Yes avedups() worked fine.

I presume it must be a design matrix problem. If there's something obvious
maybe you could point it out for me? Otherwise I'll just use avedups().

> design
   (Intercept) factor(pData$population)2
1            1                         1
2            1                         1
3            1                         1
4            1                         1
5            1                         0
6            1                         1
7            1                         1
8            1                         1
9            1                         1
10           1                         0
11           1                         1
12           1                         1
13           1                         1
14           1                         1
15           1                         1
16           1                         1
17           1                         0
18           1                         0
19           1                         1
20           1                         1
21           1                         1
22           1                         1
23           1                         0
24           1                         0
25           1                         1
26           1                         1
27           1                         0
28           1                         1
29           1                         1
30           1                         1
31           1                         0
32           1                         1
33           1                         1
34           1                         1
35           1                         1
36           1                         1
37           1                         0
38           1                         1
39           1                         1
40           1                         1
41           1                         1
42           1                         0
43           1                         0
44           1                         1
45           1                         0
46           1                         1
47           1                         1
48           1                         0
49           1                         1
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$`factor(pData$population)`
[1] "contr.treatment"

> dim(mat@hx)
[1] 1077   49

corfit <- duplicateCorrelation(mat@hx, design, ndups=3)

On Mon, Sep 7, 2009 at 12:12 AM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Dear Paul,
>
> While I know what this error is, I haven't seen it arise previously in
> duplicateCorrelation() with microarray data, so it is hard to give advice.
> It is a numeric error in the inner optimization routine which may arise for
> reasons which are hard to pin down.
>
> Try removing rows from the hx object, e.g., run duplicateCorrelation() on
> the top half or bottom half of the spots.  One or the other may run if the
> problem is caused by one particular probe.
>
> Also check that your design matrix is appropriate, perhaps by trying it
> with avedups().
>
> Best wishes
> Gordon
>
>  Date: Fri, 4 Sep 2009 11:25:55 +0100
>> From: Paul Geeleher <paulgeeleher@gmail.com>
>> Subject: [BioC] Duplicate correlation error in limma
>> To: Bioconductor mailing list <bioconductor@stat.math.ethz.ch>
>> Content-Type: text/plain
>>
>> Hello folks,
>>
>> I'm getting this error when I run duplicate correlation:
>>
>> corfit <- duplicateCorrelation(mat@hx, design, ndups=3, spacing=1)
>> Error in glmgam.fit(dx, dy, start = start, tol = tol, maxit = maxit, trace
>> =
>> trace) :
>> Starting values give negative fitted values
>>
>> I saw a previous post on the mailing list where this error occurred
>> because
