[R] Analysing insecticide biossays using lmer
pierricklabbe
pierrick.labbe at univ-montp2.fr
Thu Jun 30 15:56:36 CEST 2011
Hi all,
Here is my problem: I performed bioassays using a unique insecticide on 9
different genotypes and got their mortality depending on the dose of
insecticide used.
Now, I want to see wether some genotypes are different or not in their
responses to insecticide.
My problem is that I have up to four replicates for some genotypes, but only
one for other... Due to this unbalanced design, I thought of using lmer
procedure (lme4).
Here are the data, the model and the R output:
> dataf
geno ml bouteille dose logdose tot mort date
1 D1D1 0.4 0.10 4e-04 -3.397940 32 0 1
2 D1D1 0.4 0.10 4e-04 -3.397940 83 4 2
3 D1D1 0.5 0.10 5e-04 -3.301030 120 17 2
4 D1D1 0.5 0.10 5e-04 -3.301030 114 2 4
5 D1D1 0.6 0.10 6e-04 -3.221849 34 10 1
6 D1D1 0.6 0.10 6e-04 -3.221849 72 20 2
7 D1D1 0.7 0.10 7e-04 -3.154902 120 64 2
8 D1D1 0.7 0.10 7e-04 -3.154902 116 7 4
9 D1D1 0.8 0.10 8e-04 -3.096910 35 15 1
10 D1D1 0.8 0.10 8e-04 -3.096910 78 47 2
11 D1D1 0.8 0.10 8e-04 -3.096910 120 73 3
12 D1D1 0.9 0.10 9e-04 -3.045757 32 19 1
13 D1D1 0.9 0.10 9e-04 -3.045757 87 57 2
14 D1D1 0.9 0.10 9e-04 -3.045757 119 78 3
15 D1D1 0.9 0.10 9e-04 -3.045757 116 14 4
16 D1D1 1.0 0.10 1e-03 -3.000000 33 26 1
17 D1D1 1.0 0.10 1e-03 -3.000000 83 67 2
18 D1D1 1.0 0.10 1e-03 -3.000000 120 77 3
19 D1D1 1.0 0.10 1e-03 -3.000000 120 21 4
20 D1D1 0.3 1.00 3e-03 -2.522879 38 38 1
21 D1D1 0.3 1.00 3e-03 -2.522879 94 94 2
22 D1D1 0.3 1.00 3e-03 -2.522879 120 109 3
23 D1D1 0.5 1.00 5e-03 -2.301030 40 40 1
24 D1D1 0.5 1.00 5e-03 -2.301030 94 94 2
25 D1D1 0.5 1.00 5e-03 -2.301030 120 117 3
26 D1D1 0.6 1.00 6e-03 -2.221849 120 120 2
27 D1D1 0.7 1.00 7e-03 -2.154902 40 40 1
28 D1D1 0.7 1.00 7e-03 -2.154902 94 94 2
29 D1R 0.6 0.10 6e-04 -3.221849 87 25 1
30 D1R 0.8 0.10 8e-04 -3.096910 88 40 1
31 D1R 1.0 0.10 1e-03 -3.000000 88 39 1
32 D1R 0.3 1.00 3e-03 -2.522879 93 59 1
33 D1R 0.4 1.00 4e-03 -2.397940 96 85 1
34 D1R 0.5 1.00 5e-03 -2.301030 96 91 1
35 D1R 0.7 1.00 7e-03 -2.154902 95 91 1
36 D1R 0.9 1.00 9e-03 -2.045757 97 97 1
37 D1S 0.3 0.10 3e-04 -3.522879 117 4 1
38 D1S 0.5 0.10 5e-04 -3.301030 118 37 1
39 D1S 0.6 0.10 6e-04 -3.221849 119 76 1
40 D1S 0.7 0.10 7e-04 -3.154902 118 98 1
41 D1S 0.9 0.10 9e-04 -3.045757 120 116 1
42 D1S 1.0 0.10 1e-03 -3.000000 120 120 1
43 D3D3 0.5 0.10 5e-04 -3.301030 58 6 2
44 D3D3 0.5 0.10 5e-04 -3.301030 40 4 3
45 D3D3 0.7 0.10 7e-04 -3.154902 57 12 2
46 D3D3 0.7 0.10 7e-04 -3.154902 40 9 3
47 D3D3 0.8 0.10 8e-04 -3.096910 87 13 1
