Does anyone know how to use (nlme)? I'm having a lot of problems using it for something that should be really simple, any help would be really appreciated. I won't put up the details until I know someone knows what I'm talking about.
Data was collected by a field study concerned planting pattern, genotype, we replicated this study for 3 years and in each growing season we surveyd 10 times. My question is how to interpret the results best? A professor advised using linear mixed effect model with year as a random factor, to reduce the number of separate analyses and type I error. Anybody who knows, please do me a favor.
The following is my data
site YEAR GENOTYPE PATTERN T density
1 1 G S T1 3.0035
2 1 G S T1 3.0538
3 1 G S T1 2.9939
4 1 G S T1 2.8506
5 1 G M T1 2.7316
6 1 G M T1 2.7789
7 1 G M T1 2.8791
8 1 G M T1 2.7419
9 1 S S T1 0.6024
10 1 S S T1 0.6079
11 1 S S T1 0.6014
12 1 S S T1 0.5855
13 1 S M T1 0.5719
14 1 S M T1 0.5774
15 1 S M T1 0.5887
16 1 S M T1 0.5731
1 2 G S T1 3.3064
2 2 G S T1 3.2896
3 2 G S T1 3.3448
4 2 G S T1 3.4615
5 2 G M T1 3.4104
6 2 G M T1 3.3243
7 2 G M T1 3.3477
8 2 G M T1 3.4031
9 2 S S T1 3.3660
10 2 S S T1 3.3025
11 2 S S T1 3.3760
12 2 S S T1 3.3038
13 2 S M T1 3.4237
14 2 S M T1 3.5357
15 2 S M T1 3.5001
16 2 S M T1 3.4195
1 3 G S T1 2.2068
2 3 G S T1 2.1492
3 3 G S T1 2.0969
4 3 G S T1 2.3010
5 3 G M T1 1.7404
6 3 G M T1 1.8388
7 3 G M T1 1.5185
8 3 G M T1 1.6128
9 3 S S T1 2.3636
10 3 S S T1 2.5092
11 3 S S T1 2.0828
12 3 S S T1 2.4065
13 3 S M T1 1.9243
14 3 S M T1 2.0645
15 3 S M T1 2.0000
16 3 S M T1 2.0170
1 1 G S T2 3.6379
2 1 G S T2 3.5680
3 1 G S T2 3.6095
4 1 G S T2 3.6999
5 1 G M T2 3.4241
6 1 G M T2 3.5053
7 1 G M T2 3.4559
8 1 G M T2 3.4751
9 1 S S T2 0.6663
10 1 S S T2 0.6597
11 1 S S T2 0.6637
12 1 S S T2 0.6721
13 1 S M T2 0.6458
14 1 S M T2 0.6537
15 1 S M T2 0.6489
16 1 S M T2 0.6508
1 2 G S T2 4.5387
2 2 G S T2 4.5442
3 2 G S T2 4.5174
4 2 G S T2 4.5464
5 2 G M T2 4.3512
6 2 G M T2 4.4067
7 2 G M T2 4.4090
8 2 G M T2 4.1965
9 2 S S T2 4.5641
10 2 S S T2 4.5103
11 2 S S T2 4.6466
12 2 S S T2 4.5205
13 2 S M T2 4.4246
14 2 S M T2 4.3424
15 2 S M T2 4.4487
16 2 S M T2 4.3642
1 3 G S T2 3.0099
2 3 G S T2 3.0730
3 3 G S T2 2.9657
4 3 G S T2 3.1807
5 3 G M T2 2.6812
6 3 G M T2 2.3729
7 3 G M T2 2.8820
8 3 G M T2 2.4800
9 3 S S T2 3.1364
10 3 S S T2 3.0839
11 3 S S T2 2.9036
12 3 S S T2 2.9850
13 3 S M T2 2.7412
14 3 S M T2 2.8202
15 3 S M T2 2.3692
16 3 S M T2 2.5866
1 1 G S T3 3.3762
2 1 G S T3 3.3531
3 1 G S T3 3.5340
4 1 G S T3 3.4096
5 1 G M T3 3.4263
