时间序列混合效应模型（lme4）
Time-series mixed effect model (lme4)
我正在尝试 运行 一个使用时间作为固定效应的混合效应模型。我在不规则的时间间隔 (3-7) 内重复采取了措施，并想说明我的变量与时间的固定线性关系。同时我对确定处理效果（干旱和竞争）很感兴趣。
下面是我尝试 运行 使用 lme4 和 lmer
的数据集之一
> BRCAgassydays
Species Drought Competition Treatment Time assimilation conductance intercellularcarbon
1 BRCA Control Invasive CxI 0 7.811799 0.16297273 297.5562
2 BRCA Control Invasive CxI 21 5.405663 0.19472180 314.8806
3 BRCA Control Invasive CxI 29 7.604270 0.14460617 291.1411
4 BRCA Control Invasive CxI 34 7.513887 0.22543327 326.1150
5 BRCA Control Invasive CxI 42 6.683802 0.18940180 318.6928
6 BRCA Control Invasive CxI 55 6.071712 0.13774260 301.6228
7 BRCA Control Invasive CxI 70 6.331053 0.13962460 306.8503
8 BRCA Control Invasive CxI 78 4.941679 0.13157067 312.1904
9 BRCA Control Invasive CxI 82 4.729761 0.11871700 313.6009
10 BRCA Control Invasive CxI 88 6.831296 0.16134250 305.1969
11 BRCA Control Invasive CxI 97 4.652050 0.09225767 295.9346
12 BRCA Control Invasive CxI 104 5.873223 0.16265633 313.8243
13 BRCA Control None CxN 0 7.644604 0.17184619 301.8665
14 BRCA Control None CxN 21 8.250492 0.23745253 317.0501
15 BRCA Control None CxN 29 7.463330 0.13094473 286.2365
16 BRCA Control None CxN 34 7.928604 0.24988353 328.3139
17 BRCA Control None CxN 42 7.415549 0.18531760 309.8957
18 BRCA Control None CxN 55 6.764483 0.13508080 291.4254
19 BRCA Control None CxN 70 5.666392 0.11958453 304.4248
20 BRCA Control None CxN 78 7.267130 0.16822320 303.4725
21 BRCA Control None CxN 82 5.870902 0.11838573 297.4116
22 BRCA Control None CxN 88 7.400286 0.18886560 306.0608
23 BRCA Control None CxN 97 5.397562 0.13183013 313.4487
24 BRCA Control None CxN 104 5.756836 0.14453173 311.5725
25 BRCA Drought Invasive DxI 0 6.932256 0.12224355 285.7301
26 BRCA Drought Invasive DxI 21 8.448956 0.22943413 311.6504
27 BRCA Drought Invasive DxI 29 7.410476 0.12434440 280.5574
28 BRCA Drought Invasive DxI 34 8.208636 0.26668580 327.9786
29 BRCA Drought Invasive DxI 42 5.324922 0.12691907 312.5066
30 BRCA Drought Invasive DxI 55 5.196439 0.10962533 295.7930
31 BRCA Drought Invasive DxI 70 4.643326 0.08082647 289.0008
32 BRCA Drought Invasive DxI 78 3.675965 0.07471427 298.9459
33 BRCA Drought Invasive DxI 82 4.113252 0.09586540 310.3563
34 BRCA Drought Invasive DxI 88 4.340185 0.09807740 310.9444
35 BRCA Drought Invasive DxI 97 4.410509 0.09351200 304.7900
36 BRCA Drought Invasive DxI 104 2.961152 0.06973620 309.7580
37 BRCA Drought None DxN 0 5.299206 0.10402717 299.4199
38 BRCA Drought None DxN 21 8.931698 0.22541307 310.6568
39 BRCA Drought None DxN 29 6.490820 0.10163740 271.2123
40 BRCA Drought None DxN 34 6.748470 0.19204680 323.1626
41 BRCA Drought None DxN 42 4.175082 0.08008673 295.1853
42 BRCA Drought None DxN 55 4.740064 0.10071627 293.5214
43 BRCA Drought None DxN 70 5.147252 0.09366540 284.0992
44 BRCA Drought None DxN 78 5.187626 0.09444033 291.6765
45 BRCA Drought None DxN 82 4.608225 0.09833660 303.1695
46 BRCA Drought None DxN 88 6.398861 0.14120963 303.1060
