在使用 lme4 和 lmerTest 时,我偶然发现了一个影响 Mac OS 版本的 R 3.3.2(以及 .3!)的问题。
lmerTest 产生错误:
Satterthwaite 近似值的计算错误。返回 lme4 包的输出 从 lme4 返回的摘要 在 lmerTest 中发生了一些计算错误
MacOS 下的 R 3.2 和 Windows 下的任何 R 版本都不会出现此问题。但是,这不是安装问题,因为我在重新安装 R 后以及在另一台 Mac 上都重现了该错误。
这是示例代码:
library(lme4)
#' start of data creation
mydat <-
structure(list(ID = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29,
1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 1, 2, 3, 4, 5,
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
23, 24, 25, 26, 27, 28, 29), sex = c(1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), ROI = structure(c(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, 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, 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("calf",
"DSCAT", "KM", "neck", "SSCAT", "VAT"), class = "factor"),
value = c(0.674,
0.561, 0.543, 0.563, 0.697, 0.608, 0.56, 0.448, 0.626, 0.515,
0.568, 0.528, 0.587, 0.532, 0.547, 0.514, 0.587, 0.572, 0.559,
0.569, 0.462, 0.531, 0.477, 0.582, 0.583, 0.569, 0.563, 0.576,
0.84, 0.638, 0.69, 0.707, 0.704, 0.627, 0.769, 0.637, 0.515,
0.669, 0.699, 0.626, 0.59, 0.639, 0.501, 0.632, 0.624, 0.641,
0.669, 0.656, 0.556, 0.569, 0.633, 0.608, 0.616, 0.664, 0.666,
0.669, 0.545, 0.514, 0.45, 0.585, 0.547, 0.572, 0.577, 0.458,
0.47, 0.537, 0.532, 0.455, 0.62, 0.501, 0.506, 0.44, 0.499, 0.577,
0.457, 0.481, 0.522, 0.516, 0.513, 0.559, 0.571, 0.515, 0.575,
0.521, 0.44, 0.637, 0.521, 0.634, 0.552, 0.581, 0.55, 0.553,
0.522, 0.634, 0.631, 0.512, 0.603, 0.593, 0.58, 0.442, 0.53,
0.463, 0.587, 0.538, 0.48, 0.557, 0.482, 0.53, 0.592, 0.445,
0.526, 0.45, 0.551, 0.51, 0.678, 0.64, 0.599, 0.589, 0.627, 0.621,
0.601, 0.526, 0.619, 0.599, 0.668, 0.615, 0.621, 0.561, 0.532,
0.56, 0.578, 0.686, 0.57, 0.457, 0.563, 0.61, 0.513, 0.638, 0.594,
0.777, 0.562, 0.663, 0.538, 0.471, 0.518, 0.47, 0.535, 0.644,
0.605, 0.474, 0.468, 0.563, 0.539, 0.47, 0.538, 0.453, 0.494,
0.576, 0.418, 0.609, 0.528, 0.453, 0.569, 0.484, 0.486, 0.558,
0.621, 0.465, 0.691, 0.398, 0.539, 0.574), Alter = c(45, 47,
51, 44, 35, 26, 60, 44, 42, 50, 42, 51, 57, 23, 26, 29, 29, 50,
45, 61, 61, 58, 32, 27, 49, 45, 64, 28, 45, 47, 51, 44, 35, 26,
60, 44, 50, 42, 51, 57, 23, 26, 29, 29, 50, 45, 61, 61, 58, 32,
27, 49, 27, 45, 64, 28, 45, 47, 51, 44, 35, 26, 60, 44, 42, 50,
42, 51, 57, 23, 26, 29, 29, 50, 45, 61, 61, 58, 32, 27, 49, 27,
45, 64, 28, 45, 47, 51, 44, 35, 26, 60, 44, 42, 50, 42, 51, 57,
23, 26, 29, 29, 50, 45, 61, 61, 58, 32, 27, 49, 27, 45, 64, 28,
45, 47, 51, 44, 35, 26, 60, 44, 42, 50, 42, 51, 57, 23, 26, 29,
29, 50, 45, 61, 61, 58, 32, 27, 49, 27, 45, 64, 28, 45, 47, 51,
44, 35, 26, 60, 44, 42, 50, 42, 51, 57, 23, 26, 29, 29, 50, 45,
61, 61, 58, 32, 27, 49, 27, 45, 64, 28),
BMI = c(29.7506923675537,
28.8, 28.8385677337646, 41.48, 27.7186069488525, 29.54, 38.06,
35.8453826904297, 35.57, 31.77, 31.75, 32.78, 30.5336246490479,
29.1074104309082, 36.4690246582031, 31.7769088745117, 31.5393238067627,
31.5596752166748, 27.593786239624, 30.8192825317383, 27.0799140930176,
31.481481552124, 29.0328979492188, 24.52, 29.4029197692871, 35.6112785339355,
28.2401905059814, 28.8979587554932, 29.7506923675537, 28.8, 28.8385677337646,
