Hi,
I tried to do a nested Anova with the attached Data. My response
variable is "survivors" and I would like to know the effect of
(insect-egg clutch) "size", "position" (of clutch on twig) and "clone"
(/plant genotype) on the survival of eggs (due to predation). Each plant
was provided with three different sizes of clutches (45,15,5) and had
pseudo-replications of size 15 and 5 on it.
_Question 1: right code for nested Anova_
Here part of the code I used:
clone1<-as.factor(ifelse(clone=="Gudrun",1,ifelse(clone=="Loden",2,ifelse(clone=="021",3,4))))
plant1<-as.factor(ifelse(plant==1,1,ifelse(plant==2,2,ifelse(plant==3,3,ifelse(plant==4,4,ifelse(plant==5,5,ifelse(plant==6,6,
ifelse(plant==7,7,ifelse(plant==8,8,ifelse(plant==9,9,ifelse(plant==10,10,ifelse(plant==11,11,ifelse(plant==12,12,
ifelse(plant==13,13,ifelse(plant==14,14,ifelse(plant==15,15,ifelse(plant==16,16,ifelse(plant==17,17,ifelse(plant==18,18,
ifelse(plant==19,19,ifelse(plant==20,20,ifelse(plant==21,21,ifelse(plant==22,22,ifelse(plant==23,23,ifelse(plant==24,24,
ifelse(plant==25,25,ifelse(plant==26,26,ifelse(plant==27,27,ifelse(plant==28,28,ifelse(plant==29,29,ifelse(plant==30,30,
ifelse(plant==31,31,ifelse(plant==32,32,ifelse(plant==33,33,ifelse(plant==34,34,ifelse(plant==35,35,ifelse(plant==36,36,
ifelse(plant==37,37,ifelse(plant==38,38,ifelse(plant==39,39,ifelse(plant==40,40,ifelse(plant==41,41,ifelse(plant==42,42,43)))))))))))))))))))))))))))))))))))))))))))
size1<-as.factor(ifelse(size==5,1,ifelse(size==15,2,3)))
position1<-as.factor(ifelse(position==1,1,ifelse(position==2,2,ifelse(position==3,3,
ifelse(position==4,4,ifelse(position==5,5,ifelse(position==6,6,ifelse(position==7,7,
ifelse(position==8,8,ifelse(position==9,9,ifelse(position==10,10,ifelse(position==11,11,ifelse(position==12,12,13)))))))))))))
ANOVA<-aov(survivors~(clone1*size1*position1)+Error(plant1/(size1*position1)))
After that command it says: "Error() model is singular". Even after
googling (looks like many people had that problem) I am still uncertain
how to solve that, mainly because I am not sure if the used code for the
Anova meets my set up requirements. If I am using
"ANOVA<-aov(survivors~clone1*plant1/(size1*position1)))" R sometimes is
even crashing down.
_Question 2: normal distribution and homogeneity of variance_
So fare I tested "normal" Anovas by doing a linear model following
plotting and KS-test and Bartlett-test including Q-Q-plot.
fm<-lm(survivors~clone1*plant1*size1*position1)
Residuen<-resid(fm) Here R is crashing again!!!
ks.test(Residuen,mean(Residuen),sd(Residuen))
plot(density(Residuen))
qqnorm(Residuen)
par(mfrow=c(2,2))
plot(fm)
bartlett.test(survivors~clone1*plant1*position1*size1)
Is that too much to compute for R? Is it OK to look for the
preconditions for that nested Anova in that way, or do I have to use the
same code for the Bartlett also (or evaluate only by looking at the
Graphs (Zuur et al. 2010))??????
