0

我有两个问题，显然是相关的：

  • 首先，当在 Mac 和 PC 上运行时，R 中的 lsmeans 给了我两种不同的输出。

  • 其次，该脚本在 PC 和 Mac 上的功能之外工作。但是，它不适用于 Mac 上的函数。

我已经为这个项目苦苦挣扎了好几个星期，我正在竭尽全力试图让它发挥作用。

一些背景：

  • 实验数据来自州范围内的研究试验，具有多年和地点。

  • 该脚本被内置到自动为指定的数据子集生成输出的函数中。

  • 原始数据和功能通过 Box Sync 共享，需要多人同时使用 Mac 和 PC 访问和使用。

  • 我在两台计算机上打开相同的数据和脚本，两台计算机都运行 R 3.3。所有软件包都已更新到最新版本。

这是示例数据：

df<-structure(list(YEAR=structure(c(3L,3L,3L,3L,3L,3L,3L,
3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,
3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,
3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,
3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,
3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,
3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,
3L,3L,3L,3L,3L,3L,3L,3L,3L,2L,2L,2L,2L,2L,2L,2L,
2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,
2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,
2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,
2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,
2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,
2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,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,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=c("2014","2015","2016"),class="factor"),
LOCATION=structure(c(1L,1L,1L,1L,1L,1L,1L,1L,1L,
1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,
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1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,
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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,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,1L,1L,1L,1L,1L,1L,1L,1L,
1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,
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1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,
1L,1L,1L,1L),.Label="KERN",class="factor"),LAT=c(35.3695756,
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35.394386,35.394386,35.394386,35.394386,35.394386,35.394386,
35.394386,35.394386,35.394386,35.394386,35.394386,35.394386,
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35.394386,35.394386,35.394386,35.394386,35.394386,35.394386,
35.394386,35.394386,35.394386,35.394386,35.394386,35.394386,
35.394386,35.394386,35.394386,35.394386,35.137156,35.137156,
35.137156,35.137156,35.137156,35.137156,35.137156,35.137156,
35.137156,35.137156,35.137156,35.137156,35.137156,35.137156,
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35.137156,35.137156,35.137156,35.137156,35.137156,35.137156,
35.137156,35.137156,35.137156,35.137156,35.137156,35.137156,
35.137156,35.137156,35.137156,35.137156),LONG=c(-119.3332198,
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"1756","1758","1770","1771","1776","1782","1783","1784",
"1785","1786","1796","1797","1798","1799","1800","1801",
"1810","1812","1813","1822","1823","1824","1825","1826",
"1827","1832","1833"),class="factor"),BLOCK=c(1L,
1L,1L,2L,2L,2L,2L,3L,3L,3L,3L,4L,4L,4L,4L,4L,
4L,3L,3L,3L,3L,2L,2L,2L,2L,1L,1L,1L,1L,1L,1L,
2L,2L,2L,2L,3L,3L,3L,3L,4L,4L,4L,4L,4L,4L,3L,
3L,3L,3L,2L,2L,2L,2L,1L,1L,1L,1L,1L,1L,1L,2L,
