4

在没有模式的情况下,有一半的时间我会从输出中省略行,即使我增加了 max.print 和 R studio 中的全局选项。详情如下:

我正在使用以下软件包在大型数据集(14 个变量的 100,000 多个观察值)上运行 lmer 模型:lme4、lmerTest、tidyverse、lsmeans

我有几个单独运行的数据集,但是这些数据集在设计上并没有什么不同,它们都来自同一个母数据集。我只是删除了每个数据集中的某些行来回答不同的问题。每个数据集中的变量包括小时、位置、日期、深度、温度和转换深度以满足假设。

由于某种原因,在我运行 lsmeans() 函数进行成对比较之后,有一半的时间省略了行:[达到 getOption("max.print") -- 省略了 110 行]

示例代码:

ft.model = lmer(dep.variable ~ fixed.factor + (1|random.factor), data = dataset)

anova(ft.model)

plot(ft.model, main = "residuals/fitted ft.model")

lsmeans(ft.model, pairwise~fixed.factor, adjust = "Tukey", max.print = 9999)

所以它看起来像我的一些变量:

ft.hrdepth = lmer(depth ~ hr + (1|FishID), data = all)

anova(ft.hrdepth)

plot(ft.hrdepth, main = "residuals/fitted ft.hrdepth")

lsmeans(ft.hrdepth, pairwise~hr, adjust = "Tukey", max.print = 9999)

我尝试添加 max.print 参数并增加到 10000、10000000、9999、99999999、Inf 和其他组合。我还进入了 RStudio 设置并将显示的行数限制更改为相似的数字:工具 > 全局选项 > 代码 > 显示 > 控制台中显示的行数限制为:1000000

我还把 lsmeans 变成了一个对象,并使用 print() 函数来打印输出或只是对比,但仍然没有运气:

pws = lsmeans(ft.model, pairwise~fixed.factor, adjust = "Tukey", max.print = 9999)

print(pws)

pwss = pws[[contrasts]]

print(pwss)

我已经反复尝试使用相同的确切数据集和不同的数据集,并且完全看不到为什么它有时会省略行并且有时不会省略行。

我无法在控制台或我拥有的 R Markdown 文件的底部查看 lsmeans 输出。(请注意,当我使用 R 脚本文件而不是 R mardown 文件时,我也会得到相同的结果 = 获得所有行的时间只有 1/2)。请帮忙!我可以保证看到所有行的唯一方法是将 lsmeans contrasts 输出导出到 csv 文件,这是非常低效且没有帮助的,因为我还理想地将所有结果合并到一个文档中:

pws = lsmeans(ft.model, pairwise~fixed.factor, adjust = "Tukey", max.print = 9999)

pwss = pws[[contrasts]]

write.csv(pwss,file = "pwss.csv")

仅供参考,我使用 RStudio 版本 1.1.419 和 R i386 3.4.3 我的电脑是 HP windows 10 Pro 64 位操作系统

示例数据集:

