Dear All,
I would like to ask a couple of questions on a LME model.
I tested 4 selection lines at 4 food concentrations against a standard
competitor stock. I had 3 replicate cages per selection line. In each cage
I have 10 vials. I counted the number of wild type flies and competitor
stock emerging in each vial. My main question is: is there any difference
between selection lines?
I did fit the following model:
mod1<-lme(wt~selection*food, random=~1|c1/food, competition)
The quantile plot is straight, the plot of residuals looks good, the
standardized residual plot seems ok... BUT if I do a bartlett test for
homogeneity of variance (done by cage*temperature, 48 cages in all) the
variance is NOT homogeneous. Is my model still accettable? if not, what
could I do?
The same model on asin(sqrt(wild type/total)) does not fit anywhere as well.
my dataset (NB *wt* is the number of wild type flies in each vial; *spa* is
the number of comepetitor stock flies in each vial, *p1* is
asin(sqrt(competition$prop)), *c1* is the cage nested in selection using
getGroups...):
selection cage food wt spa tot prop p1 c1
1 25 1 100 21 22 43 0.48837209 0.7737692 25/1
2 25 1 100 21 22 43 0.48837209 0.7737692 25/1
3 25 1 100 5 5 10 0.50000000 0.7853982 25/1
4 25 1 100 21 17 38 0.55263158 0.8381274 25/1
5 25 1 100 13 22 35 0.37142857 0.6553659 25/1
6 25 1 100 24 18 42 0.57142857 0.8570719 25/1
7 25 1 100 22 19 41 0.53658537 0.8220163 25/1
8 25 1 100 15 23 38 0.39473684 0.6793415 25/1
9 25 1 100 19 19 38 0.50000000 0.7853982 25/1
10 25 1 100 18 19 37 0.48648649 0.7718830 25/1
11 25 2 100 21 22 43 0.48837209 0.7737692 25/2
12 25 2 100 24 18 42 0.57142857 0.8570719 25/2
13 25 2 100 9 21 30 0.30000000 0.5796397 25/2
14 25 2 100 2 13 15 0.13333333 0.3737922 25/2
15 25 2 100 10 11 21 0.47619048 0.7615796 25/2
16 25 2 100 26 20 46 0.56521739 0.8508019 25/2
17 25 2 100 11 17 28 0.39285714 0.6774179 25/2
18 25 2 100 9 22 31 0.29032258 0.5690309 25/2
19 25 2 100 15 22 37 0.40540541 0.6902300 25/2
20 25 2 100 19 24 43 0.44186047 0.7271268 25/2
21 25 3 100 22 23 45 0.48888889 0.7742861 25/3
22 25 3 100 13 22 35 0.37142857 0.6553659 25/3
23 25 3 100 18 21 39 0.46153846 0.7468986 25/3
24 25 3 100 12 20 32 0.37500000 0.6590580 25/3
25 25 3 100 17 20 37 0.45945946 0.7448131 25/3
26 25 3 100 15 26 41 0.36585366 0.6495880 25/3
27 25 3 100 19 0 19 1.00000000 1.5707963 25/3
28 25 3 100 25 23 48 0.52083333 0.8062375 25/3
29 25 3 100 15 19 34 0.44117647 0.7264381 25/3
30 25 3 100 12 21 33 0.36363636 0.6472848 25/3
31 18 1 100 30 23 53 0.56603774 0.8516294 18/1
32 18 1 100 18 22 40 0.45000000 0.7353145 18/1
33 18 1 100 20 26 46 0.43478261 0.7199944 18/1
34 18 1 100 21 16 37 0.56756757 0.8531731 18/1
35 18 1 100 17 14 31 0.54838710 0.8338611 18/1
36 18 1 100 26 21 47 0.55319149 0.8386905 18/1
37 18 1 100 19 23 42 0.45238095 0.7377068 18/1
38 18 1 100 24 19 43 0.55813953 0.8436695 18/1