6 1 G M T3 3.1959
7 1 G M T3 3.2973
8 1 G M T3 3.1915
9 1 S S T3 0.6411
10 1 S S T3 0.6388
11 1 S S T3 0.6565
12 1 S S T3 0.6444
13 1 S M T3 0.6460
14 1 S M T3 0.6228
15 1 S M T3 0.6332
16 1 S M T3 0.6224
1 2 G S T3 5.4512
2 2 G S T3 5.3256
3 2 G S T3 5.5334
4 2 G S T3 5.3601
5 2 G M T3 4.9647
6 2 G M T3 5.0390
7 2 G M T3 5.0628
8 2 G M T3 5.0739
9 2 S S T3 5.3199
10 2 S S T3 5.2441
11 2 S S T3 5.2672
12 2 S S T3 5.3007
13 2 S M T3 5.1314
14 2 S M T3 5.0120
15 2 S M T3 5.1177
16 2 S M T3 5.0052
1 3 G S T3 2.8808
2 3 G S T3 3.0839
3 3 G S T3 2.7931
4 3 G S T3 2.8555
5 3 G M T3 2.6902
6 3 G M T3 2.7723
7 3 G M T3 2.8293
8 3 G M T3 2.9299
9 3 S S T3 2.5911
10 3 S S T3 2.5587
11 3 S S T3 2.7818
12 3 S S T3 2.7332
13 3 S M T3 2.8414
14 3 S M T3 2.7024
15 3 S M T3 2.6955
16 3 S M T3 2.9365
1 1 G S T4 2.9309
2 1 G S T4 2.9186
3 1 G S T4 3.0249
4 1 G S T4 3.0910
5 1 G M T4 2.9745
6 1 G M T4 2.9538
7 1 G M T4 3.1179
8 1 G M T4 3.0588
9 1 S S T4 0.5945
10 1 S S T4 0.5931
11 1 S S T4 0.6048
12 1 S S T4 0.6118
13 1 S M T4 0.5993
14 1 S M T4 0.5970
15 1 S M T4 0.6147
16 1 S M T4 0.6084
1 2 G S T4 5.3077
2 2 G S T4 5.4044
3 2 G S T4 5.4797
4 2 G S T4 5.4220
5 2 G M T4 5.1803
6 2 G M T4 5.2498
7 2 G M T4 5.3166
8 2 G M T4 5.2693
9 2 S S T4 5.7061
10 2 S S T4 5.4886
11 2 S S T4 5.5974
12 2 S S T4 5.6148
13 2 S M T4 5.3078
14 2 S M T4 5.1670
15 2 S M T4 5.1887
16 2 S M T4 5.2186
1 3 G S T4 4.1525
2 3 G S T4 4.1609
3 3 G S T4 4.1169
4 3 G S T4 4.1687
5 3 G M T4 3.9305
6 3 G M T4 3.9376
7 3 G M T4 3.9668
8 3 G M T4 3.9696
9 3 S S T4 4.1293
10 3 S S T4 4.1358
11 3 S S T4 4.1450
12 3 S S T4 4.1527
13 3 S M T4 3.8382
14 3 S M T4 3.8435
15 3 S M T4 3.8405
16 3 S M T4 3.8439
1 1 G S T5 3.2206
2 1 G S T5 3.0726
3 1 G S T5 3.0228
4 1 G S T5 3.1284
5 1 G M T5 2.6253
6 1 G M T5 2.7760
7 1 G M T5 2.7774
8 1 G M T5 2.7388
9 1 S S T5 0.6254
10 1 S S T5 0.6099
11 1 S S T5 0.6045
12 1 S S T5 0.6158
13 1 S M T5 0.5593
14 1 S M T5 0.5770
15 1 S M T5 0.5772
16 1 S M T5 0.5727
1 2 G S T5 3.6946
2 2 G S T5 3.7158
3 2 G S T5 3.6597
4 2 G S T5 3.6553
5 2 G M T5 3.5065
6 2 G M T5 3.3351
7 2 G M T5 3.4609
8 2 G M T5 3.3047
9 2 S S T5 3.5875
10 2 S S T5 3.4774
11 2 S S T5 3.5924
12 2 S S T5 3.5771
13 2 S M T5 3.2695
14 2 S M T5 3.3753
15 2 S M T5 3.2916
16 2 S M T5 3.2804
1 3 G S T5 3.6772
2 3 G S T5 3.6558
3 3 G S T5 3.6118
4 3 G S T5 3.6636
5 3 G M T5 3.3967
6 3 G M T5 3.4834
7 3 G M T5 3.3992
8 3 G M T5 3.6731
9 3 S S T5 3.6242
10 3 S S T5 3.6307
11 3 S S T5 3.6396
12 3 S S T5 3.6069
13 3 S M T5 3.4087
14 3 S M T5 3.4874
15 3 S M T5 3.3094
