[R-sig-ME] Differing variable lengths (missing data) and Model errors in lmer()
A Singh
Aditi.Singh at bristol.ac.uk
Thu Jul 9 18:52:46 CEST 2009
Dear All,
I am trying to run a nested random effects model in lmer (for R 2.9.1, lme4
version 0.999375-31 ) using data which is structured as follows:
family offsp. P1L74 P1L77 P1L91 P1L96..(n=426) peg.no ec.length syll.length
1 2 1 0 0 0 86 5.445 2.479
1 3 1 0 0 0 91 5.215 2.356
1 4 0 0 0 0 79 5.682 2.896
1 5 1 0 0 0 83 5.149 2.641
1 6 0 0 0 0 77 5.044 2.288
1 7 1 0 0 0 78 5.450 2.420
1 8 1 0 1 1 82 5.377 2.505
1 9 1 0 0 0 95 5.389 2.706
1 10 1 0 0 0 88 5.354 2.461
1 11 1 0 0 0 87 5.262 3.079
1 12 1 0 0 0 84 5.191 2.858
1 13 1 0 0 1 87 5.194 2.264
2 23 1 0 0 1 116 5.863 2.876
2 24 1 0 0 0 122 5.475 3.114
2 25 1 0 0 0 110 5.563 3.059
. . . . . . . . .
. . . . . . . . .
.
.
(60 families)
'Family' is the first Random effect (categorical variable), with 60 levels.
All columns labeled P1L(x) are a matrix of presence/absence genetic markers
for each individual in each family. There are 426 such columns(not numbered
in sequence) and each one is a random effect.
The last three columns (peg.no, ec.length and syll.length) are the three
dependent variables.
Each genetic marker column needs to be nested within each family, which
means that if I take the first phenotype, peg.no, for example, then I need
to run an analysis that partitions variance 60*426 times (~25,00 runs) for
that phenotype.
I also need an output with the Anova table, and summary, at each stage of
the run, so that I can get P values for each marker for each family, as a
way of determining whether it contributes significantly to explaining
within-family variance for that trait.
To do the above, I tried to write code for lmer() using two nested 'for'
loops, one for each level of random factor nesting (marker within family)
as follows (using a test data set [please find attached] with only the
first 10 marker columns, to see if this works):
> vc<-read.table("P:\\R\\Testvcomp10.txt",header=T)
> attach(vc)
> family<-factor(family)
> colms<-(vc)[,4:13] ## this to assign the 10 columns containing marker
data to a new variable, as column names are themselves not in any
recognisable sequence
> vcdf<-data.frame(family,peg.no,ec.length,syll.length,colms)
> library(lme4)
> for (c in levels(family))
+ { for (i in 1:length(colms))
+ { fit<-lmer(peg.no~1 + (1|c/i), vcdf)
+ }
+ summ<-summary(fit)
+ av<-anova(fit)
+ print(summ)
+ print(av)
+ }
This gives me:
Error in model.frame.default(data = vcdf, formula = peg.no ~ 1 + (1 + :
variable lengths differ (found for 'c')
On suggestion from a colleague I reframed this as:
> for(c in levels(family))
+ {
+ print("----New C:----")
+ print(c)
+ for (i in 1:length(colms))
+ {
+ fit<-lmer(peg.no~1+ (1|c/i),vcdf)
+ print(i)
+ summ<-summary(fit)
+ av<-anova(fit)
+ print(summ)
+ print(av)
+ }
+ }
..and this gave me the output:
[1] "----New C:----"
[1] "1"
Error in model.frame.default(data = vcdf, formula = peg.no ~ 1 + (1 + :
variable lengths differ (found for 'c')
Google-ing the error message has led me to plenty of links that suggest
forcing data into a data frame to fix this, but that hasn't worked.
My markers and phenotypes both have plenty of missing data (NA's in the
data.frame), and na.action=na.omit isn't solving the problem.
(I have tried this with lme, and tried to do it with the aov() command as
well and the error is pretty much the same).
I am completely new to R, and despite searching and trying various things,
can't get the code to work.
I really appreciate any corrections to this code, or alternative
command/function suggestions that I can look into, to try to do this again.
