG72_pav_DJF = 0.0261817491089109, GG73_pav_DJF = 0.0267062437623762, 
    GG74_pav_DJF = 0.0254928786138614, GG75_pav_DJF = 0.0263220520792079, 
    GG76_pav_DJF = 0.0266288738613861, GG77_pav_DJF = 0.0261239605940594, 
    GG78_pav_DJF = 0.0239993772277228, GG79_pav_DJF = 0.0158666427722772, 
    GG80_pav_DJF = 0.0175424689108911, GG81_pav_DJF = 0.0193922348514851, 
    GG82_pav_DJF = 0.0263564615841584, GG83_pav_DJF = 0.0194876946534653, 
    GG84_pav_DJF = 0.0253149083168317, GG85_pav_DJF = 0.0250615659405941, 
    GG86_pav_DJF = 0.0241572235643564, GG87_pav_DJF = 0.0232231510891089, 
    GG88_pav_DJF = 0.027858396039604, GG89_pav_DJF = 0.0271891471287129, 
    GG90_pav_DJF = 0.0269782621782178, GG91_pav_DJF = 0.0259993922772277, 
    GG92_pav_DJF = 0.0271551689108911, GG93_pav_DJF = 0.0274789423762376, 
    GG94_pav_DJF = 0.0246039, GG95_pav_DJF = 0.0314879467326733, 
    GG96_pav_DJF = 0.031215642970297, GG97_pav_DJF = 0.0259254443564356, 
    GG98_pav_DJF = 0.028124596039604, GG99_pav_DJF = 0.0293694499009901, 
    GGG1_pav_DJF = 0.0280423463366337, GGG2_pav_DJF = 0.0266454362376238, 
    GGG3_pav_DJF = 0.0262475837623762, GGG4_pav_DJF = 0.0271603285148515, 
    GGG5_pav_DJF = 0.026403784950495, GGG6_pav_DJF = 0.026692183960396, 
    GGG7_pav_DJF = 0.0281706176237624, GGG8_pav_DJF = 0.0275187194059406, 
    GGG9_pav_DJF = 0.0267755964356436), .Names = c("G100_pav_DJF", 
"G101_pav_DJF", "G102_pav_DJF", "G103_pav_DJF", "G104_pav_DJF", 
"G105_pav_DJF", "G106_pav_DJF", "G107_pav_DJF", "G108_pav_DJF", 
"G109_pav_DJF", "G110_pav_DJF", "G111_pav_DJF", "G112_pav_DJF", 
"G113_pav_DJF", "G114_pav_DJF", "G115_pav_DJF", "G116_pav_DJF", 
"G117_pav_DJF", "G118_pav_DJF", "G119_pav_DJF", "G120_pav_DJF", 
"GG10_pav_DJF", "GG11_pav_DJF", "GG12_pav_DJF", "GG13_pav_DJF", 
"GG14_pav_DJF", "GG15_pav_DJF", "GG16_pav_DJF", "GG17_pav_DJF", 
"GG18_pav_DJF", "GG19_pav_DJF", "GG20_pav_DJF", "GG21_pav_DJF", 
"GG22_pav_DJF", "GG23_pav_DJF", "GG24_pav_DJF", "GG25_pav_DJF", 
"GG26_pav_DJF", "GG27_pav_DJF", "GG28_pav_DJF", "GG29_pav_DJF", 
"GG30_pav_DJF", "GG31_pav_DJF", "GG32_pav_DJF", "GG33_pav_DJF", 
"GG34_pav_DJF", "GG35_pav_DJF", "GG36_pav_DJF", "GG37_pav_DJF", 
"GG38_pav_DJF", "GG39_pav_DJF", "GG40_pav_DJF", "GG41_pav_DJF", 
"GG42_pav_DJF", "GG43_pav_DJF", "GG44_pav_DJF", "GG45_pav_DJF", 
"GG46_pav_DJF", "GG47_pav_DJF", "GG48_pav_DJF", "GG49_pav_DJF", 
"GG50_pav_DJF", "GG51_pav_DJF", "GG52_pav_DJF", "GG53_pav_DJF", 
"GG54_pav_DJF", "GG55_pav_DJF", "GG56_pav_DJF", "GG57_pav_DJF", 
"GG58_pav_DJF", "GG59_pav_DJF", "GG60_pav_DJF", "GG61_pav_DJF", 
"GG62_pav_DJF", "GG63_pav_DJF", "GG64_pav_DJF", "GG65_pav_DJF", 
"GG66_pav_DJF", "GG67_pav_DJF", "GG68_pav_DJF", "GG69_pav_DJF", 
"GG70_pav_DJF", "GG71_pav_DJF", "GG72_pav_DJF", "GG73_pav_DJF", 
"GG74_pav_DJF", "GG75_pav_DJF", "GG76_pav_DJF", "GG77_pav_DJF", 
"GG78_pav_DJF", "GG79_pav_DJF", "GG80_pav_DJF", "GG81_pav_DJF", 
"GG82_pav_DJF", "GG83_pav_DJF", "GG84_pav_DJF", "GG85_pav_DJF", 
"GG86_pav_DJF", "GG87_pav_DJF", "GG88_pav_DJF", "GG89_pav_DJF", 
"GG90_pav_DJF", "GG91_pav_DJF", "GG92_pav_DJF", "GG93_pav_DJF", 
"GG94_pav_DJF", "GG95_pav_DJF", "GG96_pav_DJF", "GG97_pav_DJF", 
"GG98_pav_DJF", "GG99_pav_DJF", "GGG1_pav_DJF", "GGG2_pav_DJF", 
"GGG3_pav_DJF", "GGG4_pav_DJF", "GGG5_pav_DJF", "GGG6_pav_DJF", 
"GGG7_pav_DJF", "GGG8_pav_DJF", "GGG9_pav_DJF"), row.names = c(NA, 