结构(列表(X = c(1L、15L、20L、26L、35L、44L、55L、66L、77L、88L、99L、1111L、44444L、77777L、8888L、999L、2222L、4444L、5555777L、6666L、 88888L, 99999L, 11111L, 57890L, 23456L, 9675L, 129873L, 22222L, 333L, 5555L, 123434L, 99944L, 88833L, 77744L, 66655L, 55544L, 44433L, 33322L, 22211L, 134534L, 111111L, 121111L, 131111L, 141111L, 151111L, 161111L, 123111L, 33444L, 5566L, 6677L, 9988L, 33888L, 87878L, 148483L, 139847L, 34231L, 34200L), 原始日期 = 结构 (c(1L, 1L, 1L, 1L, 1, L 1, 1L, 1, L, 1, , 1L, 1L, 12L, 16L, 6L, 1L, 2L, 3L, 13L, 15L, 1L, 18L, 19L, 8L, 14L, 10L, 7L, 23L, 9L, 1L, 4L, 22L, 19L, 18L, 16L , 15L, 13L, 12L, 11L, 9L, 24L, 20L, 21L, 23L, 25L, 27L, 28L, 22L, 11L, 4L, 5L, 7L, 11L, 17L, 26L, 25L, 11L, 11L), .Label = c("10/01/2012", "10/02/2012", "10/03/2012", "10/04/2012",“10/05/2012”、“10/06/2012”、“10/07/2012”、“10/08/2012”、“10/14/2012”、“10/15/2012”、“10 /21/2012”、“10/27/2012”、“11/02/2012”、“11/03/2012”、“11/08/2012”、“11/15/2012”、“11/19 /2012”、“11/20/2012”、“11/25/2012”、“11/29/2012”、“12/03/2012”、“12/04/2012”、“12/08/2012” ", "12/10/2012", "12/13/2012", "12/17/2012", "12/18/2012", "12/23/2012"), 类 = "因素"), OrigTime =结构(c(1L,5L,6L,8L,9L,11L,13L,18L,22L,25L,27L,32L,47L,3L,2L,52L,33L,40L,39L,21L,4L,45L, 48L, 7L, 28L, 14L, 16L, 55L, 23L, 44L, 49L, 19L, 30L, 24L, 41L, 15L, 29L, 42L, 20L, 10L, 17L, 26L, 31L, 38L, 56L, 36L,50L, 43L, 57L, 51L, 46L, 35L, 12L, 37L, 54L, 34L, 58L, 53L), .Label = c("00:03:39", "00:10:58", "00:31 :07”、“00:41:09”、“01:24:09”、“01:48:18”、“02:10:49”、“02:17:49”、“03:33:44” ”、“04:41:53”、“04:47:35”、“05:13:35”、“05:52:04”、“06:04:16”、“06:27:49”、 “06:45:23”、“06:57:49”、“07:03:07”、“07:03:26”、“07:19:39”、“07:25:02”、“08” :12:43”、“08:37:03”、“08:44:48”、“09:27:02”、“09:30:58”、“10:41:49”、“10:52” :47”、“11:09:06”、“11:10:01”、“11:12:46”、“11:15:04”、“11:34:46”、“11:59:42” ", "12:02:58", "12:36:34”、“12:39:42”、“13:20:47”、“13:45:05”、“13:50:05”、“13:51:44”、“14:04”: 15”、“14:47:51”、“14:51:57”、“15:23:14”、“15:37:53”、“15:49:09”、“16:08:00” 、“16:10:44”、“16:37:25”、“17:34:17”、“18:17:16”、“19:34:03”、“20:23:36”、“ 20:40:22"、"21:20:51"、"21:27:49"、"22:44:33")、class = "factor")、OrigDateTime = structure(c(1L, 3L, 4L , 9L, 10L, 11L, 12L, 13L, 14L, 15L, 5L, 6L, 26L, 35L, 31L, 8L, 19L, 27L, 39L, 47L, 2L, 40L, 43L, 34L, 45L, 18L, 33L, 58L , 17L, 7L, 28L, 56L, 42L, 41L, 36L, 46L, 38L, 25L, 24L, 16L, 48L, 44L, 54L, 57L, 50L, 52L, 53L, 55L, 21L, 29L, 30L, 32L, 23L , 37L, 51L, 49L, 22L, 20L), .