Thank you very much in advance
Jörg
clone plant size position survivors
1 Gudrun 1 45 3 0.95555556
2 Gudrun 1 15 1 1.00000000
3 Gudrun 1 15 8 0.93333333
4 Gudrun 1 15 11 0.00000000
5 Gudrun 1 5 2 0.00000000
6 Gudrun 1 5 4 0.40000000
7 Gudrun 1 5 5 0.60000000
8 Gudrun 1 5 6 0.80000000
9 Gudrun 1 5 7 0.80000000
10 Gudrun 1 5 9 0.40000000
11 Gudrun 1 5 10 0.80000000
12 Gudrun 1 5 12 0.80000000
13 Gudrun 1 5 13 1.00000000
14 Gudrun 2 45 1 0.82222222
15 Gudrun 2 15 5 0.00000000
16 Gudrun 2 15 7 0.93333333
17 Gudrun 2 15 13 0.80000000
18 Gudrun 2 5 2 0.20000000
19 Gudrun 2 5 3 0.00000000
20 Gudrun 2 5 4 0.60000000
21 Gudrun 2 5 6 0.60000000
22 Gudrun 2 5 8 1.00000000
23 Gudrun 2 5 9 0.00000000
24 Gudrun 2 5 10 0.40000000
25 Gudrun 2 5 11 1.00000000
26 Gudrun 2 5 12 1.00000000
27 Gudrun 3 45 8 1.00000000
28 Gudrun 3 15 1 1.00000000
29 Gudrun 3 15 7 1.00000000
30 Gudrun 3 15 9 1.00000000
31 Gudrun 3 5 2 0.00000000
32 Gudrun 3 5 3 1.00000000
33 Gudrun 3 5 4 0.00000000
34 Gudrun 3 5 5 0.60000000
35 Gudrun 3 5 6 1.00000000
36 Gudrun 3 5 10 0.80000000
37 Gudrun 3 5 11 1.00000000
38 Gudrun 3 5 12 1.00000000
39 Gudrun 3 5 13 0.60000000
40 Gudrun 4 45 13 0.86666667
41 Gudrun 4 15 4 0.86666667
42 Gudrun 4 15 8 0.93333333
43 Gudrun 4 15 11 1.00000000
44 Gudrun 4 5 1 1.00000000
45 Gudrun 4 5 2 1.00000000
46 Gudrun 4 5 3 0.20000000
47 Gudrun 4 5 5 1.00000000
48 Gudrun 4 5 6 1.00000000
49 Gudrun 4 5 7 0.00000000
50 Gudrun 4 5 9 1.00000000
51 Gudrun 4 5 10 1.00000000
52 Gudrun 4 5 12 0.20000000
53 Gudrun 5 45 10 0.95555556
54 Gudrun 5 15 5 1.00000000
55 Gudrun 5 15 9 0.60000000
56 Gudrun 5 15 13 0.53333333
57 Gudrun 5 5 1 0.00000000
58 Gudrun 5 5 2 0.00000000
59 Gudrun 5 5 3 1.00000000
60 Gudrun 5 5 4 1.00000000
61 Gudrun 5 5 6 1.00000000
62 Gudrun 5 5 7 0.60000000
63 Gudrun 5 5 8 1.00000000
64 Gudrun 5 5 11 0.60000000
65 Gudrun 5 5 12 1.00000000
66 Gudrun 6 45 12 1.00000000
67 Gudrun 6 15 7 1.00000000
68 Gudrun 6 15 9 1.00000000
69 Gudrun 6 15 11 1.00000000
70 Gudrun 6 5 1 1.00000000
71 Gudrun 6 5 2 1.00000000
72 Gudrun 6 5 3 0.00000000
73 Gudrun 6 5 4 0.00000000
74 Gudrun 6 5 5 1.00000000
75 Gudrun 6 5 6 0.20000000
76 Gudrun 6 5 8 0.00000000
77 Gudrun 6 5 10 1.00000000
78 Gudrun 6 5 13 1.00000000
79 Gudrun 7 45 8 1.00000000
80 Gudrun 7 15 10 0.86666667
81 Gudrun 7 15 5 0.93333333
82 Gudrun 7 15 9 0.80000000
83 Gudrun 7 5 1 0.00000000
84 Gudrun 7 5 2 0.00000000
85 Gudrun 7 5 3 1.00000000
86 Gudrun 7 5 6 1.00000000
87 Gudrun 7 5 7 1.00000000
88 Gudrun 7 5 4 0.00000000
89 Gudrun 7 5 11 1.00000000
90 Gudrun 7 5 12 0.60000000
91 Gudrun 7 5 13 1.00000000
92 Gudrun 8 45 2 0.57777778
93 Gudrun 8 15 11 0.40000000