16 3 S M T5 3.6386
1 1 G S T6 2.7332
2 1 G S T6 2.6684
3 1 G S T6 2.8573
4 1 G S T6 2.6365
5 1 G M T6 2.2788
6 1 G M T6 2.4624
7 1 G M T6 2.4728
8 1 G M T6 2.3365
9 1 S S T6 0.5721
10 1 S S T6 0.5645
11 1 S S T6 0.5863
12 1 S S T6 0.5607
13 1 S M T6 0.5157
14 1 S M T6 0.5394
15 1 S M T6 0.5407
16 1 S M T6 0.5233
1 2 G S T6 3.0955
2 2 G S T6 3.0941
3 2 G S T6 3.1123
4 2 G S T6 3.0821
5 2 G M T6 2.8195
6 2 G M T6 2.8609
7 2 G M T6 2.9085
8 2 G M T6 2.9745
9 2 S S T6 3.0465
10 2 S S T6 2.9566
11 2 S S T6 3.0422
12 2 S S T6 2.9360
13 2 S M T6 2.9494
14 2 S M T6 2.9652
15 2 S M T6 2.9309
16 2 S M T6 3.0000
1 3 G S T6 2.9079
2 3 G S T6 2.8768
3 3 G S T6 2.7882
4 3 G S T6 2.9689
5 3 G M T6 2.7259
6 3 G M T6 2.6875
7 3 G M T6 2.5340
8 3 G M T6 2.6170
9 3 S S T6 2.0934
10 3 S S T6 2.0043
11 3 S S T6 2.0043
12 3 S S T6 2.2201
13 3 S M T6 2.3927
14 3 S M T6 2.4786
15 3 S M T6 2.5453
16 3 S M T6 2.4928
1 1 G S T7 2.7459
2 1 G S T7 2.6503
3 1 G S T7 2.6222
4 1 G S T7 2.7143
5 1 G M T7 2.7348
6 1 G M T7 2.6739
7 1 G M T7 2.7505
8 1 G M T7 2.6053
9 1 S S T7 0.5736
10 1 S S T7 0.5623
11 1 S S T7 0.5590
12 1 S S T7 0.5699
13 1 S M T7 0.5723
14 1 S M T7 0.5651
15 1 S M T7 0.5741
16 1 S M T7 0.5569
1 2 G S T7 2.6702
2 2 G S T7 2.5635
3 2 G S T7 2.6946
4 2 G S T7 2.7118
5 2 G M T7 2.1614
6 2 G M T7 2.1004
7 2 G M T7 2.0792
8 2 G M T7 2.3010
9 2 S S T7 2.2175
10 2 S S T7 2.3892
11 2 S S T7 2.3284
12 2 S S T7 2.3010
13 2 S M T7 2.3222
14 2 S M T7 2.1139
15 2 S M T7 2.1461
16 2 S M T7 2.0128
1 3 G S T7 2.5328
2 3 G S T7 2.5623
3 3 G S T7 2.4624
4 3 G S T7 2.6075
5 3 G M T7 1.7709
6 3 G M T7 1.5315
7 3 G M T7 1.6902
8 3 G M T7 1.6021
9 3 S S T7 2.4786
10 3 S S T7 2.3729
11 3 S S T7 2.5809
12 3 S S T7 2.6191
13 3 S M T7 1.8388
14 3 S M T7 2.0969
15 3 S M T7 1.9590
16 3 S M T7 2.1271
1 1 G S T8 2.6180
2 1 G S T8 2.6405
3 1 G S T8 2.6385
4 1 G S T8 2.6875
5 1 G M T8 2.5092
6 1 G M T8 2.6107
7 1 G M T8 2.7185
8 1 G M T8 2.6964
9 1 S S T8 0.5585
10 1 S S T8 0.5612
11 1 S S T8 0.5609
12 1 S S T8 0.5667
13 1 S M T8 0.5452
14 1 S M T8 0.5576
15 1 S M T8 0.5704
16 1 S M T8 0.5678
1 2 G S T8 3.1355
2 2 G S T8 3.2087
3 2 G S T8 3.2117
4 2 G S T8 3.3015
5 2 G M T8 3.0580
6 2 G M T8 3.2695
7 2 G M T8 3.2269
8 2 G M T8 2.9881
9 2 S S T8 3.0569
10 2 S S T8 3.0035
11 2 S S T8 3.2405
12 2 S S T8 3.0402
13 2 S M T8 3.1294
14 2 S M T8 3.1623
15 2 S M T8 3.2095
16 2 S M T8 3.0645
1 3 G S T8 2.1430
2 3 G S T8 2.2175
3 3 G S T8 2.2788
4 3 G S T8 2.3118
5 3 G M T8 2.0607
6 3 G M T8 2.3617
7 3 G M T8 2.1430
8 3 G M T8 2.4928
9 3 S S T8 2.0043
10 3 S S T8 2.1335