39 18 1 100 25 21 46 0.54347826 0.8289314 18/1
40 18 1 100 14 20 34 0.41176471 0.6966984 18/1
41 18 2 100 18 16 34 0.52941176 0.8148269 18/2
42 18 2 100 23 19 42 0.54761905 0.8330895 18/2
43 18 2 100 19 20 39 0.48717949 0.7725762 18/2
44 18 2 100 25 20 45 0.55555556 0.8410687 18/2
45 18 2 100 19 21 40 0.47500000 0.7603877 18/2
46 18 2 100 20 23 43 0.46511628 0.7504861 18/2
47 18 2 100 23 24 47 0.48936170 0.7747591 18/2
48 18 2 100 22 20 42 0.52380952 0.8092167 18/2
49 18 2 100 25 19 44 0.56818182 0.8537931 18/2
50 18 2 100 19 26 45 0.42222222 0.7073032 18/2
51 18 3 100 21 20 41 0.51219512 0.7975945 18/3
52 18 3 100 22 24 46 0.47826087 0.7636522 18/3
53 18 3 100 19 26 45 0.42222222 0.7073032 18/3
54 18 3 100 17 22 39 0.43589744 0.7211187 18/3
55 18 3 100 22 23 45 0.48888889 0.7742861 18/3
56 18 3 100 20 25 45 0.44444444 0.7297277 18/3
57 18 3 100 25 23 48 0.52083333 0.8062375 18/3
58 18 3 100 24 21 45 0.53333333 0.8187562 18/3
59 18 3 100 23 25 48 0.47916667 0.7645588 18/3
60 18 3 100 25 22 47 0.53191489 0.8173348 18/3
61 s 1 100 22 23 45 0.48888889 0.7742861 s/1
62 s 1 100 25 18 43 0.58139535 0.8671574 s/1
63 s 1 100 19 20 39 0.48717949 0.7725762 s/1
64 s 1 100 22 19 41 0.53658537 0.8220163 s/1
65 s 1 100 18 18 36 0.50000000 0.7853982 s/1
66 s 1 100 23 18 41 0.56097561 0.8465259 s/1
67 s 1 100 23 20 43 0.53488372 0.8203102 s/1
68 s 1 100 24 21 45 0.53333333 0.8187562 s/1
69 s 1 100 21 18 39 0.53846154 0.8238977 s/1
70 s 1 100 25 22 47 0.53191489 0.8173348 s/1
71 s 2 100 20 24 44 0.45454545 0.7398808 s/2
72 s 2 100 18 19 37 0.48648649 0.7718830 s/2
73 s 2 100 20 18 38 0.52631579 0.8117261 s/2
74 s 2 100 19 11 30 0.63333333 0.9203646 s/2
75 s 2 100 20 23 43 0.46511628 0.7504861 s/2
76 s 2 100 23 22 45 0.51111111 0.7965102 s/2
77 s 2 100 23 24 47 0.48936170 0.7747591 s/2
78 s 2 100 24 18 42 0.57142857 0.8570719 s/2
79 s 2 100 20 17 37 0.54054054 0.8259833 s/2
80 s 2 100 23 20 43 0.53488372 0.8203102 s/2
81 s 3 100 19 21 40 0.47500000 0.7603877 s/3
82 s 3 100 17 18 35 0.48571429 0.7711105 s/3
83 s 3 100 10 19 29 0.34482759 0.6276203 s/3
84 s 3 100 17 21 38 0.44736842 0.7326689 s/3
85 s 3 100 20 22 42 0.47619048 0.7615796 s/3
86 s 3 100 8 17 25 0.32000000 0.6012642 s/3
87 s 3 100 21 12 33 0.63636364 0.9235115 s/3
88 s 3 100 17 22 39 0.43589744 0.7211187 s/3
89 s 3 100 18 21 39 0.46153846 0.7468986 s/3
90 s 3 100 21 22 43 0.48837209 0.7737692 s/3
91 l 1 100 21 20 41 0.51219512 0.7975945 l/1
92 l 1 100 21 20 41 0.51219512 0.7975945 l/1
93 l 1 100 15 18 33 0.45454545 0.7398808 l/1
94 l 1 100 4 8 12 0.33333333 0.6154797 l/1
95 l 1 100 11 10 21 0.52380952 0.8092167 l/1
96 l 1 100 5 18 23 0.21739130 0.4850498 l/1
97 l 1 100 9 18 27 0.33333333 0.6154797 l/1
98 l 1 100 6 18 24 0.25000000 0.5235988 l/1
99 l 1 100 14 1 15 0.93333333 1.3096389 l/1
100 l 1 100 15 26 41 0.36585366 0.6495880 l/1
101 l 2 100 21 20 41 0.51219512 0.7975945 l/2