标签 = c("10/1/2012 0:03:39"、"10/1/2012 0:41:09"、"10/1/2012 1:24:09"、"10/1/2012 1 :48:18”、“2012 年 10 月 1 日 10:41:49”、“2012 年 10 月 1 日 11:15:04”、“2012 年 10 月 1 日 14:51:57”、“10/1/ 2012 18:17:16”、“2012 年 10 月 1 日 2:17:49”、“2012 年 10 月 1 日 3:33:44”、“2012 年 10 月 1 日 4:47:35”、“10/ 1/2012 5:52:04”、“10/1/2012 7:03:07”、“10/1/2012 8:12:43”、“10/1/2012 9:27:02”、“ 2012 年 10 月 14 日 4:41:53”、“2012 年 10 月 14 日 8:37:03”、“2012 年 10 月 15 日 6:04:16”、“2012 年 10 月 2 日 11:34:46” ,“2012 年 10 月 21 日 19:34:03”,“2012 年 10 月 21 日 21:27:49”,“2012 年 10 月 21 日 22:44:33”,“2012 年 10 月 21 日 5:13: 35”、“2012 年 10 月 21 日 7:19:39”、“2012 年 10 月 27 日 14:04:15”、“2012 年 10 月 27 日 15:49:09”、“2012 年 10 月 3 日 13:50:05”、“2012 年 10 月 4 日 16:10:44”、“2012 年 10 月 4 日 17:34:17”、“2012 年 10 月 5 日 15:37:53” 、“2012 年 10 月 6 日 0:10:58”、“2012 年 10 月 7 日 12:02:58”、“2012 年 10 月 7 日 6:45:23”、“2012 年 10 月 8 日 2:10: 49”、“2012 年 11 月 15 日 0:31:07”、“2012 年 11 月 15 日 13:51:44”、“2012 年 11 月 19 日 12:39:42”、“2012 年 11 月 2 日 11: 09:06”、“2012 年 11 月 2 日 13:45:05”、“2012 年 11 月 20 日 15:23:14”、“2012 年 11 月 20 日 8:44:48”、“2012 年 11 月 25 日11:10:01”、“11/25/2012 16:08:00”、“11/29/2012 9:30:58”、“11/3/2012 10:52:47”、“11/8 /2012 6:27:49”、“2012 年 11 月 8 日 7:25:02”、“2012 年 12 月 10 日 6:57:49”、“2012 年 12 月 13 日 11:59:42”、“12 /13/2012 21:20:51”、“12/17/2012 20:23:36”、“12/18/2012 12:36:34”、“12/23/2012 16:37:25”、“2012 年 12 月 3 日 11:12:46”、“2012 年 12 月 4 日 14:47:51”、“2012 年 12 月 4 日 7:03:26”、“2012 年 12 月 8 日13:20:47", "12/8/2012 20:40:22"), class = "factor"), TimeSerial = c(41183.0025347222, 41183.0584375, 41183.0752083333, 41183.0957060185, 41183.1484259259, 41183.1997106482, 41183.2444907407, 41183.2938310185, 41183.3421643519, 41183.3937731481, 41183.4457060185, 41183.4687962963, 41209.6591319444, 41228.0216087963, 41188.0076157407, 41183.7619907407, 41184.4824768519, 41185.5764467593, 41215.572974537, 41221.3090509259, 41183.0285763889, 41233.6411342593, 41238.6722222222, 41190.0908449074, 41216.4533217593, 41197.252962963, 41189.2815162037, 41251.8613657407, 41196.3590625, 41183.6194097222, 41186.6741203704, 41247.2940509259, 41238.4652893519, 41233.3644444444, 41228.5775925926, 41221.2693171296, 41215.4646527778, 41209.5862847222, 41203.3053125, 41196.1957523148, 41253.290150463, 41242.3965046296, 41246.4671990741, 41251.556099537, 41256.8894791667, 41261.5253935185, 41266.6926504629, 41247.6165625, 41203.8943171296, 41186.7321412037, 41187.6513078704, 41189.5020601851, 41203.2177662037, 41232.5275694444, 41260.8497222222, 41256.4997916667, 41203.9476041667, 41203.8153125) , DateNum = c(41183L, 41183L, 41183L, 41183L, 41183L, 41183L, 41183L, 41183L, 41183L, 41183L, 41183L, 41183L, 41209L, 41228L, 41188L, 41183L, 41184L, 41185L, 41215L, 41221L, 41183L, 41233L, 41238L , 41190L, 41216L, 41197L, 41189L, 41251L, 41196L, 