47 BRCA Drought None DxN 97 3.980145 0.11448827 324.5742
48 BRCA Drought None DxN 104 5.233092 0.12851007 313.0436
transpiration seA seG seCi seE
1 2.822450 0.43644138 0.05761956 105.2020 0.9978866
2 2.320922 0.45469707 0.08708224 140.8189 1.0379479
3 1.790830 0.47508738 0.05903522 118.8578 0.7311035
4 2.400032 0.43793202 0.10081682 145.8431 1.0733268
5 2.805418 0.67185683 0.08470306 142.5237 1.2546211
6 3.114693 0.74197040 0.06160036 134.8898 1.3929330
7 1.795997 1.23465637 0.06244202 137.2276 0.8031942
8 2.715327 0.72150617 0.05884019 139.6158 1.2143311
9 2.544619 0.74568747 0.05309186 140.2466 1.1379881
10 3.019007 1.51528270 0.08067125 152.5985 1.5095034
11 1.784082 0.03402206 0.04612883 147.9673 0.8920409
12 3.453956 0.33488091 0.07274212 140.3465 1.5446562
13 2.598881 0.46217235 0.06075680 106.7259 0.9188432
14 2.909069 0.84976554 0.10619200 141.7891 1.3009752
15 2.025599 0.50776426 0.05856027 128.0088 0.9058755
16 2.487442 0.54267771 0.11175131 146.8264 1.1124180
17 2.875295 0.11954494 0.08287655 138.5896 1.2858709
18 3.265247 0.84720688 0.06040997 130.3294 1.4602628
19 1.483867 0.44247329 0.05347983 136.1429 0.6636057
20 3.203109 0.74145804 0.07523170 135.7170 1.4324737
21 2.539916 1.02080831 0.05294371 133.0065 1.1358850
22 3.553893 0.34056152 0.08446326 136.8745 1.5893495
23 1.926768 0.80018829 0.05895623 140.1785 0.8616768
24 3.047124 0.42481340 0.06463656 139.3395 1.3627154
25 1.899871 0.55859666 0.04321962 101.0208 0.6717057
26 2.797467 0.88094476 0.10260606 139.3743 1.2510653
27 2.110417 0.64763904 0.05560851 125.4691 0.9438074
28 2.689897 0.42303271 0.11926552 146.6765 1.2029587
29 2.197370 0.97036269 0.05675993 139.7572 0.9826938
30 2.669697 0.54886662 0.04902594 132.2827 1.1939247
31 1.295458 0.61865489 0.03614669 129.2451 0.5793462
32 1.736331 0.69632750 0.03341324 133.6927 0.7765110
33 2.171248 0.58167406 0.04287231 138.7956 0.9710117
34 2.127337 1.02370322 0.04386155 139.0585 0.9513742
35 1.719165 0.61438695 0.04181984 136.3062 0.7688341
36 1.628725 1.18038670 0.03118698 138.5280 0.7283880
37 1.572679 0.73317733 0.03677916 105.8609 0.5560261
38 2.887991 0.75700712 0.10080779 138.9300 1.2915488
39 1.712095 0.86022203 0.04545363 121.2898 0.7656720
40 2.074472 0.31120772 0.08588594 144.5227 0.9277320
41 1.466688 0.40419493 0.03581588 132.0109 0.6559227
42 2.380746 0.70855853 0.04504168 131.2668 1.0647018
43 1.520280 0.62697587 0.04188844 127.0530 0.6798899
44 1.772095 0.45202487 0.04223500 130.4417 0.7925049
45 2.191443 0.55772642 0.04397746 135.5815 0.9800431
46 2.756795 1.13005016 0.06315087 135.5531 1.2328762
47 2.051825 0.83631901 0.05120071 145.1540 0.9176041
48 2.867810 1.07582755 0.05747145 139.9974 1.2825237
我很难确定我的编码是否正确：
brcalme <- lmer(assimilation ~ Competition*Drought + (1|Time), data = BRCAgassydays)
我也很难解释结果并确定它们在没有 p 值的情况下是否显着
> brcalme
Linear mixed model fit by REML ['lmerMod']
Formula: assimilation ~ Competition * Drought + (1 | Time)
Data: BRCAgassydays
REML criterion at convergence: 147.2326
Random effects:
Groups Name Std.Dev.