102 l 2 100 21 21 42 0.50000000 0.7853982 l/2
103 l 2 100 8 12 20 0.40000000 0.6847192 l/2
104 l 2 100 14 9 23 0.60869565 0.8949687 l/2
105 l 2 100 14 12 26 0.53846154 0.8238977 l/2
106 l 2 100 27 23 50 0.54000000 0.8254410 l/2
107 l 2 100 7 16 23 0.30434783 0.5843739 l/2
108 l 2 100 13 23 36 0.36111111 0.6446581 l/2
109 l 2 100 24 25 49 0.48979592 0.7751934 l/2
110 l 2 100 26 24 50 0.52000000 0.8054035 l/2
111 l 3 100 23 22 45 0.51111111 0.7965102 l/3
112 l 3 100 23 25 48 0.47916667 0.7645588 l/3
113 l 3 100 21 23 44 0.47727273 0.7626631 l/3
114 l 3 100 26 21 47 0.55319149 0.8386905 l/3
115 l 3 100 22 16 38 0.57894737 0.8646773 l/3
116 l 3 100 22 22 44 0.50000000 0.7853982 l/3
117 l 3 100 25 20 45 0.55555556 0.8410687 l/3
118 l 3 100 29 26 55 0.52727273 0.8126844 l/3
119 l 3 100 26 20 46 0.56521739 0.8508019 l/3
120 l 3 100 24 22 46 0.52173913 0.8071441 l/3
121 25 1 50 20 18 38 0.52631579 0.8117261 25/1
122 25 1 50 19 19 38 0.50000000 0.7853982 25/1
123 25 1 50 16 23 39 0.41025641 0.6951656 25/1
124 25 1 50 20 25 45 0.44444444 0.7297277 25/1
125 25 1 50 17 23 40 0.42500000 0.7101140 25/1
126 25 1 50 20 20 40 0.50000000 0.7853982 25/1
127 25 1 50 19 21 40 0.47500000 0.7603877 25/1
128 25 1 50 21 26 47 0.44680851 0.7321058 25/1
129 25 1 50 18 20 38 0.47368421 0.7590702 25/1
130 25 1 50 8 22 30 0.26666667 0.5426391 25/1
131 25 2 50 17 24 41 0.41463415 0.6996120 25/2
132 25 2 50 3 19 22 0.13636364 0.3782282 25/2
133 25 2 50 8 14 22 0.36363636 0.6472848 25/2
134 25 2 50 19 22 41 0.46341463 0.7487801 25/2
135 25 2 50 3 24 27 0.11111111 0.3398369 25/2
136 25 2 50 15 22 37 0.40540541 0.6902300 25/2
137 25 2 50 2 9 11 0.18181818 0.4405107 25/2
138 25 2 50 16 19 35 0.45714286 0.7424884 25/2
139 25 2 50 1 23 24 0.04166667 0.2055689 25/2
140 25 2 50 18 16 34 0.52941176 0.8148269 25/2
141 25 3 50 0 17 17 0.00000000 0.0000000 25/3
142 25 3 50 6 20 26 0.23076923 0.5010930 25/3
143 25 3 50 12 25 37 0.32432432 0.6058911 25/3
144 25 3 50 12 21 33 0.36363636 0.6472848 25/3
145 25 3 50 13 22 145 0.84827586 1.1706883 25/3
146 25 3 50 14 22 36 0.38888889 0.6733516 25/3
147 25 3 50 17 25 42 0.40476190 0.6895746 25/3
148 25 3 50 15 23 38 0.39473684 0.6793415 25/3
149 25 3 50 14 25 39 0.35897436 0.6424324 25/3
150 25 3 50 23 24 47 0.48936170 0.7747591 25/3
151 18 1 50 9 21 30 0.30000000 0.5796397 18/1
152 18 1 50 18 19 37 0.48648649 0.7718830 18/1
153 18 1 50 15 25 40 0.37500000 0.6590580 18/1
154 18 1 50 25 23 48 0.52083333 0.8062375 18/1
155 18 1 50 19 22 41 0.46341463 0.7487801 18/1
156 18 1 50 18 25 43 0.41860465 0.7036390 18/1
157 18 1 50 18 21 39 0.46153846 0.7468986 18/1
158 18 1 50 20 21 41 0.48780488 0.7732018 18/1
159 18 1 50 8 22 30 0.26666667 0.5426391 18/1
160 18 1 50 14 25 39 0.35897436 0.6424324 18/1
161 18 2 50 11 21 32 0.34375000 0.6264863 18/2
162 18 2 50 22 21 43 0.51162791 0.7970271 18/2
163 18 2 50 4 16 20 0.20000000 0.4636476 18/2