Thanks a bunch for your help,
Aditi
----------------------
A Singh
Aditi.Singh at bristol.ac.uk
School of Biological Sciences
University of Bristol
-------------- next part --------------
male.parent family offspring.id P1L55 P1L73 P1L74 P1L77 P1L91 P1L96 P1L98 P1L100 P1L114 P1L118 peg.no ec.length syll.length
1 1 3 0 0 1 0 0 0 1 1 1 0 86 5.445 2.479
1 1 4 0 0 1 0 0 0 1 1 1 0 91 5.215 2.356
1 1 5 0 1 0 0 0 0 1 1 1 0 79 5.682 2.896
1 1 6 0 0 1 0 0 0 1 1 1 0 83 5.149 2.641
1 1 7 0 0 0 0 0 0 1 1 1 0 77 5.044 2.288
1 1 8 0 0 1 0 0 0 1 1 1 0 78 5.450 2.420
1 1 9 0 0 1 0 1 1 1 1 0 0 82 5.377 2.505
1 1 10 0 0 1 0 0 0 1 1 1 0 95 5.389 2.706
1 1 11 0 0 1 0 0 0 1 1 1 0 88 5.354 2.461
1 1 12 0 0 1 0 0 0 1 1 1 1 87 5.262 3.079
1 1 13 0 0 1 0 0 0 1 1 1 0 77 5.219 2.106
1 1 14 0 0 1 0 0 0 1 1 1 0 74 5.464 2.677
1 1 15 0 0 1 0 0 0 1 1 1 0 85 5.230 2.929
1 1 16 0 1 1 0 0 0 1 1 1 0 107 5.609 2.752
1 1 17 0 0 1 0 0 0 1 1 1 0 76 5.306 2.785
1 1 18 0 0 1 0 0 0 1 1 1 0 86 5.306 2.374
1 1 19 0 0 1 0 0 0 1 1 1 0 84 5.191 2.858
1 1 20 0 0 1 0 0 1 1 1 1 0 87 5.194 2.264
21 2 23 0 1 1 0 0 1 1 1 1 0 116 5.863 2.876
21 2 24 0 0 1 0 0 0 1 1 1 0 122 5.475 3.114
21 2 25 0 1 1 0 0 0 1 1 1 0 110 5.563 3.059
21 2 26 1 0 1 0 0 0 1 0 0 0 120 5.723 2.755
21 2 27 0 0 0 0 0 0 0 1 1 0 NA NA NA
21 2 28 0 1 1 0 0 1 1 1 1 0 107 5.867 2.473
21 2 29 0 1 1 1 0 0 1 1 1 0 NA 5.956 2.436
21 2 30 0 0 1 0 0 0 1 1 1 0 103 5.601 2.544
21 2 31 1 0 1 0 0 1 1 1 1 0 92 5.768 2.978
21 2 32 0 0 1 0 0 0 1 1 1 0 127 5.710 2.461
21 2 33 0 0 1 0 0 0 1 1 1 0 115 5.697 2.528
21 2 34 0 0 1 1 0 0 1 1 1 0 105 5.743 2.813
21 2 35 0 0 1 0 1 0 1 1 1 1 103 5.745 2.609
21 2 36 NA NA NA NA NA NA NA NA NA NA 106 5.901 2.884
21 2 37 0 0 1 1 0 0 1 1 1 0 108 5.639 2.292
21 2 38 0 1 1 0 0 0 1 1 1 0 105 5.588 2.839
21 2 39 0 0 1 0 0 1 1 1 1 0 112 5.543 2.172
21 2 40 0 1 1 0 0 0 1 1 1 0 NA NA NA
21 2 41 0 0 1 0 0 1 1 1 1 0 NA NA NA
42 3 44 0 0 1 0 0 0 1 0 1 0 107 6.026 2.778
42 3 45 0 0 1 0 0 0 1 1 1 0 91 5.962 2.708
42 3 46 1 0 1 0 0 0 1 1 1 0 82 5.756 2.614
42 3 47 0 0 1 0 0 0 1 1 1 0 92 5.828 2.571
42 3 48 0 0 1 1 0 1 1 1 1 0 80 5.423 2.314
42 3 49 1 0 1 0 0 0 1 1 1 0 82 5.183 2.173
42 3 50 1 0 1 1 0 0 1 1 1 0 90 5.609 2.211
42 3 51 1 0 1 0 0 0 1 1 1 0 92 5.556 2.615
42 3 52 0 0 1 0 0 0 0 0 1 0 99 5.507 2.184
42 3 53 0 0 1 0 0 1 1 1 1 0 NA NA NA
54 4 56 0 1 0 0 0 0 1 1 1 0 118 6.380 3.339
54 4 57 0 0 0 0 0 0 1 1 1 0 104 5.919 2.805
54 4 58 0 0 1 0 0 0 1 1 1 0 120 6.183 3.097
54 4 59 0 0 1 0 0 0 1 1 0 0 103 6.414 3.554
54 4 60 0 0 1 0 0 0 1 1 1 0 NA NA NA
54 4 61 0 0 0 0 0 0 1 1 0 1 112 5.966 2.814
54 4 62 0 0 0 0 0 1 1 1 1 0 113 6.000 2.724
54 4 63 0 0 1 0 1 0 1 1 1 0 NA NA NA
54 4 64 NA NA NA NA NA NA NA NA NA NA NA NA NA
65 5 67 0 0 1 0 0 0 1 1 1 0 104 5.602 2.404
65 5 68 NA NA NA NA NA NA NA NA NA NA 99 5.683 3.045
65 5 69 1 0 1 0 0 1 1 0 1 0 98 5.979 2.465
65 5 70 0 0 1 0 0 0 1 1 1 0 108 5.936 2.343
65 5 71 NA NA NA NA NA NA NA NA NA NA NA 5.754 2.687
65 5 72 0 0 1 0 0 0 1 1 1 0 NA NA NA
65 5 73 NA NA NA NA NA NA NA NA NA NA NA NA NA
74 6 76 NA NA NA NA NA NA NA NA NA NA NA NA NA