Time (Intercept) 1.0027
Residual 0.9269
Number of obs: 48, groups: Time, 12
Fixed Effects:
(Intercept) CompetitionNone
6.2042 0.6980
DroughtDrought CompetitionNone:DroughtDrought
-0.7320 -0.5918
summary(brcalme)
Linear mixed model fit by REML ['lmerMod']
Formula: assimilation ~ Competition * Drought + (1 | Time)
Data: BRCAgassydays
REML criterion at convergence: 147.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.3906 -0.4165 0.0132 0.5943 2.0888
Random effects:
Groups Name Variance Std.Dev.
Time (Intercept) 1.0055 1.0027
Residual 0.8591 0.9269
Number of obs: 48, groups: Time, 12
Fixed effects:
Estimate Std. Error t value
(Intercept) 6.2042 0.3942 15.739
CompetitionNone 0.6980 0.3784 1.845
DroughtDrought -0.7320 0.3784 -1.935
CompetitionNone:DroughtDrought -0.5918 0.5351 -1.106
Correlation of Fixed Effects:
(Intr) CmpttN DrghtD
CompetitnNn -0.480
DroghtDrght -0.480 0.500
CmpttnNn:DD 0.339 -0.707 -0.707
在此先感谢您的帮助！
关于 lmer() 函数的 coding/instructions，您确实有有效的输入。它们是否正确或有用最终取决于你的理论 using/testing。对于此类特定主题或理论问题，请查看 CrossValidated。
关于如何获取p值，加载包lmerTest然后运行summary()在模型上。
library(lme4)
library(lmerTest)
lmm <- lmer(assimilation ~ Competition*Drought + (1|Time), data = brcalme)
summary(lmm)
输出：
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: assimilation ~ Competition * Drought + (1 | Time)
Data: brcalme
REML criterion at convergence: 147.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.3906 -0.4165 0.0132 0.5943 2.0888
Random effects:
Groups Name Variance Std.Dev.
Time (Intercept) 1.0055 1.0027
Residual 0.8591 0.9269
Number of obs: 48, groups: Time, 12
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 6.2042 0.3942 23.4989 15.739 5.61e-14 ***
CompetitionNone 0.6980 0.3784 33.0000 1.845 0.0741 .
DroughtDrought -0.7320 0.3784 33.0000 -1.935 0.0617 .
CompetitionNone:DroughtDrought -0.5918 0.5351 33.0000 -1.106 0.2768
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CmpttN DrghtD
CompetitnNn -0.480
DroghtDrght -0.480 0.500
CmpttnNn:DD 0.339 -0.707 -0.707
关于“我正在尝试 运行 一个使用时间作为固定效应的混合效应模型。我在不规则的时间间隔 (3-7) 内重复测量，并希望考虑固定线性我的变量与时间的关系。”
不，您没有正确编码。 (1|Time) 是一个随机效应项（请参阅这些参考资料：https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf, https://stats.stackexchange.com/questions/4700/what-is-the-difference-between-fixed-effect-random-effect-and-mixed-effect-mode）
此随机效应项用于对“重复测量”中的数据点进行分组。例如，如果您多次调查 20 个不同的站点，您可能会使用 (1|Sites) 作为随机效应项（请参阅上面的 lme4 插图 link 以获得语法帮助）。如果您确实有重复测量，最好以这种方式对数据进行分组（有助于避免伪复制）。
对于时间，你可以将其拟合为固定效应：
brcalme <- lmer(assimilation ~ Competition*Drought + Time, data = BRCAgassydays)
但是这里你可能会得到一个错误，因为你没有指定任何随机效应（时间在上面的等式中是一个固定效应）。因此，您只需要一个线性模型（不是混合效应）。您可以将 lmer 更改为 lm 来执行此操作：
brcalme <- lm(assimilation ~ Competition*Drought + Time, data = BRCAgassydays)
但同样，如果可以/合适的话，拟合随机效应项（但根据你的问题，我猜时间不是你想要的随机效应项）。
关于 p 值，值得阅读它们的真正含义（例如，, https://stats.stackexchange.com/questions/166323/misunderstanding-a-p-value），并尝试学习围绕这些估计值（影响的大小）解释您的估计值和 SE，而不是而不是寻找 'statistical significance'。 lme4 没有开箱即用的 p 值功能是有原因的（即，您必须加载 lmerTest）。