2L,2L,3L,3L,3L,4L,4L,4L,4L,4L,4L,4L,4L,3L,3L,
3L,2L,2L,2L,1L,1L,1L,1L,1L,1L,1L,1L,2L,2L,2L,
3L,3L,3L,4L,4L,4L,4L,4L,4L,4L,4L,3L,3L,3L,2L,
2L,2L,1L,1L,1L,1L,2L,2L,2L,2L,2L,2L,2L,2L,2L,
2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,2L,
2L,2L,2L,2L,2L,2L,2L,2L,2L,3L,3L,3L,3L,3L,3L,
3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,
3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,3L,4L,4L,4L,
4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,
4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,4L,
1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,
1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,
1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,
1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,
1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,
1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L,
1L,2L,3L,1L,2L,3L,1L,2L,3L,1L,2L,3L),YIELD=c(6161.570159,
5560.05164,5027.418215,6479.200167,5741.388915,7172.181818,
4689.599352,5987.74792,5666.825975,6071.348724,5478.883003,
7124.783728,5656.242954,3610.181643,4331.324396,4904.546884,
4447.489172,4106.787872,4658.348018,6259.997274,6153.207065,
6313.497876,5676.779979,5357.37221,6466.719177,6230.109145,
6688.341036,6442.7157,6100.712992,4621.929508,5755.886769,
6604.254137,4954.200669,5867.5903,3991.831947,6005.947762,
6650.642431,5068.746236,3389.123183,4665.019064,5061.072647,
4478.621635,3941.029714,4229.627327,5402.926203,4245.353341,
5637.312903,5518.179277,4389.871419,4215.083338,4897.365913,
6086.120238,4502.709961,5024.207751,5723.2024,4795.406733,
5589.303172,4157.52343,4694.93871,5977.432504,4828.809253,
4207.050908,5533.320623,3808.889598,5191.0883,5565.5,
5967.716748,4695.892909,3745.651761,2326.036846,5520.691951,
3616.957981,6062.2518,5539.725094,4599.123634,4539.368454,
6400.747636,3883.129209,6770.653931,6197.140663,5676.23605,
5778.724469,5696.726085,4583.617968,4006.825885,5611.114514,
5077.577925,6096.838084,3900.072863,4227.004389,3784.270665,
4244.82975,5221.064527,6211.31643,5790.782248,6239.067852,
2557.838189,2827.312193,3869.848855,4470.534568,7274.162574,
5137.88216,6012.906525,5530.157816,6173.018569,5228.493966,
5781.589525,5887.075038,5244.684963,5063.663862,4275.956581,
5568.79725,6649.127517,4069.433962,5760,5241.904762,
5195.294118,5482.105263,7206.233766,5491.2,4979.2,6739.328859,
5202.162162,5734.736842,6568.421053,6227.027027,5296.107383,
5222.4,4458.947368,5564.081633,6829.530201,6778.77551,
6973.333333,5667.945205,5942.857143,6044.903226,6349.803922,
6101.917808,7069.090909,5682.162162,5476.510067,5220.392157,
5166.315789,6669.473684,6055.384615,6233.6,4869.933775,
5733.146853,4718.873239,4746.335404,5397.482517,6014.304636,
5681.632653,7018.378378,6003.2,4618.039216,7048.767123,
5409.811321,6632.727273,5952,4966.4,5322.531646,5423.376623,
6662.781457,5823.157895,7210.06993,5216.842105,6063.157895,
7000.816327,5687.088608,6896.326531,6630.4,5734.228188,
6136.470588,4522.95302,5446.27451,5987.368421,5060.662252,
5027.913669,5844.528302,4882.285714,5169.230769,5290.06993,