-1L), class = "data.frame"), pav_DJF_rcp26_2040s.csv = structure(list(
    G100_pav_DJF = 0.0336921695049505, G101_pav_DJF = 0.0353346894059406, 
    G102_pav_DJF = 0.0374039577722772, G103_pav_DJF = 0.0382527494059406, 
    G104_pav_DJF = 0.0372147038613861, G105_pav_DJF = 0.036982626039604, 
    G106_pav_DJF = 0.0357056598514851, G107_pav_DJF = 0.0367259367326733, 
    G108_pav_DJF = 0.0339615762376238, G109_pav_DJF = 0.0352461818316832, 
    G110_pav_DJF = 0.0331901645544554, G111_pav_DJF = 0.0327048907425743, 
    G112_pav_DJF = 0.0338771433663366, G113_pav_DJF = 0.0304345107425743, 
    G114_pav_DJF = 0.0299715592574257, G115_pav_DJF = 0.0323663623267327, 
    G116_pav_DJF = 0.0302113144059406, G117_pav_DJF = 0.0348880272772277, 
    G118_pav_DJF = 0.0244634474752475, G119_pav_DJF = 0.0356601117821782, 
    G120_pav_DJF = 0.0318457833168317, GG10_pav_DJF = 0.0292012213366337, 
    GG11_pav_DJF = 0.0276689620792079, GG12_pav_DJF = 0.0293319658910891, 
    GG13_pav_DJF = 0.0304862144059406, GG14_pav_DJF = 0.0280408795544554, 
    GG15_pav_DJF = 0.0301200932178218, GG16_pav_DJF = 0.0286451583663366, 
    GG17_pav_DJF = 0.0300997758415842, GG18_pav_DJF = 0.0288163182673267, 
    GG19_pav_DJF = 0.0263369057920792, GG20_pav_DJF = 0.0266477852475248, 
    GG21_pav_DJF = 0.0300933431683168, GG22_pav_DJF = 0.0289349790594059, 
    GG23_pav_DJF = 0.0268180481683168, GG24_pav_DJF = 0.0244735858415842, 
    GG25_pav_DJF = 0.0365696418316832, GG26_pav_DJF = 0.0300007536138614, 
    GG27_pav_DJF = 0.0316598915346535, GG28_pav_DJF = 0.0408627464356436, 
    GG29_pav_DJF = 0.0336171423762376, GG30_pav_DJF = 0.0293838656930693, 
    GG31_pav_DJF = 0.0328603355445545, GG32_pav_DJF = 0.029574533960396, 
    GG33_pav_DJF = 0.030923384009901, GG34_pav_DJF = 0.0295480556930693, 
    GG35_pav_DJF = 0.0253115618316832, GG36_pav_DJF = 0.0283057747029703, 
    GG37_pav_DJF = 0.0302438872277228, GG38_pav_DJF = 0.01403465347, 
    GG39_pav_DJF = 0.0297127561386139, GG40_pav_DJF = 0.0210915803465347, 
    GG41_pav_DJF = 0.0244056779207921, GG42_pav_DJF = 0.0318838803465347, 
    GG43_pav_DJF = 0.0262421126237624, GG44_pav_DJF = 0.0286438319306931, 
    GG45_pav_DJF = 0.0269299434158416, GG46_pav_DJF = 0.0278216301980198, 
    GG47_pav_DJF = 0.0284923436633663, GG48_pav_DJF = 0.024062545, 
    GG49_pav_DJF = 0.0297595119306931, GG50_pav_DJF = 0.0267064392079208, 
    GG51_pav_DJF = 0.0251601975247525, GG52_pav_DJF = 0.0270363448019802, 
    GG53_pav_DJF = 0.027031203960396, GG54_pav_DJF = 0.0253016908415842, 
    GG55_pav_DJF = 0.0266129271287129, GG56_pav_DJF = 0.0264486413861386, 
    GG57_pav_DJF = 0.0283687212376238, GG58_pav_DJF = 0.0267607587623762, 
    GG59_pav_DJF = 0.0258524088118812, GG60_pav_DJF = 0.0284728297029703, 
    GG61_pav_DJF = 0.0242683505445545, GG62_pav_DJF = 0.0269807252970297, 
    GG63_pav_DJF = 0.0260612163366337, GG64_pav_DJF = 0.0268121706435644, 
    GG65_pav_DJF = 0.0254399943069307, GG66_pav_DJF = 0.0256744116831683, 
    GG67_pav_DJF = 0.0242163128712871, GG68_pav_DJF = 0.0232865385643564, 
    GG69_pav_DJF = 0.027024237970297, GG70_pav_DJF = 0.0280309896039604, 
    GG71_pav_DJF = 0.027491227970297, GG72_pav_DJF = 0.0271313031188119, 
    GG73_pav_DJF = 0.0286036438118812, GG74_pav_DJF = 0.0282849307920792, 