我正在尝试 运行 一个使用时间作为固定效应的混合效应模型。我在不规则的时间间隔 (3-7) 内重复采取了措施，并想说明我的变量与时间的固定线性关系。同时我对确定处理效果（干旱和竞争）很感兴趣。
下面是我尝试 运行 使用 lme4 和 lmer
> BRCAgassydays
Species Drought Competition Treatment Time assimilation conductance intercellularcarbon
1 BRCA Control Invasive CxI 0 7.811799 0.16297273 297.5562
2 BRCA Control Invasive CxI 21 5.405663 0.19472180 314.8806
3 BRCA Control Invasive CxI 29 7.604270 0.14460617 291.1411
4 BRCA Control Invasive CxI 34 7.513887 0.22543327 326.1150
5 BRCA Control Invasive CxI 42 6.683802 0.18940180 318.6928
6 BRCA Control Invasive CxI 55 6.071712 0.13774260 301.6228
7 BRCA Control Invasive CxI 70 6.331053 0.13962460 306.8503
8 BRCA Control Invasive CxI 78 4.941679 0.13157067 312.1904
9 BRCA Control Invasive CxI 82 4.729761 0.11871700 313.6009
10 BRCA Control Invasive CxI 88 6.831296 0.16134250 305.1969
11 BRCA Control Invasive CxI 97 4.652050 0.09225767 295.9346
12 BRCA Control Invasive CxI 104 5.873223 0.16265633 313.8243
13 BRCA Control None CxN 0 7.644604 0.17184619 301.8665
14 BRCA Control None CxN 21 8.250492 0.23745253 317.0501
15 BRCA Control None CxN 29 7.463330 0.13094473 286.2365
16 BRCA Control None CxN 34 7.928604 0.24988353 328.3139
17 BRCA Control None CxN 42 7.415549 0.18531760 309.8957
18 BRCA Control None CxN 55 6.764483 0.13508080 291.4254
19 BRCA Control None CxN 70 5.666392 0.11958453 304.4248
20 BRCA Control None CxN 78 7.267130 0.16822320 303.4725
21 BRCA Control None CxN 82 5.870902 0.11838573 297.4116
22 BRCA Control None CxN 88 7.400286 0.18886560 306.0608
23 BRCA Control None CxN 97 5.397562 0.13183013 313.4487
24 BRCA Control None CxN 104 5.756836 0.14453173 311.5725
25 BRCA Drought Invasive DxI 0 6.932256 0.12224355 285.7301
26 BRCA Drought Invasive DxI 21 8.448956 0.22943413 311.6504
27 BRCA Drought Invasive DxI 29 7.410476 0.12434440 280.5574
28 BRCA Drought Invasive DxI 34 8.208636 0.26668580 327.9786
29 BRCA Drought Invasive DxI 42 5.324922 0.12691907 312.5066
30 BRCA Drought Invasive DxI 55 5.196439 0.10962533 295.7930
31 BRCA Drought Invasive DxI 70 4.643326 0.08082647 289.0008
32 BRCA Drought Invasive DxI 78 3.675965 0.07471427 298.9459
33 BRCA Drought Invasive DxI 82 4.113252 0.09586540 310.3563
34 BRCA Drought Invasive DxI 88 4.340185 0.09807740 310.9444
35 BRCA Drought Invasive DxI 97 4.410509 0.09351200 304.7900
36 BRCA Drought Invasive DxI 104 2.961152 0.06973620 309.7580
37 BRCA Drought None DxN 0 5.299206 0.10402717 299.4199
38 BRCA Drought None DxN 21 8.931698 0.22541307 310.6568
39 BRCA Drought None DxN 29 6.490820 0.10163740 271.2123
40 BRCA Drought None DxN 34 6.748470 0.19204680 323.1626
41 BRCA Drought None DxN 42 4.175082 0.08008673 295.1853
42 BRCA Drought None DxN 55 4.740064 0.10071627 293.5214
43 BRCA Drought None DxN 70 5.147252 0.09366540 284.0992
44 BRCA Drought None DxN 78 5.187626 0.09444033 291.6765
45 BRCA Drought None DxN 82 4.608225 0.09833660 303.1695
46 BRCA Drought None DxN 88 6.398861 0.14120963 303.1060
47 BRCA Drought None DxN 97 3.980145 0.11448827 324.5742