74 6 77 0 0 1 0 0 1 1 1 1 0 110 5.767 3.392
74 6 78 0 0 1 0 0 0 0 0 1 0 99 5.515 2.924
74 6 79 0 0 1 1 0 0 1 1 1 0 93 5.582 2.368
74 6 80 0 0 1 0 0 0 1 1 1 0 104 5.933 2.536
74 6 81 0 0 1 0 0 0 1 1 1 0 98 5.683 3.112
74 6 82 0 0 1 0 0 0 1 1 1 0 97 5.706 2.817
74 6 83 0 0 1 0 0 0 1 1 1 0 113 5.938 2.839
74 6 84 0 0 1 1 0 0 1 1 1 0 84 5.796 2.120
74 6 85 1 1 1 0 0 0 1 1 1 0 98 5.643 2.695
74 6 86 0 0 1 0 0 0 1 1 1 0 95 5.855 2.553
74 6 87 0 0 1 0 0 0 1 1 1 0 101 5.938 2.212
74 6 88 0 0 1 0 0 0 1 1 1 0 NA NA NA
89 7 91 0 0 1 0 0 0 1 1 0 0 NA 5.596 2.597
89 7 92 NA NA NA NA NA NA NA NA NA NA 79 5.288 2.187
89 7 93 NA NA NA NA NA NA NA NA NA NA 93 5.448 2.717
89 7 94 NA NA NA NA NA NA NA NA NA NA 77 5.612 2.619
89 7 95 NA NA NA NA NA NA NA NA NA NA 89 5.586 2.560
89 7 96 0 0 1 0 0 0 1 1 1 0 NA NA NA
89 7 97 0 0 1 0 0 0 1 1 1 0 NA NA NA
89 7 98 0 0 1 0 0 0 1 1 1 0 NA NA NA
89 7 99 0 0 1 0 0 0 1 1 1 0 NA NA NA
89 7 100 0 0 1 0 0 0 1 1 0 0 NA NA NA
89 7 101 NA NA NA NA NA NA NA NA NA NA NA NA NA
102 8 104 1 0 1 0 0 0 1 1 1 0 113 6.352 3.074
102 8 105 0 0 1 0 0 0 1 1 1 0 117 5.894 2.505
102 8 106 1 0 0 0 0 0 1 1 1 0 103 6.118 2.651
102 8 107 1 0 1 0 0 0 1 1 1 0 134 6.336 2.669
102 8 108 0 0 1 0 0 0 1 1 1 0 112 6.212 2.412
102 8 109 1 0 1 0 0 0 1 1 1 0 130 6.095 2.744
102 8 110 1 0 1 0 0 0 1 1 1 0 112 6.410 2.874
102 8 111 0 0 1 0 0 0 1 1 1 0 102 5.788 2.578
102 8 112 0 0 1 0 0 0 1 1 1 0 115 5.996 2.490
102 8 113 0 0 1 0 0 0 1 1 1 0 124 5.977 2.860
102 8 114 0 0 0 0 0 0 1 1 1 0 108 5.863 2.704
102 8 115 0 0 1 0 0 0 1 1 1 0 116 6.102 2.515
102 8 116 0 0 1 0 0 0 1 1 1 0 105 5.811 2.506
102 8 117 0 0 1 0 0 0 1 1 1 0 NA NA NA
118 9 120 0 0 1 1 1 0 1 1 0 0 111 5.728 2.193
118 9 121 NA NA NA NA NA NA NA NA NA NA 114 5.438 2.449
118 9 122 0 0 1 0 0 0 1 1 1 0 94 5.142 2.540
118 9 123 0 0 1 0 0 1 1 1 1 0 101 5.590 2.644
118 9 124 0 0 1 0 0 0 0 1 1 0 94 5.788 2.647
118 9 125 0 0 1 0 0 0 1 1 1 0 110 5.740 2.940
118 9 126 0 0 1 0 0 0 1 1 1 0 106 5.280 2.833
118 9 127 0 0 0 0 0 0 1 1 1 0 124 5.646 2.797
118 9 128 0 0 1 0 0 0 0 0 0 0 99 5.595 2.502
118 9 129 0 0 1 0 0 0 1 0 0 0 98 5.762 2.664
118 9 130 0 0 1 0 0 0 1 1 1 0 NA 5.697 2.305
131 10 133 0 0 0 0 0 0 1 1 1 0 118 6.339 2.800
131 10 134 0 0 1 0 0 0 1 1 1 0 121 6.007 2.869
131 10 135 0 0 1 0 0 0 1 1 1 0 122 6.044 2.612
131 10 136 0 0 1 0 0 0 1 1 1 0 113 6.517 2.632
131 10 137 1 0 0 0 0 0 1 0 0 0 NA 5.962 2.919
131 10 138 1 0 1 0 0 0 1 1 1 0 116 6.084 2.723
131 10 139 0 0 0 0 0 1 0 1 1 1 117 6.095 2.793
131 10 140 0 0 0 0 0 0 1 1 1 0 114 6.445 2.831
131 10 141 0 0 0 0 0 0 1 1 1 0 115 6.232 2.480
131 10 142 NA NA NA NA NA NA NA NA NA NA 109 5.956 2.742
131 10 143 0 0 1 0 0 0 1 1 1 0 123 5.839 2.583
131 10 144 0 0 1 0 0 0 1 1 1 1 112 6.016 2.770
131 10 145 0 0 0 0 0 0 1 1 1 0 110 5.895 2.421
131 10 146 0 0 1 0 0 0 1 1 1 0 98 6.019 2.971
131 10 147 0 0 0 0 0 0 1 1 1 0 103 6.505 2.749
131 10 148 0 0 0 0 0 0 1 1 1 0 100 5.969 2.724
149 11 151 0 0 1 0 0 0 1 1 1 0 NA 5.727 2.978
149 11 152 0 0 1 0 0 0 1 1 1 0 NA NA NA
153 12 155 0 0 1 0 0 0 1 1 1 0 91 6.049 3.183