164 18 2 50 20 23 43 0.46511628 0.7504861 18/2
165 18 2 50 21 22 43 0.48837209 0.7737692 18/2
166 18 2 50 18 20 38 0.47368421 0.7590702 18/2
167 18 2 50 9 17 26 0.34615385 0.6290148 18/2
168 18 2 50 20 24 44 0.45454545 0.7398808 18/2
169 18 2 50 13 20 33 0.39393939 0.6785256 18/2
170 18 2 50 20 21 41 0.48780488 0.7732018 18/2
171 18 3 50 6 14 20 0.30000000 0.5796397 18/3
172 18 3 50 19 25 44 0.43181818 0.7170032 18/3
173 18 3 50 7 21 28 0.25000000 0.5235988 18/3
174 18 3 50 19 24 43 0.44186047 0.7271268 18/3
175 18 3 50 21 23 44 0.47727273 0.7626631 18/3
176 18 3 50 16 20 36 0.44444444 0.7297277 18/3
177 18 3 50 23 26 49 0.46938776 0.7547668 18/3
178 18 3 50 22 20 42 0.52380952 0.8092167 18/3
179 18 3 50 13 20 33 0.39393939 0.6785256 18/3
180 18 3 50 18 24 42 0.42857143 0.7137244 18/3
181 s 1 50 14 21 35 0.40000000 0.6847192 s/1
182 s 1 50 16 21 37 0.43243243 0.7176232 s/1
183 s 1 50 18 18 36 0.50000000 0.7853982 s/1
184 s 1 50 12 16 28 0.42857143 0.7137244 s/1
185 s 1 50 15 17 32 0.46875000 0.7541278 s/1
186 s 1 50 17 22 39 0.43589744 0.7211187 s/1
187 s 1 50 3 20 23 0.13043478 0.3695089 s/1
188 s 1 50 18 20 38 0.47368421 0.7590702 s/1
189 s 1 50 18 23 41 0.43902439 0.7242704 s/1
190 s 1 50 9 19 28 0.32142857 0.6027946 s/1
191 s 2 50 16 24 40 0.40000000 0.6847192 s/2
192 s 2 50 20 22 42 0.47619048 0.7615796 s/2
193 s 2 50 18 20 38 0.47368421 0.7590702 s/2
194 s 2 50 22 22 44 0.50000000 0.7853982 s/2
195 s 2 50 19 23 42 0.45238095 0.7377068 s/2
196 s 2 50 13 15 28 0.46428571 0.7496534 s/2
197 s 2 50 13 15 28 0.46428571 0.7496534 s/2
198 s 2 50 18 20 38 0.47368421 0.7590702 s/2
199 s 2 50 12 26 38 0.31578947 0.5967431 s/2
200 s 2 50 7 21 28 0.25000000 0.5235988 s/2
201 s 3 50 13 22 35 0.37142857 0.6553659 s/3
202 s 3 50 16 21 37 0.43243243 0.7176232 s/3
203 s 3 50 12 17 29 0.41379310 0.6987583 s/3
204 s 3 50 17 10 27 0.62962963 0.9165257 s/3
205 s 3 50 7 18 25 0.28000000 0.5575988 s/3
206 s 3 50 7 9 16 0.43750000 0.7227342 s/3
207 s 3 50 8 20 28 0.28571429 0.5639426 s/3
208 s 3 50 5 15 20 0.25000000 0.5235988 s/3
209 s 3 50 9 20 29 0.31034483 0.5908728 s/3
210 s 3 50 6 15 21 0.28571429 0.5639426 s/3
211 l 1 50 4 18 22 0.18181818 0.4405107 l/1
212 l 1 50 7 16 23 0.30434783 0.5843739 l/1
213 l 1 50 4 20 24 0.16666667 0.4205343 l/1
214 l 1 50 21 24 45 0.46666667 0.7520401 l/1
215 l 1 50 7 24 31 0.22580645 0.4951811 l/1
216 l 1 50 9 19 28 0.32142857 0.6027946 l/1
217 l 1 50 2 13 15 0.13333333 0.3737922 l/1
218 l 1 50 5 24 29 0.17241379 0.4281928 l/1
219 l 1 50 23 21 44 0.52272727 0.8081333 l/1
220 l 1 50 16 19 35 0.45714286 0.7424884 l/1
221 l 2 50 20 16 36 0.55555556 0.8410687 l/2
222 l 2 50 12 8 20 0.60000000 0.8860771 l/2
223 l 2 50 15 23 38 0.39473684 0.6793415 l/2
224 l 2 50 3 18 21 0.14285714 0.3875967 l/2
225 l 2 50 8 16 24 0.33333333 0.6154797 l/2
226 l 2 50 9 19 28 0.32142857 0.6027946 l/2