48 BRCA Drought None DxN 104 5.233092 0.12851007 313.0436
transpiration seA seG seCi seE
1 2.822450 0.43644138 0.05761956 105.2020 0.9978866
2 2.320922 0.45469707 0.08708224 140.8189 1.0379479
3 1.790830 0.47508738 0.05903522 118.8578 0.7311035
4 2.400032 0.43793202 0.10081682 145.8431 1.0733268
5 2.805418 0.67185683 0.08470306 142.5237 1.2546211
6 3.114693 0.74197040 0.06160036 134.8898 1.3929330
7 1.795997 1.23465637 0.06244202 137.2276 0.8031942
8 2.715327 0.72150617 0.05884019 139.6158 1.2143311
9 2.544619 0.74568747 0.05309186 140.2466 1.1379881
10 3.019007 1.51528270 0.08067125 152.5985 1.5095034
11 1.784082 0.03402206 0.04612883 147.9673 0.8920409
12 3.453956 0.33488091 0.07274212 140.3465 1.5446562
13 2.598881 0.46217235 0.06075680 106.7259 0.9188432
14 2.909069 0.84976554 0.10619200 141.7891 1.3009752
15 2.025599 0.50776426 0.05856027 128.0088 0.9058755
16 2.487442 0.54267771 0.11175131 146.8264 1.1124180
17 2.875295 0.11954494 0.08287655 138.5896 1.2858709
18 3.265247 0.84720688 0.06040997 130.3294 1.4602628
19 1.483867 0.44247329 0.05347983 136.1429 0.6636057
20 3.203109 0.74145804 0.07523170 135.7170 1.4324737
21 2.539916 1.02080831 0.05294371 133.0065 1.1358850
22 3.553893 0.34056152 0.08446326 136.8745 1.5893495
23 1.926768 0.80018829 0.05895623 140.1785 0.8616768
24 3.047124 0.42481340 0.06463656 139.3395 1.3627154
25 1.899871 0.55859666 0.04321962 101.0208 0.6717057
26 2.797467 0.88094476 0.10260606 139.3743 1.2510653
27 2.110417 0.64763904 0.05560851 125.4691 0.9438074
28 2.689897 0.42303271 0.11926552 146.6765 1.2029587
29 2.197370 0.97036269 0.05675993 139.7572 0.9826938
30 2.669697 0.54886662 0.04902594 132.2827 1.1939247
31 1.295458 0.61865489 0.03614669 129.2451 0.5793462
32 1.736331 0.69632750 0.03341324 133.6927 0.7765110
33 2.171248 0.58167406 0.04287231 138.7956 0.9710117
34 2.127337 1.02370322 0.04386155 139.0585 0.9513742
35 1.719165 0.61438695 0.04181984 136.3062 0.7688341
36 1.628725 1.18038670 0.03118698 138.5280 0.7283880
37 1.572679 0.73317733 0.03677916 105.8609 0.5560261
38 2.887991 0.75700712 0.10080779 138.9300 1.2915488
39 1.712095 0.86022203 0.04545363 121.2898 0.7656720
40 2.074472 0.31120772 0.08588594 144.5227 0.9277320
41 1.466688 0.40419493 0.03581588 132.0109 0.6559227
42 2.380746 0.70855853 0.04504168 131.2668 1.0647018
43 1.520280 0.62697587 0.04188844 127.0530 0.6798899
44 1.772095 0.45202487 0.04223500 130.4417 0.7925049
45 2.191443 0.55772642 0.04397746 135.5815 0.9800431
46 2.756795 1.13005016 0.06315087 135.5531 1.2328762
47 2.051825 0.83631901 0.05120071 145.1540 0.9176041
48 2.867810 1.07582755 0.05747145 139.9974 1.2825237
我很难确定我的编码是否正确：
brcalme <- lmer(assimilation ~ Competition*Drought + (1|Time), data = BRCAgassydays)
我也很难解释结果并确定它们在没有 p 值的情况下是否显着
> brcalme
Linear mixed model fit by REML ['lmerMod']
Formula: assimilation ~ Competition * Drought + (1 | Time)
Data: BRCAgassydays
REML criterion at convergence: 147.2326
Random effects:
Groups Name Std.Dev.