153 12 156 0 0 1 0 0 0 1 1 1 0 95 6.094 2.948
153 12 157 0 0 1 0 0 0 1 1 1 0 109 5.916 3.146
153 12 158 0 0 1 0 0 0 1 1 1 1 94 6.021 3.017
153 12 159 0 0 1 0 0 0 1 1 1 0 115 5.849 3.164
153 12 160 0 0 1 0 0 0 1 1 1 1 94 5.833 2.737
153 12 161 0 0 1 0 0 0 1 1 1 0 85 5.924 2.915
153 12 162 0 0 1 0 0 0 1 1 1 1 94 5.624 2.614
153 12 163 0 1 1 0 0 0 1 1 1 1 87 6.181 3.213
153 12 164 0 0 1 0 0 0 1 1 1 1 100 5.690 2.680
153 12 165 0 0 1 0 0 0 1 1 1 1 115 5.832 3.350
153 12 166 0 0 0 0 0 0 1 1 0 1 102 6.075 2.682
153 12 167 0 0 1 0 0 0 1 1 1 0 120 5.869 2.970
153 12 168 0 0 0 0 0 0 1 1 1 0 NA NA NA
169 13 171 1 0 1 1 0 0 1 1 1 0 87 5.590 3.360
169 13 172 0 0 1 0 0 0 1 1 1 0 80 5.673 2.606
169 13 173 0 0 1 0 0 0 0 0 1 0 91 5.875 2.452
169 13 174 0 0 0 0 0 0 1 1 0 0 79 5.622 2.566
169 13 175 0 0 1 0 0 1 1 1 1 0 82 5.960 3.270
169 14 177 1 0 1 0 0 0 1 1 1 0 83 5.640 2.411
169 14 178 0 0 1 0 0 0 1 1 1 0 84 5.668 3.251
169 14 179 1 0 1 0 0 0 1 1 1 0 79 5.704 3.227
169 14 180 0 0 1 0 0 0 1 0 1 0 103 6.024 2.972
169 14 181 0 0 1 0 0 0 1 1 1 0 76 5.756 2.742
169 14 182 1 0 1 0 0 0 0 1 1 0 85 6.049 2.998
169 14 183 0 0 1 0 0 0 1 1 1 0 74 5.880 3.246
169 14 184 0 0 1 0 0 0 1 1 1 0 102 5.978 2.655
169 14 185 1 0 1 0 0 0 1 1 1 0 83 5.532 2.716
169 14 186 NA NA NA NA NA NA NA NA NA NA 75 5.619 2.358
169 14 187 1 0 1 0 0 0 1 1 1 0 NA NA NA
169 15 189 0 0 1 0 0 0 1 1 1 0 74 5.697 3.234
169 15 190 1 1 1 1 0 0 1 1 1 0 78 5.710 2.277
169 15 191 1 0 1 0 0 0 1 0 1 0 100 6.318 3.650
169 15 192 0 0 1 0 0 0 1 1 1 0 87 5.969 2.988
169 15 193 0 0 1 1 0 1 1 0 1 0 96 6.020 2.791
169 15 194 0 0 1 0 0 0 1 1 1 1 90 5.950 2.619
169 15 195 1 0 1 0 0 0 1 1 1 0 83 6.299 3.515
169 15 196 1 0 0 0 0 0 1 1 1 0 80 5.410 2.354
169 15 197 1 0 1 0 0 0 1 1 1 0 90 5.717 3.085
169 15 198 1 0 1 0 0 0 1 1 1 0 90 5.762 3.013
169 15 199 0 0 0 0 0 0 1 0 0 0 87 5.685 2.858
169 15 200 0 0 1 0 0 0 1 1 1 0 86 5.641 2.565
169 15 201 0 0 1 0 0 0 1 1 1 0 80 5.950 2.423
169 15 202 0 0 1 0 0 0 1 1 1 0 NA 5.775 2.670
169 15 203 1 1 0 0 0 1 1 1 1 0 75 5.859 2.837
169 15 204 0 0 1 0 0 0 1 1 1 1 NA NA NA
169 15 205 1 0 1 0 0 0 1 1 1 0 NA NA NA
169 15 206 0 0 1 0 0 0 1 1 1 1 NA NA NA
207 16 209 1 0 0 0 1 0 1 1 1 0 111 5.798 2.518
207 16 210 0 0 1 0 0 0 1 1 1 0 117 6.021 2.657
207 16 211 0 0 0 0 0 0 1 0 0 0 109 5.871 2.771
212 17 214 0 1 1 0 0 0 1 1 1 0 125 5.934 3.060
212 17 215 0 0 1 1 0 0 1 1 1 0 122 5.825 3.232
212 17 216 0 1 1 1 0 0 1 1 1 0 107 6.003 2.974
212 17 217 NA NA NA NA NA NA NA NA NA NA 118 5.562 3.475
212 17 218 0 1 1 1 0 0 1 1 1 0 119 5.946 2.923
212 17 219 0 0 1 0 0 0 1 1 0 0 120 5.320 2.828
212 17 220 0 0 1 0 0 0 1 1 1 0 110 5.706 2.578
212 17 221 0 0 1 0 0 0 1 1 1 0 100 5.547 2.652
212 17 222 0 0 0 0 0 1 1 0 0 0 106 5.515 2.708
212 17 223 0 0 1 1 0 0 1 1 1 0 105 5.555 2.862
212 17 224 0 0 1 0 0 0 1 1 1 0 NA NA NA
212 17 225 0 0 1 0 0 0 1 1 1 0 NA NA NA
226 18 228 0 1 1 0 0 0 1 1 0 1 89 6.044 3.091
226 18 229 0 0 1 0 0 0 1 1 1 1 86 5.720 2.700
226 18 230 1 0 1 0 0 0 1 1 1 0 101 5.912 3.142
226 18 231 0 0 1 0 0 0 1 1 1 0 100 5.910 3.224
226 18 232 1 0 1 0 0 0 1 0 1 0 90 5.822 2.543
