[R-sig-ME] c++ exception (unknown reason) when using an offset of the slope with glmer

Ben Bolker bbolker at gmail.com
Wed Jan 28 15:45:27 CET 2015


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  Confirmed on

R Under development (unstable) (2015-01-26 r67627)
Platform: i686-pc-linux-gnu (32-bit)
lme4 1.1.8

 I will see what I can figure out.  I suspect the main problem is that
the doses range from 4 to 6, so with an offset of (1.5*dose), that
says that the logit-probability or log-odds should range from 6 to 9,
which corresponds to a baseline probability of 0.997 to 0.999.  Those
are very high probabilities: they're going to make it very hard to
make a sensible model.   Can you say a little bit more about what
you're trying to do/why an offset of 1.5 makes sense?

  Ben Bolker



On 15-01-27 08:24 AM, xavier.paoletti at curie.fr wrote:
> Thanks for your attention.
> 
> Here there are. 180 rows, 4 columns + obs number. Obs    patid
> cycle dose DLT 1       1     1  4.1   0 2       2     1  4.8   0 3
> 3     1  5.3   0 4       4     1  5.7   0 5       5     1  6.0   1 
> 6       6     1  5.7   1 7       7     1  5.7   0 8       8     1
> 5.3   0 9       9     1  5.3   0 10     10     1  5.7   0 11     11
> 1  5.3   0 12     12     1  5.3   0 13     13     1  5.3   0 14
> 14     1  5.3   0 15     15     1  5.7   0 16     16     1  5.3
> 0 17     17     1  5.3   0 18     18     1  5.3   0 19     19     1
> 5.3   0 20     20     1  5.7   0 21     21     1  5.7   1 22     22
> 1  5.7   1 23     23     1  5.7   0 24     24     1  5.3   0 25
> 25     1  5.3   0 26     26     1  5.3   0 27     27     1  5.3
> 1 28     28     1  5.7   0 29     29     1  5.7   1 30     30     1
> 5.7   0 31      1     2  4.1   0 32      2     2  4.8   0 33      3
> 2  5.3   0 34      4     2  5.7   0 35      5     2  6.0   0 36
> 6     2  5.7   0 37      7     2  5.7   1 38      8     2  5.3   0 
> 39      9     2  5.3   0 40     10     2  5.7   0 41     11     2
> 5.3   0 42     12     2  5.3   0 43     13     2  5.3   0 44     14
> 2  5.3   0 45     15     2  5.7   0 46     16     2  5.3   0 47
> 17     2  5.3   0 48     18     2  5.3   0 49     19     2  5.3
> 0 50     20     2  5.7   0 51     21     2  5.7   0 52     22     2
> 5.7   0 53     23     2  5.7   1 54     24     2  5.3   0 55     25
> 2  5.3   0 56     26     2  5.3   0 57     27     2  5.3   0 58
> 28     2  5.7   0 59     29     2  5.7   0 60     30     2  5.7
> 1 61      1     3  4.1   0 62      2     3  4.8   0 63      3     3
> 5.3   0 64      4     3  5.7   0 65      5     3  6.0   1 66      6
> 3  5.7   1 67      7     3  5.7   1 68      8     3  5.3   0 69
> 9     3  5.3   0 70     10     3  5.7   0 71     11     3  5.3   0 
> 72     12     3  5.3   0 73     13     3  5.3   0 74     14     3
> 5.3   0 75     15     3  5.7   0 76     16     3  5.3   0 77     17
> 3  5.3   0 78     18     3  5.3   0 79     19     3  5.3   1 80
> 20     3  5.7   0 81     21     3  5.7   0 82     22     3  5.7
> 0 83     23     3  5.7   1 84     24     3  5.3   0 85     25     3
> 5.3   0 86     26     3  5.3   1 87     27     3  5.3   0 88     28
> 3  5.7   0 89     29     3  5.7   0 90     30     3  5.7   1 91
> 1     4  4.1   0 92      2     4  4.8   0 93      3     4  5.3   0 
> 94      4     4  5.7   0 95      5     4  6.0   0 96      6     4
> 5.7   0 97      7     4  5.7   0 98      8     4  5.3   0 99      9
> 4  5.3   0 100    10     4  5.7   0 101    11     4  5.3   1 102
> 12     4  5.3   1 103    13     4  5.3   0 104    14     4  5.3
> 0 105    15     4  5.7   1 106    16     4  5.3   0 107    17     4
> 5.3   0 108    18     4  5.3   0 109    19     4  5.3   0 110    20
> 4  5.7   0 111    21     4  5.7   0 112    22     4  5.7   0 113
> 23     4  5.7   0 114    24     4  5.3   0 115    25     4  5.3
