以下是代码示例
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
我尝试过使用confint.default，但收到错误
tc <- confint.default(t, level=0.95)
Error: $ operator not defined for this S4 class
答案 0 :(得分：3)
它有点埋没在文档中，但是减慢你速度的是多重比较校正计算。可用方法的耗用时间差异很大。有关详细信息，请参阅?summary.ref.grid的置信限制和P值调整部分，并确定哪种方法符合您的标准，既足够快又可靠，足以满足您的使用需求...... < / p>
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