226 18 233 1 0 1 0 0 0 1 1 1 0 108 5.814 2.668
226 18 234 0 1 1 0 0 0 0 0 1 0 99 6.067 3.154
226 18 235 0 0 1 0 0 0 1 0 0 0 103 5.850 3.239
226 18 236 0 0 1 0 0 0 1 1 1 0 84 6.112 3.067
226 18 237 0 0 1 0 0 0 1 1 1 0 86 5.628 2.598
226 18 238 0 0 1 0 0 0 1 1 1 0 NA NA NA
239 19 241 1 0 1 0 0 0 0 0 0 1 81 5.721 2.649
239 19 242 NA NA NA NA NA NA NA NA NA NA 93 5.847 2.240
239 19 243 0 0 1 0 0 0 0 1 1 0 84 5.974 3.258
239 19 244 0 0 1 0 0 0 1 1 1 0 77 5.842 3.101
239 19 245 0 0 1 0 0 0 1 1 1 0 88 5.837 2.612
239 19 246 0 0 1 0 0 1 1 1 1 0 78 5.932 2.364
239 19 247 0 0 1 0 0 1 1 1 1 1 88 5.845 3.039
239 19 248 0 0 1 0 0 1 1 1 1 0 75 5.526 2.648
249 20 251 0 0 1 0 0 0 1 1 1 0 88 5.961 2.602
249 20 252 0 0 1 0 0 0 1 1 1 0 97 6.018 2.600
249 20 253 0 0 1 0 0 0 1 1 1 0 94 5.686 3.035
249 20 254 0 0 1 0 0 0 1 1 1 0 100 6.179 2.697
249 20 255 0 1 1 1 0 0 1 1 1 0 97 5.873 2.478
249 20 256 0 0 1 0 0 1 1 1 1 0 99 5.702 2.578
249 20 257 0 0 1 0 0 0 1 1 1 0 104 6.001 2.697
249 20 258 0 0 1 0 0 0 1 1 1 0 96 5.627 2.959
249 20 259 0 0 0 0 0 0 1 1 0 0 98 5.920 2.820
249 20 260 0 0 1 0 0 0 1 1 1 0 NA NA NA
249 20 261 0 0 1 0 0 0 1 1 1 0 NA NA NA
262 21 264 0 0 1 0 0 0 0 1 1 0 95 5.719 2.757
262 21 265 0 0 1 0 1 0 1 1 1 0 104 5.540 3.159
262 21 266 NA NA NA NA NA NA NA NA NA NA 115 5.858 2.812
262 21 267 0 0 1 0 0 0 1 1 1 0 89 6.034 2.915
262 21 268 0 0 1 0 0 0 1 1 1 0 116 5.984 3.408
262 21 269 0 0 1 0 0 0 0 0 1 0 100 5.886 3.085
262 21 270 0 1 1 0 0 0 1 1 1 0 85 5.615 2.613
262 21 271 0 0 1 0 0 0 1 1 1 0 120 6.127 2.747
262 21 272 0 0 1 0 0 0 1 1 1 0 112 5.469 2.809
262 21 273 0 0 1 0 0 0 1 1 1 0 107 5.599 2.459
262 21 274 0 0 1 0 0 0 1 1 1 0 88 5.686 2.589
262 21 275 0 0 1 0 0 0 1 1 1 0 NA NA NA
276 22 278 0 0 1 0 0 0 1 1 1 0 75 5.852 2.881
276 22 279 0 0 1 0 0 0 1 1 1 0 79 5.544 2.632
276 22 280 0 0 1 0 0 0 1 1 1 0 89 5.530 2.594
276 22 281 0 0 0 0 1 0 0 0 1 0 82 5.572 2.604
276 22 282 0 0 0 0 0 1 1 1 0 0 87 5.948 2.719
276 22 283 0 0 0 0 0 0 1 1 1 0 73 5.398 2.932
276 22 284 0 0 0 0 0 0 1 1 1 0 86 6.062 2.795
276 22 285 NA NA NA NA NA NA NA NA NA NA 87 6.389 3.069
276 22 286 0 0 1 0 0 0 1 1 1 0 74 5.529 2.667
276 22 287 0 0 1 0 0 0 1 1 1 1 NA 5.715 2.322
276 22 288 0 0 0 0 0 0 1 1 1 0 88 5.718 2.628
276 22 289 0 0 1 0 0 1 1 1 1 0 83 5.641 2.704
276 22 290 0 0 0 0 0 0 1 1 1 0 72 5.359 2.327
276 22 291 0 0 1 0 0 0 1 1 1 0 NA NA NA
276 23 293 0 0 0 0 0 0 1 1 0 0 80 5.658 2.574
276 23 294 0 0 0 0 0 0 1 1 1 0 89 5.770 2.875
276 23 295 0 0 0 0 0 0 1 1 0 0 96 5.743 2.734
276 23 296 0 0 1 0 0 0 1 1 0 0 96 5.902 2.617
276 23 297 0 0 0 0 0 0 1 1 1 0 100 5.942 3.248
298 24 300 0 0 0 0 0 0 0 1 1 0 84 5.935 3.061
298 24 301 0 0 0 0 0 0 1 1 1 0 109 5.567 3.084
298 24 302 1 0 1 0 0 0 1 1 1 0 88 6.133 2.965
298 24 303 NA NA NA NA NA NA NA NA NA NA 105 5.539 2.799
298 24 304 1 0 1 0 0 1 1 1 1 0 94 5.585 2.546
298 24 305 0 0 1 0 0 0 1 1 1 0 100 5.794 2.828
298 24 306 0 0 1 0 0 0 1 1 1 0 99 5.678 2.703
298 24 307 0 0 1 0 0 1 1 1 1 0 103 5.547 2.333
298 24 308 1 0 1 0 0 1 1 1 1 0 90 5.240 2.752
