Dear R Community! We analyse the impact of climbing activity on cliff vegetation. During our fieldwork, we recorded 90 Transects in 3 climbing sites. The aim is to see, if the plant cover (response: Cover) is influenced only by crevice availability (predictor: Cracs), or, additional, by the distance to the climbing route (predictor: Distance). Six plots are nested within one Transect (ID.Transekt), the Transects are nested within the climbing site (Site).
I tried to fit two linear mixed models with lmer(), and to compare them
by anova():
> cracks.lmer <- lmer (Cover ~ Cracks + Distance + (Cracks + Distance |
> + Site / ID.Transekt) , method = ("ML"))
Warning message:
In .local(x, ..., value) :
Estimated variance-covariance for factors ‘ID.Transekt:Site’, ‘Site’
is singular
> cracks.lmer1 <- lmer (Cover ~ Cracks + (Cracks | Site / ID.Transekt) ,
> + data = Lmm.spalten.corr, method = ("ML"))
Warning messages:
1: In .local(x, ..., value) :
Estimated variance-covariance for factor ‘Site’ is singular
2: In .local(x, ..., value) :
nlminb returned message false convergence (8)
For this one, i changed the maxIter and msMaxIter up to 50000000, but
the Warning messages remained the same.
I have many "0" values for "Cover" and "Cracks" and tried to exclude
some of them in different ways (as all transects without vegetation
cover), but this did not help. The current version deals with all
transects wich have at least two plots with crevices.
The correlation Intercept/Cracks ist -1.000 for both models.
I attachet a symmary of the used data.frame, a randomly selected subset
and the summarys of cracks.lmer and cracks.lmer1 in a *.txt file
(modified with kate). Some xyplots are attached as *.png. (The error
message on the plots means "Error using packet 5, NA/NaN/Inf in externem
Funktionsaufruf", excluding these transects does not change the lmer
message.)
My question is: Is it possible and does it make sense to fit these date
with lmer? I do get outputs, but they dont seem really reliable to me.
I hope that this is not a stupid and unnecessary question, but i did't
really find a answer in the current mailing-lists. Thanks a lot!
Martin Klipp
R version 2.6.2 (2008-02-08)
i486-pc-linux-gnu
Kubuntu 6.06.1, precompieled packages from the CRAN-mirror.
lme4_0.99875-9
Matrix_0.999375-4
lattice_0.17-2
nlme_3.1-86
>summary (Lmm.spalten.corr)
ID.Transekt Distance Cracks Cover
11 : 6 Min. :1.0 Min. : 0.00 Min. : 0.000
12 : 6 1st Qu.:2.0 1st Qu.: 6.00 1st Qu.: 0.000
14 : 6 Median :3.5 Median : 25.00 Median : 0.100
16 : 6 Mean :3.5 Mean : 31.77 Mean : 1.133
17 : 6 3rd Qu.:5.0 3rd Qu.: 45.00 3rd Qu.: 0.700
18 : 6 Max. :6.0 Max. :244.00 Max. :33.650
(Other):450
Site
Brueggele :162
Kirchler :168
Plaetzwiese:156
#Subset of 20 randomly selectet Transects from Lmm.spalten.corr:
Grouped Data: Cover ~ Cracks + Distance | Site/ID.Transekt
ID.Transekt Distance Cracks Cover Site
1 11 1 25 0.14 Plaetzwiese
2 11 2 30 7.10 Plaetzwiese
3 11 3 53 0.80 Plaetzwiese
4 11 4 25 0.70 Plaetzwiese
5 11 5 25 0.90 Plaetzwiese
6 11 6 28 0.00 Plaetzwiese
19 14 1 10 0.00 Plaetzwiese
20 14 2 10 0.00 Plaetzwiese
21 14 3 0 0.00 Plaetzwiese
22 14 4 10 0.00 Plaetzwiese
23 14 5 0 0.00 Plaetzwiese
24 14 6 0 0.00 Plaetzwiese
31 16 1 25 0.00 Plaetzwiese
32 16 2 20 0.00 Plaetzwiese
33 16 3 38 8.20 Plaetzwiese
34 16 4 45 16.00 Plaetzwiese
35 16 5 60 6.50 Plaetzwiese
36 16 6 0 0.00 Plaetzwiese
49 19 1 45 0.18 Plaetzwiese
50 19 2 17 0.10 Plaetzwiese
51 19 3 7 0.00 Plaetzwiese
52 19 4 8 0.00 Plaetzwiese
53 19 5 0 0.00 Plaetzwiese
54 19 6 5 0.00 Plaetzwiese
67 22 1 44 0.00 Plaetzwiese
68 22 2 25 0.00 Plaetzwiese
69 22 3 0 0.00 Plaetzwiese
70 22 4 0 0.00 Plaetzwiese
71 22 5 14 0.26 Plaetzwiese
72 22 6 0 0.00 Plaetzwiese
85 25 1 65 0.35 Plaetzwiese
86 25 2 43 0.81 Plaetzwiese
87 25 3 31 2.00 Plaetzwiese
88 25 4 0 0.00 Plaetzwiese
89 25 5 0 0.00 Plaetzwiese
90 25 6 6 0.00 Plaetzwiese
109 29 1 0 0.00 Plaetzwiese
