Dear Laura, this model is most certainly overfit. for a binomial model the rule of thumb for the absolute upper limit for the number of paramters in the model is min(outcome A, outcome B) / 10, which in your case is 12/ 10 ~ 1. you're using 2 parameters for fixed effects (and 1 for a random intercept).
Most of your subjects will have 0 deaccented syllables, making it hard to impossible for the model to estimate the effects of info. I am still a bit puzzled by the output though. did the fitting process not through a warning (I am leaving town tomorrow, so i did not have time to load your data and see for myself). Florian On Mon, Dec 22, 2008 at 4:57 AM, Laura de Ruiter <laura.derui...@mpi.nl>wrote: > Dear R-users and -experts, > > I am performing a rather simple analysis on a small data set (pasted below > this email) and keep getting a to me inexplicable result. Perhaps I am > missing something here - it would be great if someone could point out to me > what I am doing wrong. > > I want to test whether the factor "Info" (which has three levels: "new", > "given", "accessible") is a significant predictor for the binary variable > "DeaccYN". The random factor is "Subject". The distribution of the data > looks as follows: > > > ----------------------------------------------------------------------------- > xtabs (~ DeaccYN + Info, data = dat) > Info > DeaccYN given new accessible > no 25 42 21 > yes 11 0 1 > > ------------------------------------------------------------------------------ > > This is the model: > > > ---------------------------------------------------------------------------------------------------------- > deacc.lmer = lmer (DeaccYN ~ Info + (1|Subject), data = dat, family = > "binomial") > > ----------------------------------------------------------------------------------------------------------------- > > However, given the distribution above, this outcome seems rather weird to > me: > > > --------------------------------------------------------------------------------------------------------- > summary (deacc.lmer) > Generalized linear mixed model fit using Laplace > Formula: DeaccYN ~ Info + (1 | Subject) > Data: dat > Family: binomial(logit link) > AIC BIC logLik deviance > 60.4 70.82 -26.2 52.4 > Random effects: > Groups Name Variance Std.Dev. > Subject (Intercept) 0.18797 0.43356 > number of obs: 100, groups: Subject, 21 > > Estimated scale (compare to 1 ) 0.7316067 > > Fixed effects: > Estimate Std. Error z value Pr(>|z|) > (Intercept) -0.8635 0.3795 -2.2754 0.0229 * > Infonew -18.7451 2764.2445 -0.0068 0.9946 > Infoaccessible -2.2496 1.1186 -2.0110 0.0443 * > --- > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > Correlation of Fixed Effects: > (Intr) Infonw > Infonew 0.000 > Infoaccssbl -0.315 0.000 > > ---------------------------------------------------------------------------------------------------- > > Why should the difference between 25/11 and 21/1 be significant, but the > difference between 25/11 and 42/0 not? Very odd to me seems the standard > error of 2764! > First I was wondering whether the problem had sth. to do with the fact that > I had read in an SPSS file, but I keep getting this result even when I first > save the SPSS file as a tabdelim. text and read it into R then. I couldn't > find anything strange going on with the Subject factor either (and I have > also tried the model with "Item" as random factor, which didn't change > anything). > > I was wondering: Is it perhaps a problem for the model that there are no > cases in the DeaccYN == "yes" category for Info == "given"? And if this is > the case, why? > Am I overlooking something here? > > Any help with this would be very much appreciated! > > Thanks a lot in advance for your answers & happy holidays, > > Laura de Ruiter > > --------------------------------------- > The data set: > > dat > Subject Info Deacc > 1 93 new no > 2 93 new no > 3 93 given yes > 4 93 new no > 5 93 given no > 6 94 new no > 7 94 new no > 8 94 given no > 9 94 new no > 10 94 given no > 11 94 given no > 12 94 accessible no > 13 95 given no > 14 95 new no > 15 95 accessible no > 16 95 new no > 17 95 accessible no > 18 95 new no > 19 97 new no > 20 98 new no > 21 98 new no > 22 98 given yes > 23 98 given no > 24 101 new no > 25 101 new no > 26 101 given yes > 27 101 accessible no > 28 101 new no > 29 101 given yes > 30 101 given no > 31 101 accessible no > 32 102 new no > 33 102 new no > 34 102 given no > 35 102 given no > 36 102 accessible no > 37 104 new no > 38 104 accessible no > 39 104 new no > 40 104 given yes > 41 104 new no > 42 104 given no > 43 104 accessible no > 44 107 new no > 45 107 given no > 46 107 accessible no > 47 107 new no > 48 109 new no > 49 109 given yes > 50 109 new no > 51 110 accessible no > 52 110 new no > 53 110 new no > 54 110 given no > 55 113 new no > 56 113 given yes > 57 113 new no > 58 113 given yes > 59 114 new no > 60 114 accessible no > 61 114 new no > 62 114 given no > 63 114 accessible no > 64 114 new no > 65 114 given yes > 66 114 accessible yes > 67 115 accessible no > 68 115 given no > 69 115 given no > 70 116 accessible no > 71 116 new no > 72 116 given no > 73 116 new no > 74 116 new no > 75 116 accessible no > 76 117 accessible no > 77 117 given no > 78 117 new no > 79 117 new no > 80 117 given no > 81 117 accessible no > 82 118 new no > 83 118 accessible no > 84 118 given no > 85 118 new no > 86 118 given no > 87 118 accessible no > 88 119 given no > 89 119 given yes > 90 119 given no > 91 119 accessible no > 92 121 given no > 93 121 given no > 94 131 new no > 95 131 new no > 96 131 new no > 97 131 new no > 98 132 given yes > 99 132 given no > 100 132 accessible no > ------------------------------------------------ > > -- > ////////////////////////////////////////////////////// > > Laura de Ruiter > PhD student > Language Acquisition Group > Max Planck Institute for Psycholinguistics > P.O. Box 310 > 6500 AH Nijmegen > Netherlands > > Phone: 0031-24-3521 456 > Homepage: http://www.mpi.nl/Members/LauradeRuiter > > /////////////////////////////////////////////////// > > > _______________________________________________ > R-lang mailing list > R-lang@ling.ucsd.edu > http://pidgin.ucsd.edu/mailman/listinfo/r-lang >
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