94 Gudrun 8 15 6 0.86666667
95 Gudrun 8 15 10 0.06666667
96 Gudrun 8 5 3 0.00000000
97 Gudrun 8 5 4 0.00000000
98 Gudrun 8 5 5 0.80000000
99 Gudrun 8 5 7 0.20000000
100 Gudrun 8 5 8 0.20000000
101 Gudrun 8 5 9 0.00000000
102 Gudrun 8 5 1 0.00000000
103 Gudrun 8 5 12 0.00000000
104 Gudrun 8 5 13 1.00000000
105 Gudrun 9 45 4 0.48888889
106 Gudrun 9 15 6 0.93333333
107 Gudrun 9 15 8 0.93333333
108 Gudrun 9 15 10 0.80000000
109 Gudrun 9 5 1 0.00000000
110 Gudrun 9 5 22 0.00000000
111 Gudrun 9 5 3 0.00000000
112 Gudrun 9 5 5 0.00000000
113 Gudrun 9 5 7 0.00000000
114 Gudrun 9 5 9 0.00000000
115 Gudrun 9 5 11 1.00000000
116 Gudrun 9 5 12 0.00000000
117 Gudrun 9 5 13 0.60000000
118 Gudrun 10 45 5 1.00000000
119 Gudrun 10 15 4 1.00000000
120 Gudrun 10 15 10 0.66666667
121 Gudrun 10 15 12 0.80000000
122 Gudrun 10 5 1 1.00000000
123 Gudrun 10 5 2 1.00000000
124 Gudrun 10 5 3 1.00000000
125 Gudrun 10 5 6 0.00000000
126 Gudrun 10 5 7 0.80000000
127 Gudrun 10 5 8 0.80000000
128 Gudrun 10 5 9 1.00000000
129 Gudrun 10 5 11 0.40000000
130 Gudrun 11 45 8 0.77777778
131 Gudrun 11 15 2 0.00000000
132 Gudrun 11 15 3 1.00000000
133 Gudrun 11 15 7 0.26666667
134 Gudrun 11 5 1 0.00000000
135 Gudrun 11 5 4 0.00000000
136 Gudrun 11 5 5 0.20000000
137 Gudrun 11 5 6 0.00000000
138 Gudrun 11 5 9 1.00000000
139 Gudrun 11 5 10 0.80000000
140 Gudrun 11 5 11 0.40000000
141 Gudrun 11 5 12 0.20000000
142 Gudrun 11 5 13 0.00000000
143 Loden 12 45 1 1.00000000
144 Loden 12 15 2 0.86666667
145 Loden 12 15 4 1.00000000
146 Loden 12 15 5 1.00000000
147 Loden 12 5 3 0.80000000
148 Loden 12 5 6 1.00000000
149 Loden 12 5 7 1.00000000
150 Loden 12 5 8 1.00000000
151 Loden 12 5 9 1.00000000
152 Loden 12 5 10 1.00000000
153 Loden 12 5 11 1.00000000
154 Loden 12 5 12 1.00000000
155 Loden 12 5 13 1.00000000
156 Loden 13 45 2 0.95555556
157 Loden 13 15 1 1.00000000
158 Loden 13 15 13 0.33333333
159 Loden 13 5 3 0.00000000
160 Loden 13 5 4 0.00000000
161 Loden 13 5 5 1.00000000
162 Loden 13 5 6 0.80000000
163 Loden 13 5 7 1.00000000
164 Loden 13 5 8 1.00000000
165 Loden 13 5 9 0.20000000
166 Loden 13 5 10 0.20000000
167 Loden 13 5 11 0.00000000
168 Loden 14 45 12 0.93333333
169 Loden 14 15 1 0.80000000
170 Loden 14 15 5 0.33333333
171 Loden 14 15 7 0.06666667
172 Loden 14 5 2 0.00000000
173 Loden 14 5 3 0.60000000
174 Loden 14 5 4 0.00000000
175 Loden 14 5 6 0.40000000
176 Loden 14 5 8 0.20000000
177 Loden 14 5 9 0.80000000
178 Loden 14 5 10 0.20000000
179 Loden 14 5 11 0.40000000
180 Loden 14 5 13 0.40000000
181 Loden 15 45 5 0.91111111
182 Loden 15 15 8 1.00000000
183 Loden 15 15 9 0.93333333
184 Loden 15 15 11 0.26666667