>> of drastically different values in the input matrix, but I don't have that
>> problem so any help would be greatly appreciated. Here's some extra info:
>>
>>
>>  GSM219865        GSM219869        GSM219870        GSM219872
>> Min.   : 2.788   Min.   : 2.445   Min.   : 3.091   Min.   : 2.567
>> 1st Qu.: 4.491   1st Qu.: 4.639   1st Qu.: 4.434   1st Qu.: 4.782
>> Median : 5.252   Median : 5.305   Median : 4.945   Median : 5.450
>> Mean   : 6.051   Mean   : 6.030   Mean   : 6.045   Mean   : 6.032
>> 3rd Qu.: 7.064   3rd Qu.: 6.817   3rd Qu.: 7.231   3rd Qu.: 6.612
>> Max.   :13.602   Max.   :14.033   Max.   :13.113   Max.   :14.167
>>  GSM219879        GSM219880        GSM219881        GSM219882
>> Min.   : 3.201   Min.   : 2.832   Min.   : 3.058   Min.   : 3.144
>> 1st Qu.: 4.512   1st Qu.: 4.530   1st Qu.: 4.490   1st Qu.: 4.531
>> Median : 5.095   Median : 5.049   Median : 5.124   Median : 5.029
>> Mean   : 6.023   Mean   : 6.028   Mean   : 6.011   Mean   : 6.052
>> 3rd Qu.: 6.886   3rd Qu.: 6.897   3rd Qu.: 7.087   3rd Qu.: 7.087
>> Max.   :13.979   Max.   :13.766   Max.   :13.600   Max.   :13.382
>>  GSM219886        GSM219887        GSM219892        GSM219894
>> Min.   : 3.236   Min.   : 3.007   Min.   : 3.048   Min.   : 2.707
>> 1st Qu.: 4.419   1st Qu.: 4.520   1st Qu.: 4.407   1st Qu.: 4.674
>> Median : 4.971   Median : 5.146   Median : 4.968   Median : 5.290
>> Mean   : 6.048   Mean   : 6.061   Mean   : 6.069   Mean   : 6.013
>> 3rd Qu.: 7.036   3rd Qu.: 7.023   3rd Qu.: 7.237   3rd Qu.: 6.589
>> Max.   :13.723   Max.   :14.208   Max.   :13.281   Max.   :14.199
>>  GSM219896        GSM219899        GSM219900        GSM219902
>> Min.   : 3.137   Min.   : 3.217   Min.   : 2.896   Min.   : 2.986
>> 1st Qu.: 4.523   1st Qu.: 4.559   1st Qu.: 4.430   1st Qu.: 4.514
>> Median : 4.960   Median : 5.163   Median : 5.074   Median : 5.090
>> Mean   : 6.046   Mean   : 6.026   Mean   : 6.035   Mean   : 6.002
>> 3rd Qu.: 7.117   3rd Qu.: 6.746   3rd Qu.: 7.074   3rd Qu.: 7.077
>> Max.   :13.824   Max.   :13.816   Max.   :13.760   Max.   :13.700
>>  GSM219903        GSM219905        GSM219907        GSM219909
>> Min.   : 3.014   Min.   : 3.179   Min.   : 3.062   Min.   : 2.756
>> 1st Qu.: 4.465   1st Qu.: 4.418   1st Qu.: 4.372   1st Qu.: 4.693
>> Median : 5.027   Median : 4.998   Median : 4.885   Median : 5.386
>> Mean   : 6.057   Mean   : 6.045   Mean   : 6.056   Mean   : 6.027
>> 3rd Qu.: 7.280   3rd Qu.: 7.160   3rd Qu.: 7.175   3rd Qu.: 6.380
>> Max.   :13.592   Max.   :13.572   Max.   :13.637   Max.   :14.457
>>  GSM219911        GSM219912        GSM219913        GSM219918
>> Min.   : 3.048   Min.   : 1.977   Min.   : 2.819   Min.   : 2.929
>> 1st Qu.: 4.431   1st Qu.: 4.730   1st Qu.: 4.440   1st Qu.: 4.614
>> Median : 4.857   Median : 5.303   Median : 4.956   Median : 5.192
>> Mean   : 6.017   Mean   : 6.013   Mean   : 6.062   Mean   : 6.041
>> 3rd Qu.: 7.233   3rd Qu.: 6.513   3rd Qu.: 7.362   3rd Qu.: 6.948
>> Max.   :13.312   Max.   :14.396   Max.   :13.212   Max.   :14.050
>>  GSM219919        GSM219920        GSM219934        GSM219937
>> Min.   : 3.227   Min.   : 3.294   Min.   : 2.737   Min.   : 2.600
>> 1st Qu.: 4.363   1st Qu.: 4.385   1st Qu.: 4.518   1st Qu.: 4.665
>> Median : 4.991   Median : 4.869   Median : 5.110   Median : 5.318
>> Mean   : 6.059   Mean   : 6.041   Mean   : 6.039   Mean   : 6.030
>> 3rd Qu.: 7.171   3rd Qu.: 7.175   3rd Qu.: 6.915   3rd Qu.: 6.814
>> Max.   :13.454   Max.   :13.221   Max.   :13.875   Max.   :14.067
>>  GSM219938        GSM219939        GSM219940        GSM219943
>> Min.   : 2.946   Min.   : 2.861   Min.   : 3.266   Min.   : 2.729
>> 1st Qu.: 4.676   1st Qu.: 4.635   1st Qu.: 4.362   1st Qu.: 4.584