227 l 2 50 22 22 44 0.50000000 0.7853982 l/2
228 l 2 50 9 10 19 0.47368421 0.7590702 l/2
229 l 2 50 13 23 36 0.36111111 0.6446581 l/2
230 l 2 50 15 20 35 0.42857143 0.7137244 l/2
231 l 3 50 20 21 41 0.48780488 0.7732018 l/3
232 l 3 50 9 18 27 0.33333333 0.6154797 l/3
233 l 3 50 18 18 36 0.50000000 0.7853982 l/3
234 l 3 50 9 16 25 0.36000000 0.6435011 l/3
235 l 3 50 22 26 48 0.45833333 0.7436831 l/3
236 l 3 50 21 17 38 0.55263158 0.8381274 l/3
237 l 3 50 19 23 42 0.45238095 0.7377068 l/3
238 l 3 50 19 20 39 0.48717949 0.7725762 l/3
239 l 3 50 18 21 39 0.46153846 0.7468986 l/3
240 l 3 50 21 22 43 0.48837209 0.7737692 l/3
241 25 1 25 19 23 42 0.45238095 0.7377068 25/1
242 25 1 25 0 7 7 0.00000000 0.0000000 25/1
243 25 1 25 14 9 23 0.60869565 0.8949687 25/1
244 25 1 25 0 14 14 0.00000000 0.0000000 25/1
245 25 1 25 14 22 36 0.38888889 0.6733516 25/1
246 25 1 25 0 3 3 0.00000000 0.0000000 25/1
247 25 1 25 15 23 38 0.39473684 0.6793415 25/1
248 25 1 25 14 20 34 0.41176471 0.6966984 25/1
249 25 1 25 7 13 20 0.35000000 0.6330518 25/1
250 25 1 25 15 20 35 0.42857143 0.7137244 25/1
251 25 2 25 0 9 9 0.00000000 0.0000000 25/2
252 25 2 25 16 23 39 0.41025641 0.6951656 25/2
253 25 2 25 15 25 40 0.37500000 0.6590580 25/2
254 25 2 25 17 19 36 0.47222222 0.7576061 25/2
255 25 2 25 1 10 11 0.09090909 0.3062774 25/2
256 25 2 25 2 20 22 0.09090909 0.3062774 25/2
257 25 2 25 7 15 22 0.31818182 0.5993139 25/2
258 25 2 25 1 18 19 0.05263158 0.2314774 25/2
259 25 2 25 3 22 25 0.12000000 0.3537416 25/2
260 25 2 25 9 21 30 0.30000000 0.5796397 25/2
261 25 3 25 2 23 25 0.08000000 0.2867566 25/3
262 25 3 25 1 19 20 0.05000000 0.2255134 25/3
263 25 3 25 8 21 29 0.27586207 0.5529804 25/3
264 25 3 25 10 20 30 0.33333333 0.6154797 25/3
265 25 3 25 6 6 12 0.50000000 0.7853982 25/3
266 25 3 25 0 15 15 0.00000000 0.0000000 25/3
267 25 3 25 1 17 18 0.05555556 0.2379411 25/3
268 25 3 25 3 24 27 0.11111111 0.3398369 25/3
269 25 3 25 7 19 26 0.26923077 0.5455338 25/3
270 25 3 25 4 22 26 0.15384615 0.4030571 25/3
271 18 1 25 6 18 24 0.25000000 0.5235988 18/1
272 18 1 25 0 14 14 0.00000000 0.0000000 18/1
273 18 1 25 7 19 26 0.26923077 0.5455338 18/1
274 18 1 25 5 22 27 0.18518519 0.4448600 18/1
275 18 1 25 3 10 13 0.23076923 0.5010930 18/1
276 18 1 25 14 24 38 0.36842105 0.6522512 18/1
277 18 1 25 12 21 33 0.36363636 0.6472848 18/1
278 18 1 25 0 23 23 0.00000000 0.0000000 18/1
279 18 1 25 14 17 31 0.45161290 0.7369352 18/1
280 18 1 25 2 12 14 0.14285714 0.3875967 18/1
281 18 2 25 8 19 27 0.29629630 0.5755915 18/2
282 18 2 25 12 20 32 0.37500000 0.6590580 18/2
283 18 2 25 10 18 28 0.35714286 0.6405223 18/2
284 18 2 25 0 6 6 0.00000000 0.0000000 18/2
285 18 2 25 8 16 24 0.33333333 0.6154797 18/2
286 18 2 25 8 21 29 0.27586207 0.5529804 18/2
287 18 2 25 2 18 20 0.10000000 0.3217506 18/2
288 18 2 25 5 17 22 0.22727273 0.4969325 18/2
289 18 2 25 3 20 23 0.13043478 0.3695089 18/2