11 3 S S T8 1.9085
12 3 S S T8 2.0645
13 3 S M T8 2.0645
14 3 S M T8 2.3096
15 3 S M T8 2.3784
16 3 S M T8 2.3404
1 1 G S T9 2.4265
2 1 G S T9 2.4886
3 1 G S T9 2.4393
4 1 G S T9 2.4166
5 1 G M T9 2.0569
6 1 G M T9 2.1106
7 1 G M T9 2.2833
8 1 G M T9 2.1553
9 1 S S T9 0.5349
10 1 S S T9 0.5426
11 1 S S T9 0.5365
12 1 S S T9 0.5336
13 1 S M T9 0.4853
14 1 S M T9 0.4928
15 1 S M T9 0.5163
16 1 S M T9 0.4990
1 2 G S T9 3.1258
2 2 G S T9 3.2368
3 2 G S T9 3.1498
4 2 G S T9 3.3566
5 2 G M T9 2.9956
6 2 G M T9 3.0888
7 2 G M T9 3.0265
8 2 G M T9 2.9279
9 2 S S T9 3.0892
10 2 S S T9 3.2253
11 2 S S T9 3.0531
12 2 S S T9 3.0212
13 2 S M T9 2.8176
14 2 S M T9 3.0170
15 2 S M T9 2.8899
16 2 S M T9 2.8993
1 3 G S T9 2.4928
2 3 G S T9 2.3979
3 3 G S T9 2.3617
4 3 G S T9 2.2279
5 3 G M T9 2.3324
6 3 G M T9 2.5705
7 3 G M T9 2.3892
8 3 G M T9 2.7738
9 3 S S T9 2.7839
10 3 S S T9 2.8537
11 3 S S T9 3.0527
12 3 S S T9 2.8904
13 3 S M T9 2.2355
14 3 S M T9 2.3160
15 3 S M T9 2.3404
16 3 S M T9 3.1072
1 1 G S T10 2.5145
2 1 G S T10 2.4728
3 1 G S T10 2.4487
4 1 G S T10 2.4639
5 1 G M T10 2.4757
6 1 G M T10 2.5328
7 1 G M T10 2.5441
8 1 G M T10 2.3075
9 1 S S T10 0.5459
10 1 S S T10 0.5407
11 1 S S T10 0.5377
12 1 S S T10 0.5396
13 1 S M T10 0.5410
14 1 S M T10 0.5481
15 1 S M T10 0.5495
16 1 S M T10 0.5195
1 2 G S T10 2.0414
2 2 G S T10 2.0086
3 2 G S T10 2.0792
4 2 G S T10 2.1206
5 2 G M T10 2.2788
6 2 G M T10 2.2923
7 2 G M T10 2.3856
8 2 G M T10 2.1959
9 2 S S T10 2.1959
10 2 S S T10 2.1703
11 2 S S T10 2.4232
12 2 S S T10 2.2041
13 2 S M T10 2.3139
14 2 S M T10 2.2856
15 2 S M T10 2.2227
16 2 S M T10 2.1239
1 3 G S T10 2.3711
2 3 G S T10 2.5502
3 3 G S T10 2.4133
4 3 G S T10 2.6031
5 3 G M T10 2.7694
6 3 G M T10 2.8768
7 3 G M T10 2.7803
8 3 G M T10 2.7340
9 3 S S T10 2.8331
10 3 S S T10 2.9036
11 3 S S T10 2.8976
12 3 S S T10 2.7767
13 3 S M T10 2.7435
14 3 S M T10 2.8727
15 3 S M T10 2.7860
16 3 S M T10 2.8457
@mpiktas:Just as you proposed,I didn't show my goal. My interest is to: (1)compare the response of a pest density to planting pattern and genotype;(2)is there any discrepancy among 3 successive years? I have tried linear mixed effect model using year as a random factor, to reduce the number of separate analyses and type I error using R. The concerned code is followed:
Data <- read.csv("aphids.csv", header=T)
Data$T <- as.numeric(Data$T)
library(nlme)
aphids.lme <- lme(density ~ GENOTYPE*PATTERN+year+site , random=~T| year,
method=”ML”, na.action=na.omit, data=Data)
summary(aphids.lme)
is this right?