185 Loden 15 5 1 0.00000000
186 Loden 15 5 2 0.20000000
187 Loden 15 5 3 0.40000000
188 Loden 15 5 4 0.00000000
189 Loden 15 5 6 0.00000000
190 Loden 15 5 7 0.40000000
191 Loden 15 5 10 0.00000000
192 Loden 15 5 12 1.00000000
193 Loden 15 5 13 1.00000000
194 Loden 16 45 11 0.93333333
195 Loden 16 15 2 0.13333333
196 Loden 16 15 6 0.86666667
197 Loden 16 15 8 0.86666667
198 Loden 16 5 1 0.00000000
199 Loden 16 5 3 0.00000000
200 Loden 16 5 4 0.00000000
201 Loden 16 5 5 1.00000000
202 Loden 16 5 7 0.00000000
203 Loden 16 5 9 1.00000000
204 Loden 16 5 10 0.00000000
205 Loden 16 5 12 1.00000000
206 Loden 16 5 13 0.80000000
207 Loden 17 45 1 0.86666667
208 Loden 17 15 3 0.73333333
209 Loden 17 15 5 0.73333333
210 Loden 17 15 10 0.20000000
211 Loden 17 5 2 0.20000000
212 Loden 17 5 4 0.80000000
213 Loden 17 5 6 0.20000000
214 Loden 17 5 7 0.80000000
215 Loden 17 5 8 0.00000000
216 Loden 17 5 9 0.00000000
217 Loden 17 5 11 0.00000000
218 Loden 17 5 12 0.00000000
219 Loden 18 45 5 0.77777778
220 Loden 18 15 1 0.00000000
221 Loden 18 15 4 0.00000000
222 Loden 18 15 7 0.86666667
223 Loden 18 5 2 1.00000000
224 Loden 18 5 3 1.00000000
225 Loden 18 5 6 0.80000000
226 Loden 18 5 8 1.00000000
227 Loden 18 5 9 1.00000000
228 Loden 18 5 10 0.80000000
229 Loden 18 5 11 1.00000000
230 Loden 18 5 12 1.00000000
231 Loden 18 5 13 0.00000000
232 Loden 19 45 3 0.62222222
233 Loden 19 15 2 0.20000000
234 Loden 19 15 6 0.00000000
235 Loden 19 5 1 0.20000000
236 Loden 19 5 4 0.80000000
237 Loden 19 5 5 0.00000000
238 Loden 19 5 7 0.40000000
239 Loden 19 5 8 0.60000000
240 Loden 19 5 9 0.00000000
241 Loden 19 5 10 1.00000000
242 Loden 19 5 11 0.00000000
243 Loden 19 5 12 1.00000000
244 Loden 20 45 5 0.97777778
245 Loden 20 15 3 0.73333333
246 Loden 20 15 6 1.00000000
247 Loden 20 15 8 1.00000000
248 Loden 20 5 1 0.00000000
249 Loden 20 5 2 0.60000000
250 Loden 20 5 4 0.00000000
251 Loden 20 5 7 1.00000000
252 Loden 20 5 9 1.00000000
253 Loden 20 5 10 1.00000000
254 Loden 20 5 11 1.00000000
255 Loden 20 5 12 1.00000000
256 Loden 20 5 13 1.00000000
257 Loden 21 45 6 1.00000000
258 Loden 21 15 2 0.53333333
259 Loden 21 15 5 1.00000000
260 Loden 21 15 10 0.40000000
261 Loden 21 5 1 1.00000000
262 Loden 21 5 3 1.00000000
263 Loden 21 5 4 1.00000000
264 Loden 21 5 7 0.80000000
265 Loden 21 5 8 0.60000000
266 Loden 21 5 99 1.00000000
267 Loden 21 5 11 0.00000000
268 Loden 21 5 12 0.00000000
269 Loden 21 5 13 0.00000000
270 021 22 45 3 0.31111111
271 021 22 15 5 0.60000000
272 021 22 15 8 0.93333333
273 021 22 15 12 0.73333333
274 021 22 5 1 0.80000000
275 021 22 5 2 0.00000000
276 021 22 5 4 1.00000000
277 021 22 5 6 0.60000000