290 18 2 25 7 17 24 0.29166667 0.5705104 18/2
291 18 3 25 11 20 31 0.35483871 0.6381162 18/3
292 18 3 25 8 19 27 0.29629630 0.5755915 18/3
293 18 3 25 6 14 20 0.30000000 0.5796397 18/3
294 18 3 25 19 19 38 0.50000000 0.7853982 18/3
295 18 3 25 8 21 29 0.27586207 0.5529804 18/3
296 18 3 25 3 21 24 0.12500000 0.3613671 18/3
297 18 3 25 4 22 26 0.15384615 0.4030571 18/3
298 18 3 25 5 18 23 0.21739130 0.4850498 18/3
299 18 3 25 6 17 23 0.26086957 0.5360615 18/3
300 18 3 25 19 22 41 0.46341463 0.7487801 18/3
301 s 1 25 9 19 28 0.32142857 0.6027946 s/1
302 s 1 25 2 17 19 0.10526316 0.3304226 s/1
303 s 1 25 9 22 31 0.29032258 0.5690309 s/1
304 s 1 25 9 19 28 0.32142857 0.6027946 s/1
305 s 1 25 12 19 31 0.38709677 0.6715128 s/1
306 s 1 25 5 19 24 0.20833333 0.4739849 s/1
307 s 1 25 2 15 17 0.11764706 0.3501058 s/1
308 s 1 25 12 22 34 0.35294118 0.6361321 s/1
309 s 1 25 0 8 8 0.00000000 0.0000000 s/1
310 s 1 25 15 23 38 0.39473684 0.6793415 s/1
311 s 2 25 9 15 24 0.37500000 0.6590580 s/2
312 s 2 25 17 19 36 0.47222222 0.7576061 s/2
313 s 2 25 17 17 34 0.50000000 0.7853982 s/2
314 s 2 25 12 16 28 0.42857143 0.7137244 s/2
315 s 2 25 6 21 27 0.22222222 0.4908827 s/2
316 s 2 25 13 27 40 0.32500000 0.6066126 s/2
317 s 2 25 7 16 23 0.30434783 0.5843739 s/2
318 s 2 25 0 9 9 0.00000000 0.0000000 s/2
319 s 2 25 11 18 29 0.37931034 0.6635047 s/2
320 s 2 25 5 19 24 0.20833333 0.4739849 s/2
321 s 3 25 11 18 29 0.37931034 0.6635047 s/3
322 s 3 25 12 16 28 0.42857143 0.7137244 s/3
323 s 3 25 2 15 17 0.11764706 0.3501058 s/3
324 s 3 25 12 16 28 0.42857143 0.7137244 s/3
325 s 3 25 13 20 33 0.39393939 0.6785256 s/3
326 s 3 25 9 23 32 0.28125000 0.5589899 s/3
327 s 3 25 2 18 20 0.10000000 0.3217506 s/3
328 s 3 25 0 11 11 0.00000000 0.0000000 s/3
329 s 3 25 1 5 6 0.16666667 0.4205343 s/3
330 s 3 25 6 19 25 0.24000000 0.5119727 s/3
331 l 1 25 1 16 17 0.05882353 0.2449787 l/1
332 l 1 25 16 17 33 0.48484848 0.7702443 l/1
333 l 1 25 1 14 15 0.06666667 0.2611574 l/1
334 l 1 25 4 21 25 0.16000000 0.4115168 l/1
335 l 1 25 5 20 25 0.20000000 0.4636476 l/1
336 l 1 25 4 20 24 0.16666667 0.4205343 l/1
337 l 1 25 4 14 18 0.22222222 0.4908827 l/1
338 l 1 25 1 15 16 0.06250000 0.2526803 l/1
339 l 1 25 12 22 34 0.35294118 0.6361321 l/1
340 l 1 25 4 20 24 0.16666667 0.4205343 l/1
341 l 2 25 19 24 43 0.44186047 0.7271268 l/2
342 l 2 25 4 22 26 0.15384615 0.4030571 l/2
343 l 2 25 3 18 21 0.14285714 0.3875967 l/2
344 l 2 25 1 17 18 0.05555556 0.2379411 l/2
345 l 2 25 1 16 17 0.05882353 0.2449787 l/2
346 l 2 25 19 20 39 0.48717949 0.7725762 l/2
347 l 2 25 0 16 16 0.00000000 0.0000000 l/2
348 l 2 25 1 10 11 0.09090909 0.3062774 l/2
349 l 2 25 0 4 4 0.00000000 0.0000000 l/2
350 l 2 25 8 19 27 0.29629630 0.5755915 l/2
351 l 3 25 11 16 27 0.40740741 0.6922680 l/3
352 l 3 25 10 15 25 0.40000000 0.6847192 l/3
353 l 3 25 6 12 18 0.33333333 0.6154797 l/3
354 l 3 25 16 18 34 0.47058824 0.7559694 l/3
