3

这是代码示例

cq = c(15.5193589369923,15.6082097993496,15.3048276641115,15.887944383963,15.3813224284544,14.9723432922121,14.8742353464212,15.0448020475332,15.1584221729435,15.3692219904727,15.2369219681739,15.0804950645883,15.0836397511495,14.8821059462034,14.6827696388115,14.5701385743889,14.8506248103639,14.8475325690146,14.7377458445001,14.6258765734272,15.3585770134881,15.2994209401567,15.5178103826596,15.2411668198437,15.3413307248142,15.3645926457095,15.2241340874265,15.7516405898009,15.7683360263607,15.5852354340738,14.7451372367313,14.650625258402,14.7596201108925,15.0504977144055,15.0178091754821,15.100874342289,14.5156700740607,15.0530667717479,14.754595621435,15.5879633065185,15.3449828894141,15.3112460363113,15.232600495493,15.4378070492087,15.1621663266126,16.0120124580213,16.2104534293435,16.2765899877946,17.1446379330444,17.1717364140053,17.0155350105157,15.5218922723681,15.4543443324508,15.5282690363252,15.0202919978723,15.0410524376083,15.1169661551775,15.335220483258,15.3191814464955,15.0679651604846,14.7270263787123,14.70717761566,14.7907442084919,15.8468089268423,15.6714073529734,15.5478017041242,14.6949593095613,14.7537769900696,14.830942214569,15.0820225358985,15.3454125813989,15.304399073517,15.4159319040107,16.1250033895004,15.5359407225865,15.3251900155103,15.1571914994646,15.412721442436,15.913112918988,15.9852823695227,16.0912887332562,15.4897399161851,15.0710262650299,15.3517226832146,17.0001128578501,17.1040579605654,16.9578316599788,15.8842918497549,15.7016383123704,15.8513519332371,16.9420990886101,17.0793832045434,16.9288868492911,14.9127628979216,14.7689529893246,15.0534122173222,15.3185448628303,15.5507864243439,15.3737185073511,15.4350799532271,15.2414612478027,15.361320770232,15.7401140808761,15.8582795450189,15.7634036480016,15.5797995263497,15.9126261329496,15.9256641722586,15.1308493265056,15.2450158090279,15.0699176510971,15.0368959001792,14.8828877909216,14.8852035927172,15.8253506435753,15.8938440960183,15.888311876759,15.4872886586516,15.5492199156675,15.7313291529313,16.5365758222542,16.8386981731158,16.7239280992675,15.9356391540897,15.7383049532238,15.9409000309973,15.2005952554035,15.0390142751348,15.154888655127,14.6373767323354,14.3087397097081,14.3970067065903,14.6453627024929,14.8109205614192,14.6266778290643,15.5170574352528,15.359943766027,15.5869322081508,15.5246550838727,15.4670382654415,15.4211907882731,16.9534561402918,17.4848334482537,17.3182067272327,15.7804318020053,15.86794322314,15.6532944587946,16.543432367992,16.6848617423114,16.8344939905775,15.5212254647114,15.8348559815603,15.6592827767612,15.3027400892518,15.5498124465958,15.8362202772445,14.8415823671167,15.7307379811374,14.8529575353737,16.6466266958983,16.1687733596343,16.0342973266029,14.5976161739123,14.776507726931,14.6780484406283,15.3927619991024,15.3106866267163,15.2920260038624,15.9666798099925,16.2595244266754,16.1035265916681,16.018233002759,15.8460056716414,16.0722176294152,16.2763177549617,16.364250121284,16.2995041975045,16.3975912697976,16.182759197402,16.1164022801451,16.5026752161837,16.2401540005223,16.3715573563274,18.4119769797938,18.1208386122385,18.0068316479116,17.1455993749728,17.0558275544137,16.9150038143768)