110 29 2 0 0.00 Plaetzwiese
111 29 3 105 0.85 Plaetzwiese
112 29 4 20 0.80 Plaetzwiese
113 29 5 5 0.10 Plaetzwiese
114 29 6 0 0.00 Plaetzwiese
199 44 1 72 0.85 Kirchler
200 44 2 20 0.05 Kirchler
201 44 3 3 0.00 Kirchler
202 44 4 0 0.00 Kirchler
203 44 5 9 0.00 Kirchler
204 44 6 0 0.00 Kirchler
205 45 1 35 1.75 Kirchler
206 45 2 35 1.20 Kirchler
207 45 3 19 0.30 Kirchler
208 45 4 48 0.25 Kirchler
209 45 5 30 0.00 Kirchler
210 45 6 17 0.00 Kirchler
223 48 1 48 0.12 Kirchler
224 48 2 5 0.00 Kirchler
225 48 3 10 0.00 Kirchler
226 48 4 43 2.25 Kirchler
227 48 5 39 2.90 Kirchler
228 48 6 46 0.75 Kirchler
235 50 1 24 0.25 Kirchler
236 50 2 93 0.25 Kirchler
237 50 3 0 0.00 Kirchler
238 50 4 7 0.00 Kirchler
239 50 5 0 0.00 Kirchler
240 50 6 7 0.00 Kirchler
295 60 1 55 3.10 Kirchler
296 60 2 27 1.50 Kirchler
297 60 3 50 3.60 Kirchler
298 60 4 75 5.50 Kirchler
299 60 5 65 1.25 Kirchler
300 60 6 0 0.00 Kirchler
307 62 1 75 2.00 Kirchler
308 62 2 20 0.20 Kirchler
309 62 3 10 0.30 Kirchler
310 62 4 0 0.00 Kirchler
311 62 5 10 0.00 Kirchler
312 62 6 50 2.00 Kirchler
319 64 1 109 5.20 Kirchler
320 64 2 48 2.72 Kirchler
321 64 3 67 8.00 Kirchler
322 64 4 23 0.00 Kirchler
323 64 5 0 0.00 Kirchler
324 64 6 0 0.00 Kirchler
331 66 1 76 0.01 Kirchler
332 66 2 83 1.30 Kirchler
333 66 3 24 0.30 Kirchler
334 66 4 100 13.80 Kirchler
335 66 5 25 0.00 Kirchler
336 66 6 0 0.00 Kirchler
349 69 1 103 6.05 Kirchler
350 69 2 63 1.35 Kirchler
351 69 3 20 0.00 Kirchler
352 69 4 15 0.20 Kirchler
353 69 5 0 0.00 Kirchler
354 69 6 15 0.00 Kirchler
367 72 1 244 0.15 Brueggele
368 72 2 180 1.55 Brueggele
369 72 3 165 11.95 Brueggele
370 72 4 137 5.90 Brueggele
371 72 5 145 7.20 Brueggele
372 72 6 93 4.30 Brueggele
397 77 1 6 0.02 Brueggele
398 77 2 7 0.00 Brueggele
399 77 3 25 0.00 Brueggele
400 77 4 60 0.20 Brueggele
401 77 5 59 0.70 Brueggele
402 77 6 42 0.05 Brueggele
433 83 1 55 1.10 Brueggele
434 83 2 45 1.00 Brueggele
435 83 3 0 0.00 Brueggele
436 83 4 25 0.00 Brueggele
437 83 5 15 0.10 Brueggele
438 83 6 5 0.00 Brueggele
>cracks.lmer <- (lmer (Cover ~ Cracks + Distance + (Cracks + Distance | Site /
>ID.Transekt) , data = Lmm.spalten.corr, method = ("ML"))
>summary (cracks.lmer)
Linear mixed-effects model fit by maximum likelihood
Formula: Cover ~ Cracks + Distance + (Cracks + Distance | Site/ID.Transekt)
Data: Lmm.spalten.corr
AIC BIC logLik MLdeviance REMLdeviance
1918 1981 -944 1888 1904
Random effects:
Groups Name Variance Std.Dev. Corr
ID.Transekt:Site (Intercept) 1.8358e-01 4.2846e-01
Cracks 2.1744e-03 4.6631e-02 -1.000
Distance 1.0280e-09 3.2062e-05 0.002 -0.002
Site (Intercept) 1.0328e-09 3.2137e-05
Cracks 1.0435e-09 3.2303e-05 0.044
Distance 1.0280e-09 3.2062e-05 0.057 0.122
Residual 2.0560e+00 1.4339e+00
number of obs: 486, groups: ID.Transekt:Site, 81; Site, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.585846 0.194017 -3.020
Cracks 0.040834 0.006053 6.746
Distance 0.099521 0.041494 2.398
Correlation of Fixed Effects:
(Intr) Cracks
Cracks -0.455
Distance -0.821 0.080
>cracks.lmer1 <- (lmer (Cover ~ Cracks + Distance + (Cracks + Distance | Site /
>ID.Transekt) , data = Lmm.spalten.corr, method = ("ML"))
>summary (cracks.lmer1)
Linear mixed-effects model fit by maximum likelihood
Formula: Cover ~ Cracks + (Cracks | Site/ID.Transekt)
Data: Lmm.spalten.corr
AIC BIC logLik MLdeviance REMLdeviance
1910 1943 -946.9 1894 1905
Random effects:
Groups Name Variance Std.Dev. Corr
ID.Transekt:Site (Intercept) 1.9162e-01 4.3774e-01
Cracks 2.1710e-03 4.6594e-02 -1.000
Site (Intercept) 1.1199e-09 3.3465e-05
Cracks 1.0434e-09 3.2302e-05 0.261
Residual 2.0869e+00 1.4446e+00
number of obs: 486, groups: ID.Transekt:Site, 81; Site, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.203951 0.111849 -1.823
Cracks 0.039669 0.006037 6.571
Correlation of Fixed Effects:
(Intr)
Cracks -0.688<<inline: cracks.png>>
<<inline: cracks_distance.png>>
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