4463.046358,5570.704225,5106.938776,6088.767123,5435.492958,
4940.8,6170.482759,5368.421053,6048.627451,6006.486486,
4539.428571,5347.555556,6637.350993,6271.168831,5838.367347,
6057.464789,5018.482759,6498.461538,5450.738255,6168.510638,
5106.666667,6404.475524,5357.837838,4850.526316,3458.630137,
4717.350993,5859.74026,5354.366197,7409.079755,6442.105263,
7450,6203.076923,6362.790698,6693.333333,7211.707317,
7481.806452,6858.666667,7572.670807,8274.285714,7886.769231,
6328.888889,6395.761589,6507.789474,6295.272727,6681.156069,
6965.106383,7521.019108,9086.369427,8196.923077,5969.454545,
5376,6107.234043,5934.545455,6514.682081,7135.828877,
5954.43038,5539.459459,5513.142857,5701.818182,6594.206897,
7101.176471,8905.030675,6950.614525,8660.425532,7515.092025,
7316.078431,6471.111111,3473.121019,4295.028902,5422.417582,
6970.181818,7819.441341,8506.490066,5772.715232,5268.837209,
5335.578947,7600.941176,8048.813559,6842.553191,7773.658537,
8278.441558,7326.315789,7333.012048,7232,6662.295082,
8436.363636,7949.662921,7182.222222,9399.215686,8777.142857,
7222.38342,8258.313253,7755.505618,7975.384615,6864,
7437.241379,7251.06383,7655.541401,7718.145695,7926.857143,
7959.493671,8582.013423,8792.275862,5636.923077,6573.774834,
7036.595745,8457.423313,6860.8,8353.507853,7821.349693,
9017.142857,8494.545455,7289.101796,7855.542857,7302.47191,
7917.037037,8035.955056,8543.492063,6984,5975.730337,
7298.931298,8024.615385,7200,7923.380282,7749.818182,
6758.843931,8354.198473,1110.943396,1356.8,1421.837838
),PROTEIN=c(15.03867,14.82126,15.748876,15.437255,
16.589528,14.640085,16.575034,15.582195,15.524219,14.973447,
15.408267,NA,NA,NA,NA,NA,15.002435,16.792444,17.343216,
15.770617,15.734382,15.212598,14.995188,16.219931,15.47349,
15.524219,15.567701,15.879322,15.198104,17.002607,15.661912,
15.03867,16.430094,15.959039,17.162041,14.828507,15.502478,
16.988113,17.633096,NA,NA,NA,NA,NA,NA,16.05325,15.25608,
15.190857,16.915643,17.517144,15.937298,15.567701,16.748962,
16.082238,16.647504,16.99536,15.632924,16.248919,17.335969,
15.306809,16.661998,16.857667,15.988027,17.437427,15.748876,
15.814099,NA,NA,NA,NA,NA,NA,NA,NA,14.379193,16.495317,
15.828593,16.328636,15.285068,15.357538,15.451749,15.785111,
15.321303,16.517058,16.980866,15.379279,16.473576,15.487984,
15.922804,17.379451,16.872161,15.560454,15.83584,15.705394,
NA,NA,NA,NA,NA,NA,NA,NA,16.843173,15.089399,16.212684,
15.937298,15.683653,15.959039,16.234425,15.966286,17.28524,
16.183696,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,
NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,11.56,NA,
NA,12.07,NA,NA,11.4,NA,NA,11.7,NA,NA,11.3,NA,
NA,12.07,NA,NA,12.5,NA,NA,12.57,NA,NA,12.08,NA,
NA,11.87,NA,NA,12.37,NA,NA,11.26,NA,NA,11.3,NA,
NA,11.32,NA,NA,11.98,NA,NA,12,NA,NA,11.99,NA,
NA,11.53,NA,NA,11.82,NA,NA,12.16,NA,NA,10.82,NA,
NA,10.3,NA,NA,11.24,NA,NA,10.79,NA,NA,11.17,NA,
NA,13.06,NA,NA,11.07,NA,NA,11.65,NA,NA,12,NA,
NA,10.26,NA,NA,11.38,NA,NA,10.61,NA,NA,11.24,NA,
NA,14.12,NA,NA)),.Names=c("YEAR","LOCATION","LAT",
"LONG","PLOT_NUM","ENTRY_NUM","BLOCK","YIELD","PROTEIN"),row.names=c(NA,
-313L),class="data.frame")