278 021 22 5 7 0.60000000
279 021 22 5 9 0.00000000
280 021 22 5 10 1.00000000
281 021 22 5 11 0.00000000
282 021 22 5 13 0.00000000
283 021 23 45 8 0.93333333
284 021 23 15 2 1.00000000
285 021 23 15 11 1.00000000
286 021 23 15 12 0.93333333
287 021 23 5 1 0.40000000
288 021 23 5 3 0.80000000
289 021 23 5 4 0.00000000
290 021 23 5 5 0.20000000
291 021 23 5 6 0.00000000
292 021 23 5 7 0.00000000
293 021 23 5 9 0.40000000
294 021 23 5 10 0.40000000
295 021 23 5 13 1.00000000
296 021 24 45 4 0.84444444
297 021 24 15 10 1.00000000
298 021 24 15 11 1.00000000
299 021 24 15 12 0.93333333
300 021 24 5 1 0.20000000
301 021 24 5 2 0.00000000
302 021 24 5 3 0.80000000
303 021 24 5 5 1.00000000
304 021 24 5 6 0.80000000
305 021 24 5 7 1.00000000
306 021 24 5 8 0.80000000
307 021 24 5 9 0.60000000
308 021 24 5 13 0.00000000
309 021 25 45 7 0.51111111
310 021 25 15 10 1.00000000
311 021 25 15 11 0.20000000
312 021 25 15 13 0.33333333
313 021 25 5 1 0.60000000
314 021 25 5 2 0.20000000
315 021 25 5 3 0.00000000
316 021 25 5 4 0.00000000
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318 021 25 5 6 0.20000000
319 021 25 5 8 0.40000000
320 021 25 5 9 0.00000000
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322 021 26 45 2 0.68888889
323 021 26 15 6 0.80000000
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325 021 26 15 10 0.60000000
326 021 26 5 1 0.80000000
327 021 26 5 3 1.00000000
328 021 26 5 4 0.60000000
329 021 26 5 5 0.00000000
330 021 26 5 7 1.00000000
331 021 26 5 9 0.00000000
332 021 26 5 11 0.00000000
333 021 26 5 12 1.00000000
334 021 26 5 13 0.00000000
335 021 27 45 4 0.75555556
336 021 27 15 2 0.13333333
337 021 27 15 6 0.86666667
338 021 27 15 9 1.00000000
339 021 27 5 1 0.00000000
340 021 27 5 3 1.00000000
341 021 27 5 5 1.00000000
342 021 27 5 7 0.00000000
343 021 27 5 8 0.80000000
344 021 27 5 10 0.00000000
345 021 27 5 11 1.00000000
346 021 27 5 12 1.00000000
347 021 28 45 1 0.31111111
348 021 28 15 5 0.00000000
349 021 28 15 9 0.93333333
350 021 28 15 10 1.00000000
351 021 28 5 2 0.80000000
352 021 28 5 3 0.20000000
353 021 28 5 4 0.00000000
354 021 28 5 6 0.00000000
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356 021 28 5 8 0.20000000
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358 021 28 5 12 0.20000000
359 021 28 5 13 1.00000000
360 021 29 45 9 0.84444444
361 021 29 15 1 0.06666667
362 021 29 15 3 0.86666667
363 021 29 15 12 0.80000000
364 021 29 5 2 0.20000000
365 021 29 5 4 0.60000000
366 021 29 5 5 0.00000000
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550 183 43 5 11 1.00000000
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