41183L, 41186L, 41247L, 41238L, 41233L, 41228L, 41221L, 41215L, 41209L, 41203L, 41196L, 41253L, 41242L, 41246L, 41251L,41256L, 41261L, 41266L, 41247L, 41203L, 41186L, 41187L, 41189L, 41203L, 41232L, 41260L, 41256L, 41203L, 41203L), TimeNum = c(0.0025347222, 0.0584375, 0.0752083333, 0.0957060185, 0.1484259259, 0.1997106482, 0.2444907407, 0.2938310185, 0.3421643519 , 0.3937731481, 0.4457060185, 0.4687962963, 0.6591319444, 0.0216087963, 0.0076157407, 0.7619907407, 0.4824768519, 0.5764467593, 0.572974537, 0.3090509259, 0.0285763889, 0.6411342593, 0.6722222222, 0.0908449074, 0.4533217593, 0.252962963, 0.2815162037, 0.8613657407, 0.3590625, 0.6194097222, 0.6741203704, 0.2940509259, 0.4652893519, 0.3644444444 , 0.5775925926, 0.2693171296, 0.4646527778, 0.5862847222, 0.3053125, 0.1957523148, 0.290150463, 0.3965046296, 0.4671990741, 0.556099537, 0.8894791667, 0.5253935185, 0.692650463, 0.6165625, 0.8943171296, 0.7321412037, 0.6513078704, 0.5020601852, 0.2177662037, 0.5275694444, 0.8497222222, 0.4997916667, 0.9476041667, 0.8153125 ), depth = c(19.34, 19.34, 19.12, 18.46, 19.34, 19.34, 19.34, 19.12, 18.9, 19.12, 18.02, 20.88, 21.1, 20.44 , 20.22, 20.44, 17.36, 18.9, 20, 17.8, 18.9, 17.36, 19.34, 16.92, 20.22, 20.22, 20, 9.89, 19.56, 18.68, 20, 11.9, 20.22, 19.56, 21.1, 16.7, 20, 21.1, 19.56 ,16.92,20.22,18.24,17.36,20.66,20.3,20.3,20.88,19.34,21.32,16.26,16.26,17.58,18.46,28.46,20,18.46,17.58,17.58,9.45,9.8,10.8,10.8,10.68,18.68,18.46) 5593L, 5593L, 5593L, 5593L, 5593L, 5593L, 5593L, 5593L, 7510L, 7513L, 7508L, 7510L, 7509L, 7501L, 7508L, 7511L, 5593L, 7508L, 7501L, 5593L, 7502L, 7513L, 7501L, 7501L, 5593L, 7508L、6747L、7501L、7514L、5593L、7501L、7505L、5593L、7511L、7513L、5593L、7508L,6747L,7510L,7512L,7509L,7508L,7501L,7496L,7510L,7510L,5593L,7501L,7501L,7503L,7503L,7502L,7502L,7514L,7514L,5593L,5593L,5593L,5593L,5593L,7508L,7508L,7508L)= 1 l)= 1l)= , 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L , 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L , 1L, 1L, 1L, 1L, 1L), .Label = "PM", class = "factor"), Temp = c(28.7, 28.7, 28.7, 28.7, 28.7, 28.7, 28.7, 28.6, 28.6, 28.6, 28.6, 28.6, 27, 24.1, 28, 28.6, 28.6, 28.2, 25.3, 25.8, 28.7, 22.6, 22.7, 27.5, 25.5, 27.6, 27.7, 22.5, 27.8, 28.6, 2.7.9,, 2.1.6, 2.7.9, 2.1.6 26, 25.7, 27, 27.9, 27.8, 22.7, 22, 21.7, 22.4, 20.8, 21.4, 21.8, 21.6, 27.8, 28, 27.8, 27.7, 27.9, 22.6, 21.6, 20.7, 27.8, 27)hr = 结构(c(1L, 2L, 2L, 3L, 4L, 5L, 6L, 8L, 9L, 10L, 11L, 12L, 16L, 1L, 1L, 19L, 12L, 14L, 14L, 8L, 1L, 16L, 17L, 3L, 11L, 7L, 7L, 21L, 9L, 15L, 17L, 8L, 12L, 9L, 14L, 7L, 12L, 15L, 8L, 5L, 7L, 