48 D3D3 0.8 0.10 8e-04 -3.096910 59 12 2
49 D3D3 0.9 0.10 9e-04 -3.045757 97 35 1
50 D3D3 0.9 0.10 9e-04 -3.045757 56 13 2
51 D3D3 0.9 0.10 9e-04 -3.045757 39 11 3
52 D3D3 1.0 0.10 1e-03 -3.000000 96 49 1
53 D3D3 1.0 0.10 1e-03 -3.000000 60 20 2
54 D3D3 1.0 0.10 1e-03 -3.000000 35 6 3
55 D3D3 0.2 1.00 2e-03 -2.698970 40 31 1
56 D3D3 0.3 1.00 3e-03 -2.522879 100 88 1
57 D3D3 0.3 1.00 3e-03 -2.522879 60 50 2
58 D3D3 0.3 1.00 3e-03 -2.522879 40 37 3
59 D3D3 0.5 1.00 5e-03 -2.301030 99 93 1
60 D3D3 0.5 1.00 5e-03 -2.301030 60 59 2
61 D3D3 0.5 1.00 5e-03 -2.301030 40 39 3
62 D3D3 0.6 1.00 6e-03 -2.221849 100 100 1
63 D3D3 0.6 1.00 6e-03 -2.221849 60 60 2
64 D3D3 0.6 1.00 6e-03 -2.221849 40 40 3
65 D3R 0.6 0.10 6e-04 -3.221849 87 7 1
66 D3R 0.8 0.10 8e-04 -3.096910 84 30 1
67 D3R 1.0 0.10 1e-03 -3.000000 87 36 1
68 D3R 0.3 1.00 3e-03 -2.522879 92 73 1
69 D3R 0.4 1.00 4e-03 -2.397940 100 91 1
70 D3R 0.5 1.00 5e-03 -2.301030 99 95 1
71 D3R 0.7 1.00 7e-03 -2.154902 94 93 1
72 D3R 0.8 1.00 8e-03 -2.096910 98 98 1
73 D3S 1.0 0.01 1e-04 -4.000000 30 0 1
74 D3S 1.0 0.01 1e-04 -4.000000 60 0 2
75 D3S 1.0 0.01 1e-04 -4.000000 61 0 3
76 D3S 0.3 0.10 3e-04 -3.522879 28 1 1
77 D3S 0.3 0.10 3e-04 -3.522879 66 1 3
78 D3S 0.4 0.10 4e-04 -3.397940 66 2 3
79 D3S 0.5 0.10 5e-04 -3.301030 40 9 1
80 D3S 0.5 0.10 5e-04 -3.301030 60 57 2
81 D3S 0.5 0.10 5e-04 -3.301030 67 8 3
82 D3S 0.6 0.10 6e-04 -3.221849 42 12 1
83 D3S 0.6 0.10 6e-04 -3.221849 60 59 2
84 D3S 0.6 0.10 6e-04 -3.221849 66 22 3
85 D3S 0.7 0.10 7e-04 -3.154902 38 25 1
86 D3S 0.7 0.10 7e-04 -3.154902 60 60 2
87 D3S 0.7 0.10 7e-04 -3.154902 70 36 3
88 D3S 0.8 0.10 8e-04 -3.096910 72 51 3
89 D3S 0.9 0.10 9e-04 -3.045757 45 43 1
90 D3S 1.0 0.10 1e-03 -3.000000 48 48 1
91 RR 0.2 1.00 2e-03 -2.698970 39 1 1
92 RR 0.2 1.00 2e-03 -2.698970 60 0 2
93 RR 0.4 1.00 4e-03 -2.397940 39 3 1
94 RR 0.4 1.00 4e-03 -2.397940 60 5 2
95 RR 0.5 1.00 5e-03 -2.301030 60 9 2
96 RR 0.6 1.00 6e-03 -2.221849 39 13 1
97 RR 0.6 1.00 6e-03 -2.221849 60 22 2
98 RR 0.7 1.00 7e-03 -2.154902 60 42 2
99 RR 0.8 1.00 8e-03 -2.096910 40 38 1
100 RR 0.8 1.00 8e-03 -2.096910 60 39 2
101 RR 0.2 10.00 2e-02 -1.698970 40 40 1
102 RR 0.2 10.00 2e-02 -1.698970 60 60 2
103 RS 0.5 0.10 5e-04 -3.301030 38 0 1
104 RS 0.6 0.10 6e-04 -3.221849 40 2 1
105 RS 0.8 0.10 8e-04 -3.096910 40 1 1
106 RS 0.9 0.10 9e-04 -3.045757 114 21 1
107 RS 1.0 0.10 1e-03 -3.000000 39 1 1
108 RS 0.2 1.00 2e-03 -2.698970 120 82 1
109 RS 0.3 1.00 3e-03 -2.522879 38 33 1
110 RS 0.4 1.00 4e-03 -2.397940 120 110 1
111 RS 0.6 1.00 6e-03 -2.221849 120 119 1
112 RS 0.8 1.00 8e-03 -2.096910 40 40 1