Time (Intercept) 1.0027
Residual 0.9269
Number of obs: 48, groups: Time, 12
Fixed Effects:
(Intercept) CompetitionNone
6.2042 0.6980
DroughtDrought CompetitionNone:DroughtDrought
-0.7320 -0.5918
summary(brcalme)
Linear mixed model fit by REML ['lmerMod']
Formula: assimilation ~ Competition * Drought + (1 | Time)
Data: BRCAgassydays
REML criterion at convergence: 147.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.3906 -0.4165 0.0132 0.5943 2.0888
Random effects:
Groups Name Variance Std.Dev.
Time (Intercept) 1.0055 1.0027
Residual 0.8591 0.9269
Number of obs: 48, groups: Time, 12
Fixed effects:
Estimate Std. Error t value
(Intercept) 6.2042 0.3942 15.739
CompetitionNone 0.6980 0.3784 1.845
DroughtDrought -0.7320 0.3784 -1.935
CompetitionNone:DroughtDrought -0.5918 0.5351 -1.106
Correlation of Fixed Effects:
(Intr) CmpttN DrghtD
CompetitnNn -0.480
DroghtDrght -0.480 0.500
CmpttnNn:DD 0.339 -0.707 -0.707
在此先感谢您的帮助！
关于 lmer() 函数的 coding/instructions，您确实有有效的输入。它们是否正确或有用最终取决于你的理论 using/testing。对于此类特定主题或理论问题，请查看 CrossValidated。
关于如何获取p值，加载包lmerTest然后运行summary()在模型上。
library(lme4)
library(lmerTest)
lmm <- lmer(assimilation ~ Competition*Drought + (1|Time), data = brcalme)
summary(lmm)
输出：
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: assimilation ~ Competition * Drought + (1 | Time)
Data: brcalme
REML criterion at convergence: 147.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.3906 -0.4165 0.0132 0.5943 2.0888
Random effects:
Groups Name Variance Std.Dev.
Time (Intercept) 1.0055 1.0027
Residual 0.8591 0.9269
Number of obs: 48, groups: Time, 12
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 6.2042 0.3942 23.4989 15.739 5.61e-14 ***
CompetitionNone 0.6980 0.3784 33.0000 1.845 0.0741 .
DroughtDrought -0.7320 0.3784 33.0000 -1.935 0.0617 .
CompetitionNone:DroughtDrought -0.5918 0.5351 33.0000 -1.106 0.2768
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CmpttN DrghtD
CompetitnNn -0.480
DroghtDrght -0.480 0.500
CmpttnNn:DD 0.339 -0.707 -0.707
关于“我正在尝试 运行 一个使用时间作为固定效应的混合效应模型。我在不规则的时间间隔 (3-7) 内重复测量，并希望考虑固定线性我的变量与时间的关系。”
不，您没有正确编码。 (1|Time) 是一个随机效应项（请参阅这些参考资料：https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf, https://stats.stackexchange.com/questions/4700/what-is-the-difference-between-fixed-effect-random-effect-and-mixed-effect-mode）
此随机效应项用于对“重复测量”中的数据点进行分组。例如，如果您多次调查 20 个不同的站点，您可能会使用 (1|Sites) 作为随机效应项（请参阅上面的 lme4 插图 link 以获得语法帮助）。如果您确实有重复测量，最好以这种方式对数据进行分组（有助于避免伪复制）。
对于时间，你可以将其拟合为固定效应：
brcalme <- lmer(assimilation ~ Competition*Drought + Time, data = BRCAgassydays)
但是这里你可能会得到一个错误，因为你没有指定任何随机效应（时间在上面的等式中是一个固定效应）。因此，您只需要一个线性模型（不是混合效应）。您可以将 lmer 更改为 lm 来执行此操作：
brcalme <- lm(assimilation ~ Competition*Drought + Time, data = BRCAgassydays)
但同样，如果可以/合适的话，拟合随机效应项（但根据你的问题，我猜时间不是你想要的随机效应项）。
关于 p 值，值得阅读它们的真正含义（例如，lmerTest）。