数据设置：

df$ENTRY_NUM <- as.factor(df$ENTRY_NUM)
df$YEAR <- as.factor(df$YEAR)
df$LOCATION <- as.factor(df$LOCATION)
df$BLOCK <- as.factor(df$BLOCK)

require(nlme)
require(lsmeans)

在所有年份的数据中生成 lsmeans 估计值。这在 PC 和 Mac 上都可以正常工作，无论是在功能内还是外。

m.lme <- lme((PROTEIN) ~ ENTRY_NUM,
             data = df, random = ~1|YEAR/LOCATION,
             na.action = na.exclude)

org.lsm <- lsmeans(m.lme, "ENTRY_NUM")
YIELD.lsm <<- data.frame(summary(org.lsm))

# A function to do the same thing.

Data_3Yr <- df

Three_yr_fn_Y <- function(Data_3Yr) 
{

  m.lme <- lme((PROTEIN) ~ ENTRY_NUM,
               data = df, random = ~1|YEAR/LOCATION,
               na.action = na.exclude)

  org.lsm <- lsmeans(m.lme, "ENTRY_NUM")
  YIELD.lsm <<- data.frame(summary(org.lsm))
}

ThreeYear_r <- Three_yr_fn_Y(Data_3Yr)

单年是它开始失败的地方。数据是按年份划分的，并且每年运行以获取 lsmeans 表。这在 Mac 和 PC 上运行良好。但是，当每年在 Mac 上的某个函数中运行时，它会失败。

# Individual years.
Y1 <- subset(df, YEAR == "2014")
Y2 <- subset(df, YEAR == "2015")
Y3 <- subset(df, YEAR == "2016")

# These all work fine for me.
# Single years
Data_1Yr <- Y1

m.lme <- lme((YIELD) ~ ENTRY_NUM,
             data = Data_1Yr, random = ~1|LOCATION/BLOCK,
             #weights = varPower(form = ~fitted(.)),
             na.action = na.exclude)

org.lsm <- lsmeans(m.lme, "ENTRY_NUM")
YIELD.lsm <- data.frame(summary(org.lsm))

Data_1Yr <- Y2

m.lme <- lme((YIELD) ~ ENTRY_NUM,
             data = Data_1Yr, random = ~1|LOCATION/BLOCK,
             #weights = varPower(form = ~fitted(.)),
             na.action = na.exclude)

org.lsm <- lsmeans(m.lme, "ENTRY_NUM")
YIELD.lsm <- data.frame(summary(org.lsm))

Data_1Yr <- Y3

m.lme <- lme((YIELD) ~ ENTRY_NUM,
             data = Data_1Yr, random = ~1|LOCATION/BLOCK,
             #weights = varPower(form = ~fitted(.)),
             na.action = na.exclude)

org.lsm <- lsmeans(m.lme, "ENTRY_NUM")
YIELD.lsm <- data.frame(summary(org.lsm))

# A function to do the same thing.
Single_yr_fn_Y <- function(Data_1Yr) 
{
  m.lme <- lme((YIELD) ~ ENTRY_NUM,
               data = Data_1Yr, random = ~1|LOCATION/BLOCK,
               na.action = na.exclude)

  org.lsm <- lsmeans(m.lme, "ENTRY_NUM")
  YIELD.lsm <- data.frame(summary(org.lsm))
}

# Each year subset run in the function.
Y1_r <- Single_yr_fn_Y(Y1)
Y2_r <- Single_yr_fn_Y(Y2)
Y3_r <- Single_yr_fn_Y(Y3)

在 Mac 上，返回以下错误消息。

Error in `contrasts<-`(`*tmp*`, ncol(ca), value = ca) : 
  wrong number of contrast matrix rows
Called from: `contrasts<-`(`*tmp*`, ncol(ca), value = ca)

在 PC 上，它会返回以下警告，但无论如何都会生成输出：

Warning message:
In qt((1 - level)/adiv, df) : NaNs produced

请注意：Mac 不会始终生成错误消息。如果它为同一个数据子集运行多次，有时它会生成与 PC 相同的消息（！？！）。

最后，在 Mac 上，调试返回以下消息。我用谷歌搜索了这个，只返回包的 GitHub 脚本。

  stop("wrong number of contrast matrix rows")