298 24 309 0 0 1 0 0 1 0 0 1 0 83 5.703 2.732
298 24 310 0 0 1 0 0 1 1 1 1 0 86 5.863 2.404
298 24 311 0 0 1 0 0 0 1 1 1 0 103 5.735 2.628
298 24 312 0 0 0 0 0 0 1 1 1 0 99 5.474 2.695
298 24 313 0 0 1 0 0 0 1 1 1 0 83 5.661 2.858
298 24 314 0 0 1 0 0 0 1 1 1 0 96 5.799 3.250
298 24 315 0 1 0 0 0 1 0 0 0 0 89 5.674 2.264
298 24 316 1 0 1 0 0 0 1 1 1 0 101 5.884 2.819
298 24 317 0 0 1 0 0 0 1 1 1 0 95 5.408 2.459
298 24 318 0 0 1 0 0 1 0 1 0 0 85 5.523 2.371
298 24 319 0 0 1 0 0 1 1 1 1 0 93 5.625 2.709
298 24 320 0 0 1 0 0 1 1 1 1 0 89 5.791 2.578
298 24 321 0 0 1 0 0 1 1 1 1 0 86 5.730 2.410
298 24 322 0 0 1 0 0 0 1 1 1 0 93 5.520 2.184
298 24 323 1 0 1 0 0 1 1 1 1 0 90 5.402 2.246
298 24 324 NA NA NA NA NA NA NA NA NA NA NA 5.930 3.012
298 24 325 0 0 1 0 0 1 1 1 1 0 100 5.405 2.463
298 24 326 0 0 1 0 0 1 1 1 1 0 81 5.501 2.353
298 24 327 0 0 1 0 0 0 1 1 1 0 94 5.452 2.576
328 25 330 0 0 1 0 0 0 1 1 1 0 107 5.895 2.539
328 25 331 0 0 1 0 0 1 1 0 1 0 107 5.962 3.701
328 25 332 NA NA NA NA NA NA NA NA NA NA 92 6.074 2.427
328 25 333 0 0 1 0 0 0 1 1 1 0 85 5.398 2.664
328 25 334 0 0 1 0 0 0 1 0 1 0 94 6.103 2.333
328 25 335 0 0 1 0 0 0 1 1 1 0 100 5.962 2.487
328 25 336 1 0 1 0 0 0 1 1 1 0 116 5.974 2.438
328 25 337 0 0 1 0 0 0 1 1 1 0 92 5.892 2.333
328 25 338 0 0 1 0 0 0 1 0 1 0 113 5.998 2.742
328 25 339 1 0 1 0 0 0 1 1 1 0 104 5.849 2.620
328 25 340 0 0 1 0 0 0 1 1 1 0 NA NA NA
341 26 343 0 0 1 0 0 0 0 1 1 0 NA 5.731 2.670
29 27 344 0 1 1 0 1 0 1 1 1 0 103 5.931 2.598
29 27 345 0 0 1 0 0 0 1 1 1 0 94 NA NA
29 27 346 0 0 1 0 0 0 1 1 1 0 110 NA NA
57 28 347 0 0 1 0 0 0 1 1 1 0 115 5.933 2.800
57 28 348 0 1 0 0 0 0 0 1 1 0 105 5.663 2.584
57 28 349 NA NA NA NA NA NA NA NA NA NA 131 6.238 2.344
57 28 350 0 0 1 0 0 0 1 1 1 0 111 5.762 2.902
57 28 351 0 0 1 0 0 0 1 1 1 0 119 5.744 2.533
57 28 352 0 0 1 0 0 1 1 1 1 0 89 NA NA
60 29 353 0 0 1 0 0 1 1 1 1 0 106 5.539 2.764
60 29 354 0 0 1 0 0 0 1 1 1 0 122 5.896 2.811
60 29 355 0 0 1 0 0 1 1 1 1 0 112 5.728 2.795
60 29 356 0 0 0 0 0 0 1 0 0 1 93 5.476 2.060
60 29 357 NA NA NA NA NA NA NA NA NA NA 119 5.690 2.462
60 29 358 0 0 0 0 0 1 1 1 1 0 97 NA NA
91 30 359 0 0 1 0 0 0 1 1 1 0 85 5.609 2.183
91 30 360 NA NA NA NA NA NA NA NA NA NA 92 5.775 2.296
91 30 361 1 0 0 0 0 0 1 1 0 0 84 5.700 2.127
91 30 362 0 1 1 0 0 0 1 0 1 0 85 NA NA
91 30 363 0 0 1 0 0 0 1 1 1 0 75 5.634 2.189
91 30 364 0 0 1 0 0 0 1 1 1 0 74 5.533 2.298
91 30 365 0 0 1 0 0 0 1 1 1 0 NA NA NA
76 31 366 NA NA NA NA NA NA NA NA NA NA 92 6.169 2.336
76 31 367 0 1 0 0 0 0 1 1 1 0 91 5.992 2.289
76 31 368 NA NA NA NA NA NA NA NA NA NA 105 6.431 2.455
76 31 369 0 0 1 0 0 0 1 0 1 0 105 5.834 2.402
76 31 370 0 0 1 0 0 0 1 1 1 0 108 6.341 2.440
76 31 371 0 0 1 0 1 1 1 0 1 0 110 6.448 2.687
76 31 372 0 1 0 0 0 0 1 1 1 0 NA NA NA
37 32 373 0 0 1 0 0 0 1 1 1 0 96 5.435 2.267
37 32 374 0 0 1 0 0 0 1 1 1 0 93 5.365 2.179
37 32 375 0 0 1 0 0 0 1 1 1 0 97 5.577 2.381
37 32 376 0 0 1 0 0 0 1 1 0 0 97 5.685 2.232
37 32 377 0 0 1 0 0 0 1 1 0 0 109 5.682 2.802
37 32 378 0 0 1 0 0 0 1 1 0 0 96 NA NA
37 32 379 0 0 1 0 0 0 1 1 1 0 118 NA NA