    GG75_pav_DJF = 0.0280740992079208, GG76_pav_DJF = 0.0294208429207921, 
    GG77_pav_DJF = 0.0286056740594059, GG78_pav_DJF = 0.0252083457425743, 
    GG79_pav_DJF = 0.0171779085148515, GG80_pav_DJF = 0.0187473600495049, 
    GG81_pav_DJF = 0.020924249950495, GG82_pav_DJF = 0.0285746283663366, 
    GG83_pav_DJF = 0.0212331709405941, GG84_pav_DJF = 0.0275696948019802, 
    GG85_pav_DJF = 0.027292552970297, GG86_pav_DJF = 0.0265909641584158, 
    GG87_pav_DJF = 0.025798541039604, GG88_pav_DJF = 0.0306168697029703, 
    GG89_pav_DJF = 0.0303202963861386, GG90_pav_DJF = 0.0291813004950495, 
    GG91_pav_DJF = 0.0280794616831683, GG92_pav_DJF = 0.0299837882673267, 
    GG93_pav_DJF = 0.0299919872772277, GG94_pav_DJF = 0.0269039003465347, 
    GG95_pav_DJF = 0.0339354712376238, GG96_pav_DJF = 0.0325601980693069, 
    GG97_pav_DJF = 0.0285637703465347, GG98_pav_DJF = 0.0313221871782178, 
    GG99_pav_DJF = 0.0323324087128713, GGG1_pav_DJF = 0.0298989088118812, 
    GGG2_pav_DJF = 0.0290734964356436, GGG3_pav_DJF = 0.0281705633663366, 
    GGG4_pav_DJF = 0.0291793695544554, GGG5_pav_DJF = 0.0279056234653465, 
    GGG6_pav_DJF = 0.0281455424752475, GGG7_pav_DJF = 0.0309308349009901, 
    GGG8_pav_DJF = 0.0294452942574257, GGG9_pav_DJF = 0.0289754272277228), .Names = c("G100_pav_DJF", 
"G101_pav_DJF", "G102_pav_DJF", "G103_pav_DJF", "G104_pav_DJF", 
"G105_pav_DJF", "G106_pav_DJF", "G107_pav_DJF", "G108_pav_DJF", 
"G109_pav_DJF", "G110_pav_DJF", "G111_pav_DJF", "G112_pav_DJF", 
"G113_pav_DJF", "G114_pav_DJF", "G115_pav_DJF", "G116_pav_DJF", 
"G117_pav_DJF", "G118_pav_DJF", "G119_pav_DJF", "G120_pav_DJF", 
"GG10_pav_DJF", "GG11_pav_DJF", "GG12_pav_DJF", "GG13_pav_DJF", 
"GG14_pav_DJF", "GG15_pav_DJF", "GG16_pav_DJF", "GG17_pav_DJF", 
"GG18_pav_DJF", "GG19_pav_DJF", "GG20_pav_DJF", "GG21_pav_DJF", 
"GG22_pav_DJF", "GG23_pav_DJF", "GG24_pav_DJF", "GG25_pav_DJF", 
"GG26_pav_DJF", "GG27_pav_DJF", "GG28_pav_DJF", "GG29_pav_DJF", 
"GG30_pav_DJF", "GG31_pav_DJF", "GG32_pav_DJF", "GG33_pav_DJF", 
"GG34_pav_DJF", "GG35_pav_DJF", "GG36_pav_DJF", "GG37_pav_DJF", 
"GG38_pav_DJF", "GG39_pav_DJF", "GG40_pav_DJF", "GG41_pav_DJF", 
"GG42_pav_DJF", "GG43_pav_DJF", "GG44_pav_DJF", "GG45_pav_DJF", 
"GG46_pav_DJF", "GG47_pav_DJF", "GG48_pav_DJF", "GG49_pav_DJF", 
"GG50_pav_DJF", "GG51_pav_DJF", "GG52_pav_DJF", "GG53_pav_DJF", 
"GG54_pav_DJF", "GG55_pav_DJF", "GG56_pav_DJF", "GG57_pav_DJF", 
"GG58_pav_DJF", "GG59_pav_DJF", "GG60_pav_DJF", "GG61_pav_DJF", 
"GG62_pav_DJF", "GG63_pav_DJF", "GG64_pav_DJF", "GG65_pav_DJF", 
"GG66_pav_DJF", "GG67_pav_DJF", "GG68_pav_DJF", "GG69_pav_DJF", 
"GG70_pav_DJF", "GG71_pav_DJF", "GG72_pav_DJF", "GG73_pav_DJF", 
"GG74_pav_DJF", "GG75_pav_DJF", "GG76_pav_DJF", "GG77_pav_DJF", 
"GG78_pav_DJF", "GG79_pav_DJF", "GG80_pav_DJF", "GG81_pav_DJF", 
"GG82_pav_DJF", "GG83_pav_DJF", "GG84_pav_DJF", "GG85_pav_DJF", 
"GG86_pav_DJF", "GG87_pav_DJF", "GG88_pav_DJF", "GG89_pav_DJF", 
"GG90_pav_DJF", "GG91_pav_DJF", "GG92_pav_DJF", "GG93_pav_DJF", 
"GG94_pav_DJF", "GG95_pav_DJF", "GG96_pav_DJF", "GG97_pav_DJF", 
"GG98_pav_DJF", "GG99_pav_DJF", "GGG1_pav_DJF", "GGG2_pav_DJF", 
"GGG3_pav_DJF", "GGG4_pav_DJF", "GGG5_pav_DJF", "GGG6_pav_DJF", 
"GGG7_pav_DJF", "GGG8_pav_DJF", "GGG9_pav_DJF"), row.names = c(NA, 
-1L), class = "data.frame")), .Names = c("pav_DJF_histo.csv", 
"pav_DJF_rcp26_2040s.csv"))