>> Median : 5.354   Median : 5.226   Median : 5.048   Median : 5.123
>> Mean   : 6.047   Mean   : 6.034   Mean   : 6.046   Mean   : 6.019
>> 3rd Qu.: 6.909   3rd Qu.: 6.754   3rd Qu.: 7.249   3rd Qu.: 6.928
>> Max.   :14.083   Max.   :14.110   Max.   :13.329   Max.   :13.836
>>  GSM219944        GSM219945        GSM219948        GSM219950
>> Min.   : 2.724   Min.   : 3.149   Min.   : 2.902   Min.   : 2.900
>> 1st Qu.: 4.590   1st Qu.: 4.412   1st Qu.: 4.593   1st Qu.: 4.321
>> Median : 5.289   Median : 5.076   Median : 5.172   Median : 5.114
>> Mean   : 6.055   Mean   : 6.078   Mean   : 6.062   Mean   : 6.050
>> 3rd Qu.: 6.927   3rd Qu.: 7.097   3rd Qu.: 6.843   3rd Qu.: 7.100
>> Max.   :14.071   Max.   :13.596   Max.   :13.920   Max.   :13.692
>>  GSM219952        GSM219953        GSM219954        GSM219955
>> Min.   : 2.986   Min.   : 2.784   Min.   : 2.437   Min.   : 3.065
>> 1st Qu.: 4.581   1st Qu.: 4.613   1st Qu.: 4.710   1st Qu.: 4.410
>> Median : 5.221   Median : 5.179   Median : 5.293   Median : 5.094
>> Mean   : 6.043   Mean   : 6.042   Mean   : 6.048   Mean   : 6.050
>> 3rd Qu.: 6.843   3rd Qu.: 6.738   3rd Qu.: 6.552   3rd Qu.: 7.190
>> Max.   :14.131   Max.   :13.695   Max.   :14.046   Max.   :13.209
>>  GSM219958        GSM219965        GSM219970        GSM219975
>> Min.   : 2.868   Min.   : 3.196   Min.   : 2.765   Min.   : 3.061
>> 1st Qu.: 4.501   1st Qu.: 4.417   1st Qu.: 4.426   1st Qu.: 4.662
>> Median : 5.162   Median : 4.932   Median : 5.103   Median : 5.303
>> Mean   : 6.070   Mean   : 6.044   Mean   : 6.028   Mean   : 6.039
>> 3rd Qu.: 7.001   3rd Qu.: 7.196   3rd Qu.: 6.940   3rd Qu.: 6.500
>> Max.   :13.765   Max.   :13.310   Max.   :13.648   Max.   :14.177
>>  GSM219976        GSM219977        GSM219978        GSM219980
>> Min.   : 3.393   Min.   : 3.170   Min.   : 2.694   Min.   : 2.999
>> 1st Qu.: 4.375   1st Qu.: 4.373   1st Qu.: 4.636   1st Qu.: 4.711
>> Median : 4.894   Median : 5.021   Median : 5.185   Median : 5.316
>> Mean   : 6.030   Mean   : 6.064   Mean   : 6.035   Mean   : 6.013
>> 3rd Qu.: 7.049   3rd Qu.: 7.179   3rd Qu.: 6.589   3rd Qu.: 6.377
>> Max.   :13.042   Max.   :13.413   Max.   :13.906   Max.   :13.930
>>  GSM219981
>> Min.   : 3.035
>> 1st Qu.: 4.419
>> Median : 4.998
>> Mean   : 6.064
>> 3rd Qu.: 7.091
>> Max.   :13.186
>>
>>  dim(mat@hx)
>>>
>> [1] 1077   49
>>
>>
>>  mat@hx[1:6,]
>>>
>>        GSM219865 GSM219869 GSM219870 GSM219872 GSM219879 GSM219880
>> GSM219881
>> MIRNA174  8.881515  13.19956  12.29140  13.38759  13.13721  12.91034
>> 12.76761
>> MIRNA174  8.881515  13.19956  12.29140  13.38759  13.13721  12.91034
>> 12.76761
>> MIRNA174  8.881515  13.19956  12.29140  13.38759  13.13721  12.91034
>> 12.76761
>> MIRNA175 10.424014  13.95789  13.07468  14.16651  13.93700  13.75428
>> 13.59964
>> MIRNA175 10.424014  13.95789  13.07468  14.16651  13.93700  13.75428
>> 13.59964
>> MIRNA175 10.424014  13.95789  13.07468  14.16651  13.93700  13.75428
>> 13.59964
>>        GSM219882 GSM219886 GSM219887 GSM219892 GSM219894 GSM219896
>> GSM219899
>> MIRNA174  12.56916  12.82551  13.16155  12.43174  13.33032  12.83228
>> 12.93335
>> MIRNA174  12.56916  12.82551  13.16155  12.43174  13.33032  12.83228
>> 12.93335
>> MIRNA174  12.56916  12.82551  13.16155  12.43174  13.33032  12.83228
>> 12.93335
>> MIRNA175  13.37011  13.68271  13.96500  13.28058  14.19234  13.69705
>> 13.76962
>> MIRNA175  13.37011  13.68271  13.96500  13.28058  14.19234  13.69705
>> 13.76962
>> MIRNA175  13.37011  13.68271  13.96500  13.28058  14.19234  13.69705
>> 13.76962
>>        GSM219900 GSM219902 GSM219903 GSM219905 GSM219907 GSM219909
>> GSM219911
>> MIRNA174  12.93294  12.84814  12.78597  12.79087  12.68306  13.49380
>> 12.35723