37 32 380 0 0 1 0 0 0 1 1 1 0 90 NA NA
38 33 381 0 0 1 0 0 0 1 1 1 0 94 5.262 2.855
38 33 382 0 0 1 0 0 0 1 1 1 0 103 5.949 2.338
38 33 383 0 0 1 0 0 0 1 1 1 0 89 NA NA
38 33 384 0 0 1 0 0 0 1 1 1 0 120 NA NA
38 33 385 0 0 1 0 0 0 1 1 1 0 111 5.591 2.572
38 33 386 0 0 1 0 1 0 1 1 1 0 84 5.569 2.257
38 33 387 0 0 1 0 0 0 1 1 1 0 86 5.248 2.175
38 33 388 0 0 1 0 0 0 1 1 1 0 87 NA NA
35 34 389 0 0 1 0 0 0 1 1 1 0 78 5.465 2.095
35 34 390 0 1 1 0 0 0 1 1 1 0 73 5.499 2.043
35 34 391 0 0 1 1 0 0 1 1 1 0 87 5.740 2.268
35 34 392 0 0 1 1 0 0 1 1 1 0 79 5.948 2.360
35 34 393 0 0 1 0 0 0 1 1 1 0 101 5.549 2.478
35 34 394 0 0 0 0 0 0 1 1 1 0 86 5.713 2.889
35 34 395 0 0 1 0 0 0 1 1 1 0 NA NA NA
71 35 396 0 0 1 0 0 0 1 1 1 0 98 6.062 2.840
71 35 397 0 0 1 0 0 1 1 1 1 0 89 5.774 2.374
71 35 398 0 0 1 0 0 1 1 1 1 0 101 NA NA
71 35 399 0 0 0 0 0 0 1 1 1 0 96 6.260 3.063
71 35 400 0 0 1 0 0 0 1 1 1 0 NA NA NA
71 35 401 0 0 1 0 0 0 1 1 0 0 NA NA NA
19 36 402 0 0 0 1 0 0 1 1 0 1 73 5.134 2.147
19 36 403 0 0 1 0 0 0 1 0 1 0 85 5.170 2.148
77 37 404 0 0 1 0 0 1 1 1 1 0 87 5.965 2.463
77 37 405 1 0 1 0 0 0 1 1 1 0 84 5.383 2.257
77 37 406 0 0 1 0 0 0 1 1 1 0 74 5.314 2.860
147 38 407 0 0 1 0 0 1 1 1 1 0 96 6.187 2.467
147 38 408 0 1 1 0 0 0 1 1 1 0 82 5.773 2.828
147 38 409 0 0 1 0 0 0 1 1 1 0 81 5.731 2.661
147 38 410 0 0 1 0 0 0 1 1 1 0 94 5.684 2.731
147 38 411 0 0 1 0 0 0 1 1 1 0 73 5.829 2.568
147 38 412 0 0 1 0 0 0 1 1 1 0 NA NA NA
147 38 413 0 0 1 0 0 0 1 1 1 0 NA NA NA
147 38 414 1 0 1 0 0 0 1 1 1 0 NA NA NA
147 38 415 0 0 1 0 0 0 1 0 0 0 NA NA NA
130 39 416 0 0 1 0 0 0 1 1 1 0 90 6.066 2.436
152 40 417 1 0 1 0 0 1 1 1 1 0 71 5.819 2.533
152 40 418 1 0 1 0 0 0 1 1 1 0 78 5.373 2.177
152 40 419 0 0 1 0 0 0 1 1 1 0 115 5.724 2.513
152 40 420 0 0 1 0 0 0 1 1 1 0 97 5.703 2.317
152 40 421 1 0 1 0 0 0 1 1 1 0 NA NA NA
267 41 422 0 0 1 1 0 0 1 1 1 0 104 5.734 2.539
267 41 423 0 0 1 0 0 0 1 1 1 1 98 5.855 2.400
184 42 424 0 0 0 0 0 0 1 0 0 0 79 5.958 2.727
184 42 425 0 0 1 0 0 1 1 1 1 0 88 5.789 2.971
184 42 426 1 0 1 0 0 0 1 1 1 0 95 5.840 2.613
184 42 427 0 0 1 0 0 0 1 1 1 0 98 5.971 2.673
184 42 428 0 0 0 0 0 0 1 1 1 0 99 5.715 2.479
184 42 429 0 0 1 0 0 0 1 1 1 0 94 5.738 2.695
184 42 430 0 0 1 0 0 0 1 1 1 0 102 5.919 2.613
195 43 431 1 0 0 0 0 1 1 0 0 0 102 5.592 2.734
236 44 432 1 0 1 0 0 0 1 1 1 0 94 6.156 2.389
324 45 433 0 0 1 0 0 0 1 1 1 0 100 5.835 2.624
324 45 434 0 1 1 0 0 0 1 1 1 0 84 5.712 2.329
324 45 435 1 0 0 0 0 0 1 1 1 0 106 5.646 2.229
324 45 436 1 0 1 0 0 0 1 1 1 0 78 5.770 2.502
324 45 437 1 0 1 0 0 0 1 1 1 0 97 5.591 2.203
324 45 438 1 0 1 0 0 0 1 1 1 0 110 5.764 2.121
324 45 439 0 0 1 0 0 0 1 1 1 0 100 5.568 3.039
324 45 440 0 0 1 0 0 0 1 1 1 0 96 5.565 2.157
164 46 441 0 0 1 0 0 0 1 0 1 1 101 5.687 2.664
164 46 442 0 0 1 0 0 0 1 1 1 1 100 5.692 2.741
280 47 443 0 0 1 0 0 0 1 1 1 0 89 5.537 3.162
280 47 444 0 0 1 0 0 0 1 1 1 1 88 5.668 2.841
280 47 445 0 0 1 0 0 0 1 1 1 0 69 5.556 2.915
280 47 446 0 0 0 0 0 0 1 1 1 0 86 5.662 3.254
320 48 447 0 0 1 0 0 1 1 1 1 0 97 5.821 2.565