sample = c("CD4 LM","CD4 LM","CD4 JC","CD4 JC","CD4 JC","CD4 BM","CD4 BM","CD4 BM","CD4 MC","CD4 MC","CD4 MC","CD4 TM","CD4 TM","CD4 TM","CD4 MM","CD4 MM","CD4 MM","CD4 SRits","CD4 SRits","CD4 SRits","CD4 GV","CD4 GV","CD4 GV","CD4 WW","CD4 WW","CD4 WW","CD4 CH","CD4 CH","CD4 FJ","CD4 FJ","CD4 KS","CD4 KS","CD4 KS","CD4 NG","CD4 NG","CD4 NG","CD4 CG","CD4 CG","CD4 CG","CD4 CSR","CD4 CSR","CD4 CSR","CD4 JM","CD4 JM","CD4 JM","CD4 DF","CD4 DF","CD4 DF","CD4 AM","CD4 AM","CD4 AM","CD4 BP","CD4 BP","CD4 BP","CD4 ER","CD4 ER","CD4 ER","CD4 SRusse","CD4 SRusse","CD4 SRusse","CD4 DS","CD4 DS","CD4 DS","CD4 KJ","CD4 KJ","CD4 KJ","CD4 GD","CD4 GD","CD4 GD","CD4 KG","CD4 KG","CD4 KG","CD4 KR","CD4 KR","CD4 KR","CD4 FN","CD4 FN","CD4 FN","CD4 RM","CD4 RM","CD4 RM","CD4 LA","CD4 LA","CD4 LA","CD4 EC","CD4 EC","CD4 EC","CD4 KW","CD4 KW","CD4 KW","CD4 HB","CD4 HB","CD4 HB","CD8 LM","CD8 LM","CD8 LM","CD8 JC","CD8 JC","CD8 JC","CD8 BM","CD8 BM","CD8 BM","CD8 MC","CD8 MC","CD8 MC","CD8 TM","CD8 TM","CD8 TM","CD8 MM","CD8 MM","CD8 MM","CD8 SRits","CD8 SRits","CD8 SRits","CD8 GV","CD8 GV","CD8 GV","CD8 WW","CD8 WW","CD8 WW","CD8 CH","CD8 CH","CD8 CH","CD8 FJ","CD8 FJ","CD8 FJ","CD8 KS","CD8 KS","CD8 KS","CD8 NG","CD8 NG","CD8 NG","CD8 CG","CD8 CG","CD8 CG","CD8 CSR","CD8 CSR","CD8 CSR","CD8 JM","CD8 JM","CD8 JM","CD8 DF","CD8 DF","CD8 DF","CD8 AM","CD8 AM","CD8 AM","CD8 BP","CD8 BP","CD8 BP","CD8 ER","CD8 ER","CD8 ER","CD8 SRusse","CD8 SRusse","CD8 SRusse","CD8 DS","CD8 DS","CD8 DS","CD8 KJ","CD8 KJ","CD8 KJ","CD8 GD","CD8 GD","CD8 GD","CD8 KG","CD8 KG","CD8 KG","CD8 KR","CD8 KR","CD8 KR","CD8 FN","CD8 FN","CD8 FN","CD8 RM","CD8 RM","CD8 RM","CD8 LA","CD8 LA","CD8 LA","CD8 EC","CD8 EC","CD8 EC","CD8 KW","CD8 KW","CD8 KW","CD8 HB","CD8 HB","CD8 HB")
df = data.frame(cq, sample)

df.res <- lm(cq~sample, data = df)
require(lsmeans)
t<- pairs(lsmeans(df.res, "sample"))
system.time(tc <- confint(t, level=0.95))

confint函数花费的时间是

   user  system elapsed 
  10.58    0.00   10.60

我曾尝试使用 confint.default 但我得到一个错误

tc <- confint.default(t, level=0.95)

Error: $ operator not defined for this S4 class

4

1 回答 1

4

它有点隐藏在文档中，但让你慢下来的是多重比较校正计算。可用方法的经过时间差异很大。有关详细信息，请参阅Confidence-limit 和 P-value 调整部分?summary.ref.grid，并确定哪种方法满足您的标准，即对您的用例来说足够快且足够可靠......

adj <- c("tukey","scheffe","sidak","bonferroni","dunnettx","mvt") 
sapply(adj,function(a) system.time(confint(t,adjust=a))["elapsed"])

##      tukey.elapsed    scheffe.elapsed      sidak.elapsed bonferroni.elapsed 
         9.256              0.195              0.168              0.173 
##   dunnettx.elapsed        mvt.elapsed 
        14.370              1.821
于 2016-07-29T21:22:57.833 回答