c++ exception (unknown reason) when using an offset of the slope with glmer
Thank you very much. Probabilities of event range approximately between .05 to .60 You are right, If I choose an offset of the slope=0.1, I can obtain estimates of the intercept. The offset of 1.5 came from the expected increase in the risk of event when escalating the dose from 4 to 6. If I fit the model without offset, I get the following etimates for the fixed effects intercept:: -12.7 dose : 2.3 Finally, the reason for choosing an offset is to reduce the dimensionality of the model due to the sampling matrix. I work on an extension of phase I dose escalation design in oncology, where the proportion of data that is sampled at one or 2 dose levels increases with the overall sample size. Therefore after 30, 40, 50 patients, the contribution of this dose level to the likelihood is massive. Esimating both the intercept and the slope of the dose-response relationship gets useless or even misleading. I am not sure to understand why offset of the dose = 1.5 is misleading for the intercept estimate, but I will dig in . Thanks again for your help Xavier Ben Bolker <bbolker at gmail.com> 28/01/2015 15:45 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 -----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 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
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