> 0 116    26     4  5.3   0 117    27     4  5.3   0 118    28     4
> 5.7   0 119    29     4  5.7   0 120    30     4  5.7   0 121     1
> 5  4.1   0 122     2     5  4.8   0 123     3     5  5.3   0 124
> 4     5  5.7   0 125     5     5  6.0   0 126     6     5  5.7   1 
> 127     7     5  5.7   0 128     8     5  5.3   1 129     9     5
> 5.3   0 130    10     5  5.7   0 131    11     5  5.3   0 132    12
> 5  5.3   0 133    13     5  5.3   1 134    14     5  5.3   0 135
> 15     5  5.7   0 136    16     5  5.3   0 137    17     5  5.3
> 0 138    18     5  5.3   0 139    19     5  5.3   0 140    20     5
> 5.7   0 141    21     5  5.7   1 142    22     5  5.7   0 143    23
> 5  5.7   0 144    24     5  5.3   0 145    25     5  5.3   0 146
> 26     5  5.3   0 147    27     5  5.3   0 148    28     5  5.7
> 0 149    29     5  5.7   0 150    30     5  5.7   0 151     1     6
> 4.1   0 152     2     6  4.8   0 153     3     6  5.3   0 154     4
> 6  5.7   1 155     5     6  6.0   1 156     6     6  5.7   0 157
> 7     6  5.7   0 158     8     6  5.3   0 159     9     6  5.3   0 
> 160    10     6  5.7   1 161    11     6  5.3   0 162    12     6
> 5.3   0 163    13     6  5.3   0 164    14     6  5.3   0 165    15
> 6  5.7   0 166    16     6  5.3   0 167    17     6  5.3   0 168
> 18     6  5.3   0 169    19     6  5.3   0 170    20     6  5.7
> 1 171    21     6  5.7   1 172    22     6  5.7   0 173    23     6
> 5.7   0 174    24     6  5.3   0 175    25     6  5.3   0 176    26
> 6  5.3   1 177    27     6  5.3   0 178    28     6  5.7   0 179
> 29     6  5.7   1 180    30     6  5.7   0
> 
> 
> 
> 
> Ben Bolker <bbolker at gmail.com> 27/01/2015 14:14
> 
> A <xavier.paoletti at curie.fr> cc "r-sig-mixed-models at r-project.org"
> <r-sig-mixed-models at r-project.org> Objet Re: [R-sig-ME] c++
> exception (unknown reason) when using an offset of the slope with
> glmer
> 
> 
> 
> 
> 
> 
> Your posted data set got removed by the mailing list machinery.
> Can you post it somewhere publicly accessible?
> 
> 
> On Tue, Jan 27, 2015 at 3:45 AM,  <xavier.paoletti at curie.fr>
> wrote:
>> 
>> Dear all,
>> 
>> I am new on this forum and I hope my request follows the right
>> format.
>> 
>> I use R 3.1.2 and lme1.7 on a mac OS X (snow leopard) or Windows
>> OS.
>> 
>> I try to fit a longitudinal logistic mixed effect model on
>> dose-time response data where the response is measured several
>> times at the same dose. The probability of response increases
>> with the dose. There is a set of discrete doses (let's say 6) but
>> most of the data are measured at 1 or 2 doses.
>> 
>> I use a very simple logstic model with a random intercept and the
>> dose effect. In the simplest case, there is not time effect. 
>> Furthermore, I would like to set the slope of the dose to some
>> value
> using
>> offset slope=1.5.
>> 
>> The command line, glmer(DLTb ~ offset(slope*dose) + (1 | patid), 
>> family=binomial,data=dataAllCRM,nAGQ=10) gives the following
>> error: Under Mac OSX: (function (fr, X, reTrms, family, nAGQ =
>> 1L, verbose =
> 0L,
>> control = glmerControl(),  : c++ exception (unknown reason)
>> 
>> Under Windows: Error: (maxstephalfit) PIRLS step-halvings failed
>> to
> reduce
>> deviance in pwrssUpdate
>> 
>> Whatever the value of the offset, I get the same error. If I
>> remove the offset or if I remove the variable, some estimates
>> are obtained.
>> 
>> Please find attached an example of dataset as an illustration; I
>> get the same error for all tested datasets.
>> 
>> 
>> Thank you very much for your help.
>> 
>> Best regards,
>> 
>> Xavier _______________________________________________ 
>> R-sig-mixed-models at r-project.org mailing list 
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> 
> 

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