355 l 3 25 14 21 35 0.40000000 0.6847192 l/3
356 l 3 25 14 20 34 0.41176471 0.6966984 l/3
357 l 3 25 18 21 39 0.46153846 0.7468986 l/3
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========================
Federico C.F. Calboli
Department of Biology
University College London
Room 327
Darwin Building
Gower Street
London
WClE 6BT
Tel: (+44) 020 7679 4395
Fax (+44) 020 7679 7096
f.calboli at ucl.ac.uk
Federico - I'm coming to this from outside the field, and I hope Doug Bates will reply ... but I would question the use of normal theory linear models with counted data. Why not logistic regression (binomial) or log-linear models (Poisson) ? You see, coming to the question from outside, I'm still back at the point of needing to answer *that* question for myself, before I can think about the random-effects aspects of the problem. I don't yet understand "vials within cages". Are the vials open or closed ? What determines the total number per vial ? (Thus, is a binomial or a Poisson error model appropriate ?) And, what is "temperature" in the Bartlett test, and why does it make sense to *cross* "cage" with "temperature" ? Was every cage in fact tested at multiple temperatures ? (I would guess not, but I don't fully understand the experimental setup.) So ... in the end ... I offer you many questions and *zero* answers ! - tom blackwell - u michigan medical school - ann arbor - On Tue, 13 May 2003, Federico Calboli wrote:> I would like to ask a couple of questions on a LME model. > > I tested 4 selection lines at 4 food concentrations against a standard > competitor stock. I had 3 replicate cages per selection line. In each cage > I have 10 vials. I counted the number of wild type flies and competitor > stock emerging in each vial. My main question is: is there any difference > between selection lines? > > I did fit the following model: > > mod1<-lme(wt~selection*food, random=~1|c1/food, competition) > > The quantile plot is straight, the plot of residuals looks good, the > standardized residual plot seems ok... BUT if I do a bartlett test for > homogeneity of variance (done by cage*temperature, 48 cages in all) the > variance is NOT homogeneous. Is my model still accettable? if not, what > could I do? > > The same model on asin(sqrt(wild type/total)) does not fit anywhere as well. > > my dataset (NB *wt* is the number of wild type flies in each vial; *spa* is > the number of comepetitor stock flies in each vial, *p1* is > asin(sqrt(competition$prop)), *c1* is the cage nested in selection using > getGroups...): > > selection cage food wt spa tot prop p1 c1 > > (475 lines of data deleted) > > Federico C.F. Calboli > > Department of Biology > University College London > Room 327 > Darwin Building > Gower Street > London > WClE 6BT > > Tel: (+44) 020 7679 4395 > Fax (+44) 020 7679 7096 > f.calboli at ucl.ac.uk