10L, 12L, 14L, 22L, 13L, 17L, 15L, 22L, 18L, 16L, 13L, 6L, 13L, 21L, 12L, 23L, 20L), .Label = c("01:00:00", "02:00:00", "03:00:00 ", "04:00:00", "05:00:00", "06:00:00", "07:00:00", "08:00:00", "09:00:00", “10:00:00”、“11:00:00”、“12:00:00”、“13:00:00”、“14:00:00”、“15:00:00”、“16 :00:00”、“17:00:00”、“18:00:00”、“19:00:00”、“20:00:00”、“21:00:00”、“22:00” :00", "23:00:00"),class = "factor"), sqrtdepth = c(4.3977266854592, 4.3977266854592, 4.37264222181509, 4.29651021178817, 4.3977266854592, 4.3977266854592, 4.3977266854592, 4.37264222181509, 4.34741302385683, 4.37264222181509, 4.24499705535823, 4.56946386351834, 4.59347363114234, 4.52106182218293, 4.49666543118342, 4.52106182218293, 4.16653333119993, 4.34741302385683, 4.47213595499958, 4.2190046219458 , 4.34741302385683, 4.16653333119993, 4.3977266854592, 4.11339276024063, 4.49666543118342, 4.49666543118342, 4.47213595499958, 3.14483703870328, 4.4226688774992, 4.32203655699486, 4.47213595499958, 3.44963766213207, 4.49666543118342, 4.4226688774992, 4.59347363114234, 4.08656334834051, 4.47213595499958, 4.59347363114234, 4.4226688774992, 4.11339276024063, 4.49666543118342, 4.27083130081252, 4.16653333119993, 4.5453272709454, 3.20936130717624, 4.56946386351834, 4.3977266854592, 4.61735855224608, 4.03236903073119, 4.19285105864733, 4.29651021178817, 4.47213595499958, 4.29651021178817, 4.19285105864733, 3.07408522978788, 3.286335345031, 4.32203655699486, 4.29651021178817), sqdepth = c(374.0356, 374.0356, 365.5744, 340.7716, 374.0356, 374.0356, 374.0356 , 365.5744, 357.21, 365.5744, 324.7204, 435.9744, 445.21, 417.7936, 408.8484, 417.7936, 301.3696, 357.21, 400, 316.84, 357.21, 301.3696, 374.0356, 286.2864, 408.8484, 408.8484, 400, 97.8121, 382.5936, 348.9424, 400, 141.61 , 408.8484, 382.5936, 445.21, 278.89, 400, 445.21, 382.5936, 286.2864, 408.8484, 332.6976, 301.3696, 426.8356, 106.09, 435.9744, 374.0356, 454.5424, 264.3876, 309.0564, 340.7716, 400, 340.7716, 309.0564, 89.3025,116.64, 348.9424, 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我会喜欢任何输入!

4

1 回答 1

1

您应该指定max.print选项

options('max.print' = 100000)   # or whatever value you want
getOption('max.print')
[1] 100000

当你这样做时lsmeans(stuff, max.print = 10000),你实际上是在将参数(不是选项)传递max.print给包中​​的其他函数emmeans。这就是(参见 参考资料)的...论点的含义。在这种情况下传递给的任何内容都不会更改. 您也可以这样做并且不返回任何错误,但这完全没有意义。lsmeans?lsmeans::lsmeansmax.printgetOption('max.print')lsmeans(stuff, some_random_arg = 'whatever i want')

您可以通过修改您的 Rprofile 使更改从会话转移到会话。这个问题这个问题可能会帮助您朝着正确的方向前进,这样您就不必在max.print每次启动新的 R 会话时都不断更改选项。通过 RStudio 可能有另一种方法,但我对该平台不够熟悉,无法提供帮助。

于 2018-12-05T02:23:53.670 回答