来源

2015-02-17 code123

你在每个df只有一个变量吗？ – Metrics 2015-02-17 22:39:58

请编辑您的问题以创建一个MCVE：https://stackoverflow.com/help/mcve – Stedy 2015-02-17 23:17:48

@Metrics每个df在80个站点上都有一个变量。也就是说，每个df有1行80列。 – code123 2015-02-18 00:26:34

如果像@metrics表明，每一个数据文件只包含一个变量，那么我会读他们在第一行中将它们绑定到一个数据框。

set1 <- listOfYourData[grepl("DJF", listOfYourData] 

for (i in 1:length(set1))){ 
     if (i == 1) { 
     df1 <- read.table(paste(set[i]), ...) 
     } 
     else { 
     temp <- read.table(paste(set[i], ...) 
     df1 <- rbind(df, temp) 
     } 
    } 

#now you got two data frame, df1 has all the files that contain "DJF" while  

#Now do your t-tests; note that from what I understand, the file you want to 
test against is in the first column 

base <- df1[,1] 
pval <- rep(NA, ncol(df1)-1) 
for (i in 2:ncol(df1)){ 
    against <- df[,i] 
    test <- t.test(base, against, ...) 
    pval[i] <- test$p.value #save the p-value from your test 
    print(paste("The p-val for the diff in means between col 1 and col ", i, " is", pval[i], sep = "")) 
}

然后对df2执行相同的操作或立即执行此操作。有更有效的方法来做到这一点，但这也应该做到这一点。我希望我能理解你想要的东西，这有助于你！

来源

2015-02-17 23:55:08

每个df在80个站点上有一个变量。也就是说，每个df有1行* 80列 – code123 2015-02-18 00:29:51

名录应用t检验中的R

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