113 SS 0.8 0.01 8e-05 -4.096910 72 0 2
114 SS 0.9 0.01 9e-05 -4.045757 37 9 1
115 SS 0.9 0.01 9e-05 -4.045757 75 1 2
116 SS 1.0 0.01 1e-04 -4.000000 39 5 1
117 SS 1.0 0.01 1e-04 -4.000000 73 1 2
118 SS 0.2 0.10 2e-04 -3.698970 40 21 1
119 SS 0.2 0.10 2e-04 -3.698970 77 12 2
120 SS 0.3 0.10 3e-04 -3.522879 38 28 1
121 SS 0.4 0.10 4e-04 -3.397940 40 30 1
122 SS 0.5 0.10 5e-04 -3.301030 39 37 1
123 SS 0.5 0.10 5e-04 -3.301030 41 36 2
124 SS 0.6 0.10 6e-04 -3.221849 40 40 1
125 SS 0.6 0.10 6e-04 -3.221849 40 39 2
> attach(dataf)
> y<-cbind(mort,tot-mort)
> model1<-lmer(y~logdose*geno+(1|geno/logdose)-1,family="quasibinomial",data=dataf)
Erreur : length(f1) == length(f2) is not TRUE
De plus : Warning messages:
1: In logdose:geno :
l'expression numérique a 125 éléments : seul le premier est utilisé
2: In logdose:geno :
l'expression numérique a 125 éléments : seul le premier est utilisé
Enter a frame number, or 0 to exit
1: lmer(y ~ logdose * geno + (1 | geno/logdose) - 1, family =
"quasibinomial", data = dataf)
2: eval.parent(mc)
3: eval(expr, p)
4: eval(expr, envir, enclos)
5: glmer(formula = y ~ logdose * geno + (1 | geno/logdose) - 1, data =
dataf, family = "quasibinomi
6: lmerFactorList(formula, fr, 0, 0)
7: sapply(seq_along(fl)[-1], function(i) isNested(fl[[i - 1]], fl[[i]]))
8: lapply(X, FUN, ...)
9: FUN(2[[1]], ...)
10: isNested(fl[[i - 1]], fl[[i]])
11: stopifnot(length(f1) == length(f2))
Sélection : summary (model1)
Enter an item from the menu, or 0 to exit
Sélection : 10
Called from: eval(expr, envir, enclos)
Browse[1]> f1
[1] D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1D1
D1D1 D1D1 D1D1 D1D1 D1D1
[20] D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1D1 D1R D1R D1R D1R D1R
D1R D1R D1R D1S D1S
[39] D1S D1S D1S D1S D3D3 D3D3 D3D3 D3D3 D3D3 D3D3 D3D3 D3D3 D3D3 D3D3
D3D3 D3D3 D3D3 D3D3 D3D3
[58] D3D3 D3D3 D3D3 D3D3 D3D3 D3D3 D3D3 D3R D3R D3R D3R D3R D3R D3R
D3R D3S D3S D3S D3S
[77] D3S D3S D3S D3S D3S D3S D3S D3S D3S D3S D3S D3S D3S D3S
RR RR RR RR RR
[96] RR RR RR RR RR RR RR RS RS RS RS RS RS RS
RS RS RS SS SS
[115] SS SS SS SS SS SS SS SS SS SS SS
Levels: D1D1 D1R D1S D3D3 D3R D3S RR RS SS
Browse[1]> f2
[1] -3.397940009 -2.397940009 -1.397940009 -0.397940009 0.602059991
Levels: -3.397940009 -2.397940009 -1.397940009 -0.397940009 0.602059991
As you can see, the model does not work because it does not consider all the
logdose values...
Could someone help me in finding what is wrong or in suggesting maybe
another path to follow for my statistical analysis?
Cheers
Pierrick
--
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