>> MIRNA174  12.93294  12.84814  12.78597  12.79087  12.68306  13.49380
>> 12.35723
>> MIRNA174  12.93294  12.84814  12.78597  12.79087  12.68306  13.49380
>> 12.35723
>> MIRNA175  13.70059  13.64676  13.57370  13.57222  13.52558  14.36265
>> 13.23234
>> MIRNA175  13.70059  13.64676  13.57370  13.57222  13.52558  14.36265
>> 13.23234
>> MIRNA175  13.70059  13.64676  13.57370  13.57222  13.52558  14.36265
>> 13.23234
>>        GSM219912 GSM219913 GSM219918 GSM219919 GSM219920 GSM219934
>> GSM219937
>> MIRNA174  13.53402  12.27678  13.15308  12.54759  12.38596  12.78409
>> 12.35020
>> MIRNA174  13.53402  12.27678  13.15308  12.54759  12.38596  12.78409
>> 12.35020
>> MIRNA174  13.53402  12.27678  13.15308  12.54759  12.38596  12.78409
>> 12.35020
>> MIRNA175  14.39575  13.15638  13.87750  13.37227  13.13537  13.67619
>> 13.36468
>> MIRNA175  14.39575  13.15638  13.87750  13.37227  13.13537  13.67619
>> 13.36468
>> MIRNA175  14.39575  13.15638  13.87750  13.37227  13.13537  13.67619
>> 13.36468
>>        GSM219938 GSM219939 GSM219940 GSM219943 GSM219944 GSM219945
>> GSM219948
>> MIRNA174  12.69961  13.04254  12.40181  12.94058  13.15582  12.61323
>> 12.98207
>> MIRNA174  12.69961  13.04254  12.40181  12.94058  13.15582  12.61323
>> 12.98207
>> MIRNA174  12.69961  13.04254  12.40181  12.94058  13.15582  12.61323
>> 12.98207
>> MIRNA175  13.64537  13.85869  13.28368  13.73350  13.98935  13.48931
>> 13.80361
>> MIRNA175  13.64537  13.85869  13.28368  13.73350  13.98935  13.48931
>> 13.80361
>> MIRNA175  13.64537  13.85869  13.28368  13.73350  13.98935  13.48931
>> 13.80361
>>        GSM219950 GSM219952 GSM219953 GSM219954 GSM219955 GSM219958
>> GSM219965
>> MIRNA174  12.81962  13.27037  12.72672  13.10274  12.32595  12.87308
>> 12.32967
>> MIRNA174  12.81962  13.27037  12.72672  13.10274  12.32595  12.87308
>> 12.32967
>> MIRNA174  12.81962  13.27037  12.72672  13.10274  12.32595  12.87308
>> 12.32967
>> MIRNA175  13.68518  14.06946  13.63226  13.91247  13.19594  13.66014
>> 13.18479
>> MIRNA175  13.68518  14.06946  13.63226  13.91247  13.19594  13.66014
>> 13.18479
>> MIRNA175  13.68518  14.06946  13.63226  13.91247  13.19594  13.66014
>> 13.18479
>>        GSM219970 GSM219975 GSM219976 GSM219977 GSM219978 GSM219980
>> GSM219981
>> MIRNA174  12.66376  13.28257  12.04305  12.46820  13.02737  13.00563
>> 12.22126
>> MIRNA174  12.66376  13.28257  12.04305  12.46820  13.02737  13.00563
>> 12.22126
>> MIRNA174  12.66376  13.28257  12.04305  12.46820  13.02737  13.00563
>> 12.22126
>> MIRNA175  13.56579  14.06224  12.95079  13.27818  13.82855  13.78838
>> 13.02881
>> MIRNA175  13.56579  14.06224  12.95079  13.27818  13.82855  13.78838
>> 13.02881
>> MIRNA175  13.56579  14.06224  12.95079  13.27818  13.82855  13.78838
>> 13.02881
>>
>>
>>
>>  sessionInfo()
>>>
>> R version 2.9.1 (2009-06-26)
>> i486-pc-linux-gnu
>>
>> locale:
>>
>> LC_CTYPE=en_IE.UTF-8;LC_NUMERIC=C;LC_TIME=en_IE.UTF-8;LC_COLLATE=en_IE.UTF-8;LC_MONETARY=C;LC_MESSAGES=en_IE.UTF-8;LC_PAPER=en_IE.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_IE.UTF-8;LC_IDENTIFICATION=C
>>
>> attached base packages:
>> [1] stats     graphics  grDevices utils     datasets  methods   base
>>
>> other attached packages:
>> [1] statmod_1.4.0 limma_2.18.2  vsn_3.12.0    Biobase_2.4.1
>>
>> loaded via a namespace (and not attached):
>> [1] affy_1.22.0          affyio_1.12.0        grid_2.9.1
>> [4] lattice_0.17-25      preprocessCore_1.6.0
>>
>>
>> --
>> Paul Geeleher
>> School of Mathematics, Statistics and Applied Mathematics
>> National University of Ireland
>> Galway
>> Ireland
>>
>


-- 
Paul Geeleher
School of Mathematics, Statistics and Applied Mathematics
National University of Ireland
Galway
Ireland

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