320 48 448 0 0 1 0 0 0 1 1 1 0 102 5.986 2.491
320 48 449 0 0 1 0 0 0 1 1 1 0 98 5.525 2.591
320 48 450 0 0 1 0 0 1 1 1 1 0 100 5.815 2.353
320 48 451 1 0 1 0 0 1 1 1 1 0 94 6.013 2.380
320 48 452 0 0 0 0 0 0 1 1 1 0 96 5.864 2.212
165 49 453 1 0 1 0 0 0 1 1 1 1 78 5.890 2.443
165 49 454 0 0 0 0 0 0 1 1 1 1 102 5.963 2.368
331 50 455 0 0 1 0 0 1 1 1 1 0 125 6.137 2.692
331 50 456 0 0 0 0 0 0 1 0 1 1 105 5.676 2.536
331 50 457 0 0 1 0 0 0 1 1 1 0 113 6.201 2.471
331 50 458 0 0 1 0 0 1 1 1 1 0 110 5.986 2.614
331 50 459 1 0 0 0 1 1 1 1 0 0 103 5.946 2.605
241 51 460 0 0 1 0 0 0 1 1 1 0 75 6.132 2.553
241 51 461 1 0 1 0 0 0 1 1 1 0 79 5.852 2.462
343 52 462 0 0 1 0 0 1 1 1 1 0 87 5.886 2.795
343 52 463 0 0 1 0 0 0 1 1 1 0 102 6.156 2.595
343 52 464 0 0 1 0 0 1 1 1 1 0 120 5.226 2.394
343 52 465 0 0 1 0 0 1 1 1 0 0 128 5.364 2.821
343 52 466 0 0 1 0 0 0 1 1 1 0 102 5.496 2.621
343 52 467 0 0 1 0 0 1 1 1 0 0 115 5.414 2.570
389 53 468 0 0 1 0 0 0 1 1 1 0 103 6.177 2.511
389 53 469 0 0 1 0 0 0 1 1 1 0 105 5.626 2.310
377 54 470 0 0 1 0 0 0 1 1 1 0 81 5.851 2.149
377 54 471 1 0 1 0 0 0 1 1 1 0 80 5.403 2.676
463 55 472 0 0 0 0 0 1 1 1 1 0 120 5.906 2.433
463 55 473 0 1 0 0 0 0 1 1 0 0 75 5.944 2.433
463 55 474 0 0 1 0 0 0 1 1 1 0 110 6.075 2.629
463 55 475 0 1 1 0 0 1 1 1 1 0 78 5.650 2.165
463 55 476 0 0 1 0 0 1 1 1 1 0 75 5.274 2.242
463 55 477 1 0 1 0 0 0 1 1 1 0 82 5.814 2.227
463 55 478 0 1 1 0 0 0 1 1 1 0 121 5.873 2.305
463 55 479 0 0 1 0 0 1 1 1 1 0 115 5.647 2.740
463 55 480 0 0 0 0 0 0 1 1 1 0 116 5.723 2.492
463 55 481 0 0 1 0 0 0 1 1 1 0 104 5.776 2.307
463 55 482 0 0 1 0 0 1 1 1 1 0 112 5.923 2.459
463 55 483 0 0 1 0 0 1 1 1 1 0 121 5.809 2.481
375 56 484 0 0 1 0 0 0 1 1 1 0 102 5.456 2.260
375 56 485 0 0 1 0 0 0 1 1 1 0 112 5.613 2.338
462 57 486 0 0 1 0 0 0 1 1 1 0 92 5.728 2.958
462 57 487 0 0 1 0 0 0 1 1 1 0 99 5.881 2.471
462 57 488 0 0 1 0 0 0 1 1 1 0 93 5.869 2.062
462 57 489 0 0 1 0 0 0 1 1 1 0 110 NA NA
408 58 490 0 1 1 0 0 0 1 1 1 0 107 5.644 2.681
408 58 491 0 0 1 1 0 0 1 1 1 0 74 5.826 2.594
408 58 492 0 0 1 1 0 0 1 1 1 0 110 5.901 2.642
408 58 493 0 0 1 0 0 0 1 1 1 0 95 5.999 2.512
408 58 494 0 0 1 1 0 0 1 1 1 0 92 5.396 2.269
392 59 495 0 1 1 0 0 0 1 1 1 0 93 6.029 2.284
392 59 496 0 0 1 0 0 0 1 1 1 0 87 5.591 2.202
392 59 497 0 0 0 0 0 0 1 1 0 0 84 5.777 2.200
392 59 498 0 0 1 0 0 0 1 1 1 0 80 6.262 2.095
392 59 499 0 0 1 1 0 0 1 1 1 0 86 5.966 2.079
392 59 500 0 0 1 0 0 0 1 1 1 0 NA 6.083 2.597
392 59 501 0 0 1 0 0 0 1 1 1 0 88 5.528 2.582
392 59 502 0 0 1 0 0 0 1 1 1 0 92 5.703 2.277
392 59 503 0 0 1 0 0 0 0 0 1 0 101 6.116 2.369
376 60 504 0 0 1 0 0 0 1 1 0 0 109 5.724 2.859
376 60 505 0 1 1 0 0 0 1 1 1 0 84 5.555 2.244
376 60 506 0 0 1 0 0 0 1 1 0 0 100 5.457 2.192
376 60 507 0 0 1 0 0 0 1 1 0 0 117 5.603 2.586
376 60 508 0 0 1 0 0 0 1 1 1 0 116 5.917 2.364
376 60 509 0 0 1 0 0 0 1 1 1 0 112 6.040 2.328
376 60 510 0 0 1 0 0 1 1 1 1 0 92 5.608 2.443
376 60 511 0 0 1 0 0 0 1 1 0 0 102 5.628 2.208
376 60 512 0 0 1 0 0 0 1 1 0 0 116 5.690 2.249
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