如果您能提出解决方案，您将成为我最喜欢的新人！

更新

df2<-structure(list(YEAR = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
 3L, 3L, 3L, 3L, 3L), .Label = c("2014", "2015", "2016"), class = "factor"), 
LOCATION = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 2L, 2L, 2L, 2L, 
 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("DAVIS", 
 "DELTA"), class = "factor"), ENTRY_NUM = structure(c(1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 
3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 
7L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 
7L, 7L), .Label = c("1723", "1748", "1766", "1807", "1820", 
"3178", "3179"), class = "factor"), BLOCK = c(1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 
2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L
), PROTEIN = c(NA, NA, 11.17, NA, NA, NA, 11.87, NA, NA, 
 NA, 7.98, NA, 11.87, NA, NA, NA, 11.6, NA, NA, NA, 11.79, 
 NA, NA, NA, 12.06, NA, NA, NA, 13.46, NA, NA, NA, 11.34, 
 NA, NA, NA, 12.41, NA, NA, NA, 13.27, NA, NA, 13.42, NA, 
 NA, 12.45, NA, NA, 12.97, NA, NA, 13.074733, 12.328292, 13.024004, 
 12.922546, 13.58927, 13.125462, 14.277735, 12.987769, 12.364527, 
 12.038412, 12.480479, 13.386354, 13.538541, 13.074733, 13.364613, 
 13.364613, 13.096474, 12.929793, 13.248661, 13.176191, 10.357108, 
 10.378849, 10.871645, 11.118043, 13.813927, 14.263241, 15.922804, 
 NA, 12.712383, 12.734124, 12.400762, NA, 13.582023, 13.176191, 
 13.212426, NA, 11.973189, 11.792014, 11.799261, NA, 12.422503, 
 12.060153, 12.154364, NA, 12.581937, 12.748618, 12.480479, 
 NA, 10.770187, 10.704964, 11.089055, NA, 13.726963, 12.603678, 
 12.915299, NA)), .Names = c("YEAR", "LOCATION", "ENTRY_NUM", 
 "BLOCK", "PROTEIN"), row.names = c(6125L, 6126L, 6127L, 6128L, 
6165L, 6166L, 6167L, 6168L, 6181L, 6182L, 6183L, 6184L, 6541L, 
6542L, 6543L, 6544L, 6581L, 6582L, 6583L, 6584L, 6597L, 6598L, 
6599L, 6600L, 3411L, 3472L, 3533L, 3594L, 3417L, 3478L, 3539L, 
3600L, 3421L, 3482L, 3543L, 3604L, 3439L, 3500L, 3561L, 3622L, 
3899L, 3960L, 4021L, 3905L, 3966L, 4027L, 3909L, 3970L, 4031L, 
3927L, 3988L, 4049L, 501L, 431L, 399L, 445L, 541L, 509L, 397L, 
406L, 427L, 550L, 512L, 402L, 545L, 430L, 515L, 405L, 465L, 416L, 
400L, 558L, 457L, 454L, 553L, 404L, 502L, 546L, 398L, 525L, 905L, 
855L, 843L, 841L, 823L, 821L, 845L, 892L, 932L, 830L, 858L, 867L, 
854L, 847L, 870L, 918L, 904L, 802L, 911L, 812L, 875L, 872L, 895L, 
888L, 930L, 950L, 832L, 893L), class = "data.frame")
4

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这绝对是一个范围界定问题。我没有理由相信它与 PC 与 Mac 有任何关系。在我的 Windows PC 上，我按所示顺序输入了您提供的代码，它是这样结束的：

> # Each year subset run in the function.
> Y1_r <- Single_yr_fn_Y(Y1)
 Error in `contrasts<-`(`*tmp*`, ncol(ca), value = ca) : 
  wrong number of contrast matrix rows 

> Y2_r <- Single_yr_fn_Y(Y2)
 Error in `contrasts<-`(`*tmp*`, ncol(ca), value = ca) : 
  wrong number of contrast matrix rows 

> Y3_r <- Single_yr_fn_Y(Y3)
Warning message:
In qt((1 - level)/adiv, df) : NaNs produced