41.48, 27.7186069488525, 29.54, 38.06, 35.8453826904297, 31.77,
31.75, 32.78, 30.5336246490479, 29.1074104309082, 36.4690246582031,
31.7769088745117, 31.5393238067627, 31.5596752166748, 27.593786239624,
30.8192825317383, 27.0799140930176, 31.481481552124, 29.0328979492188,
24.52, 29.4029197692871, 23.0956573486328, 35.6112785339355,
28.2401905059814, 28.8979587554932, 29.7506923675537, 28.8, 28.8385677337646,
41.48, 27.7186069488525, 29.54, 38.06, 35.8453826904297, 35.57,
31.77, 31.75, 32.78, 30.5336246490479, 29.1074104309082, 36.4690246582031,
31.7769088745117, 31.5393238067627, 31.5596752166748, 27.593786239624,
30.8192825317383, 27.0799140930176, 31.481481552124, 29.0328979492188,
24.52, 29.4029197692871, 23.0956573486328, 35.6112785339355,
28.2401905059814, 28.8979587554932, 29.7506923675537, 28.8, 28.8385677337646,
41.48, 27.7186069488525, 29.54, 38.06, 35.8453826904297, 35.57,
31.77, 31.75, 32.78, 30.5336246490479, 29.1074104309082, 36.4690246582031,
31.7769088745117, 31.5393238067627, 31.5596752166748, 27.593786239624,
30.8192825317383, 27.0799140930176, 31.481481552124, 29.0328979492188,
24.52, 29.4029197692871, 23.0956573486328, 35.6112785339355,
28.2401905059814, 28.8979587554932, 29.7506923675537, 28.8, 28.8385677337646,
41.48, 27.7186069488525, 29.54, 38.06, 35.8453826904297, 35.57,
31.77, 31.75, 32.78, 30.5336246490479, 29.1074104309082, 36.4690246582031,
31.7769088745117, 31.5393238067627, 31.5596752166748, 27.593786239624,
30.8192825317383, 27.0799140930176, 31.481481552124, 29.0328979492188,
24.52, 29.4029197692871, 23.0956573486328, 35.6112785339355,
28.2401905059814, 28.8979587554932, 29.7506923675537, 28.8, 28.8385677337646,
41.48, 27.7186069488525, 29.54, 38.06, 35.8453826904297, 35.57,
31.77, 31.75, 32.78, 30.5336246490479, 29.1074104309082, 36.4690246582031,
31.7769088745117, 31.5393238067627, 31.5596752166748, 27.593786239624,
30.8192825317383, 27.0799140930176, 31.481481552124, 29.0328979492188,
24.52, 29.4029197692871, 23.0956573486328, 35.6112785339355,
28.2401905059814, 28.8979587554932)), .Names = c("ID", "sex",
"ROI", "value", "Alter", "BMI"), row.names = c(NA, -172L), class = c("tbl_df","tbl", "data.frame"))
#' end of data creation
library(lmerTest)
mod <- lmer(value~Alter+ROI+BMI+(1|ID),data=mydat,REML=F)
summary(mod)
sessionInfo()
系统信息如下:
R version 3.3.3 (2017-03-06)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: macOS Sierra 10.12.3
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lmerTest_2.0-33 lme4_1.1-12 Matrix_1.2-8
loaded via a namespace (and not attached):
[1] Rcpp_0.12.9 Formula_1.2-1 knitr_1.15.1 magrittr_1.5 cluster_2.0.5 splines_3.3.3 MASS_7.3-45 munsell_0.4.3 [9] colorspace_1.3-2 lattice_0.20-34 minqa_1.2.4 stringr_1.1.0 plyr_1.8.4 tools_3.3.3 nnet_7.3-12 grid_3.3.3 [17] data.table_1.10.0 checkmate_1.8.2 htmlTable_1.8 gtable_0.2.0 nlme_3.1-131 latticeExtra_0.6-28 htmltools_0.3.5 digest_0.6.11 [25] survival_2.40-1 lazyeval_0.2.0 assertthat_0.1 tibble_1.2 gridExtra_2.2.1 RColorBrewer_1.1-2 nloptr_1.0.4 ggplot2_2.2.1 [33] base64enc_0.1-3 acepack_1.4.1 rpart_4.1-10 stringi_1.1.2 backports_1.0.4 scales_0.4.1 Hmisc_4.0-2 foreign_0.8-67