特别注意，我确实收到了第 1 年和第 2 年的对比错误消息，但我没有收到第 3 年的错误消息（只有警告）。发生这种情况是因为数据框Data_1Yr存在于全局环境中，并且功能正在访问它。此外，它最后一次定义是在第 3 年，所以它是正确的数据集。如果我删除该数据框，我会得到不同的结果：

> remove(list = "Data_1Yr")
> Y3_r <- Single_yr_fn_Y(Y3)
 Error in ref.grid(object = list(modelStruct = list(reStruct = list(BLOCK = -0.92001152041278,  : 
  Perhaps a 'data' or 'params' argument is needed

正在发生的事情是lsmeans由于某种原因无法访问函数体内的数据集，并且当数据在全局环境中可用时它有点工作。这似乎是该lsmeans功能的范围问题，我将对其进行调查。但这不是由于 Mac 和 PC 之间的差异，而是由于您的全球环境中的差异。请参阅最后的解决方法，了解您可以对现在存在的包执行什么操作。

现在，警告信息也很重要：

> head(YIELD.lsm)
  ENTRY_NUM   lsmean       SE df lower.CL upper.CL
1       951 6371.463 394.5384  0      NaN      NaN
2      1215 5076.817 394.5384  0      NaN      NaN
3      1375 4960.407 394.5384  0      NaN      NaN
4      1429 4472.692 394.5384  0      NaN      NaN
5      1431 4918.525 394.5384  0      NaN      NaN
6      1440 5956.541 394.5384  0      NaN      NaN

您收到警告是因为您的估计值为零，因此无法获得置信限。只看ENTRY_NUM一层...

> subset(df, ENTRY_NUM == "951")
    YEAR LOCATION      LAT      LONG PLOT_NUM ENTRY_NUM BLOCK    YIELD  PROTEIN
37  2016     KERN 35.36958 -119.3332       37       951     3 6650.642 15.50248
67  2016     KERN 35.36958 -119.3332       67       951     4 5967.717       NA
79  2016     KERN 35.36958 -119.3332       79       951     2 6770.654 15.28507
88  2016     KERN 35.36958 -119.3332       88       951     1 6096.838 15.48798
114 2015     KERN 35.39439 -119.3419      136       951     2 4069.434       NA
147 2015     KERN 35.39439 -119.3419       75       951     3 4869.934       NA
180 2015     KERN 35.39439 -119.3419       85       951     4 5844.528       NA
215 2014     KERN 35.13716 -119.0326      198       951     1 6203.077 12.07000
216 2014     KERN 35.13716 -119.0326      192       951     2 6362.791       NA
217 2014     KERN 35.13716 -119.0326      232       951     3 6693.333       NA

你注意到的是只LOCATION涉及一个 - 如果你阻止它，那么 df 为零！这是您的模型中的错误，而不是lsmeans. 也许你想要的是random = ~ 1 | BLOCK，但我不确定。确保您正确理解如何指定适当的模型。

一种解决方法

Single_yr_fn_Y <- function(data) 
{
    m.lme <- lme(YIELD ~ ENTRY_NUM,
                 data = data, random = ~1|BLOCK,
                 na.action = na.exclude)

    org.lsm <- lsmeans(m.lme, "ENTRY_NUM", data = data)
    org.lsm
}

我将模型的随机部分固定为更合理的部分。不必在模型中为响应变量加上括号。调用lsmeans明确指定要使用的数据集，这解决了范围界定问题。顺便说一句，我没有看到需要将结果包装在summaryor中data.frame，因为print结果的方法显示了您想要看到的内容。这是第一年结果的第一部分：

> Single_yr_fn_Y(Y1)
 ENTRY_NUM   lsmean       SE df  lower.CL upper.CL
 878       7100.395 335.3031  2 5657.7021 8543.088
 951       6419.734 335.3031  2 4977.0407 7862.427
 1166      7184.060 335.3031  2 5741.3672 8626.753
 1210      7911.242 335.3031  2 6468.5490 9353.935
    . . .
于 2017-03-10T23:38:48.350 回答