I suggest that you post this on the R-sig-mixed-models list where you are more likely to find those with bothe interest and expertise in these matters.
-- Bert On Thu, Mar 17, 2011 at 7:44 AM, Franssens, Samuel <samuel.franss...@econ.kuleuven.be> wrote: > Hi, > > I have the following type of data: 86 subjects in three independent groups > (high power vs low power vs control). Each subject solves 8 reasoning > problems of two kinds: conflict problems and noconflict problems. I measure > accuracy in solving the reasoning problems. To summarize: binary response, 1 > within subject var (TYPE), 1 between subject var (POWER). > > I wanted to fit the following model: for problem i, person j: > logodds ( Y_ij ) = b_0j + b_1j TYPE_ij > with b_0j = b_00 + b_01 POWER_j + u_0j > and b_1j = b_10 + b_11 POWER_j > > I think it makes sense, but I'm not sure. > Here are the observed cell means: > conflict noconflict > control 0.6896552 0.9568966 > high 0.6935484 0.9677419 > low 0.8846154 0.9903846 > > GLMER gives me: > summary(glmer(accuracy~f_power*f_type + (1|subject), > family=binomial,data=syllogisms)) > Generalized linear mixed model fit by the Laplace approximation > Formula: accuracy ~ f_power * f_type + (1 | subject) > Data: syllogisms > AIC BIC logLik deviance > 406 437.7 -196 392 > Random effects: > Groups Name Variance Std.Dev. > subject (Intercept) 4.9968 2.2353 > Number of obs: 688, groups: subject, 86 > > Fixed effects: > Estimate Std. Error z value Pr(>|z|) > (Intercept) 1.50745 0.50507 2.985 0.00284 ** > f_powerhp 0.13083 0.70719 0.185 0.85323 > f_powerlow 2.04121 0.85308 2.393 0.01672 * > f_typenoconflict 3.28715 0.64673 5.083 3.72e-07 *** > f_powerhp:f_typenoconflict 0.21680 0.93165 0.233 0.81599 > f_powerlow:f_typenoconflict -0.01199 1.45807 -0.008 0.99344 > --- > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > Correlation of Fixed Effects: > (Intr) f_pwrh f_pwrl f_typn f_pwrh:_ > f_powerhp -0.714 > f_powerlow -0.592 0.423 > f_typncnflc -0.185 0.132 0.109 > f_pwrhp:f_t 0.128 -0.170 -0.076 -0.694 > f_pwrlw:f_t 0.082 -0.059 -0.144 -0.444 0.308 > > glmmPQL gives me: > summary(glmmPQL(fixed=accuracy~f_power*f_type, random=~1|subject, > family=binomial, data=syllogisms)) > iteration 1 > iteration 2 > iteration 3 > iteration 4 > iteration 5 > iteration 6 > Linear mixed-effects model fit by maximum likelihood > Data: syllogisms > AIC BIC logLik > NA NA NA > > Random effects: > Formula: ~1 | subject > (Intercept) Residual > StdDev: 1.817202 0.8045027 > > Variance function: > Structure: fixed weights > Formula: ~invwt > Fixed effects: accuracy ~ f_power * f_type > Value Std.Error DF t-value p-value > (Intercept) 1.1403334 0.4064642 599 2.805496 0.0052 > f_powerhp 0.0996481 0.5683296 83 0.175335 0.8612 > f_powerlow 1.5358270 0.6486150 83 2.367856 0.0202 > f_typenoconflict 3.0096016 0.4769761 599 6.309754 0.0000 > f_powerhp:f_typenoconflict 0.1856061 0.6790046 599 0.273350 0.7847 > f_powerlow:f_typenoconflict 0.0968204 1.0318659 599 0.093830 0.9253 > Correlation: > (Intr) f_pwrh f_pwrl f_typn f_pwrh:_ > f_powerhp -0.715 > f_powerlow -0.627 0.448 > f_typenoconflict -0.194 0.138 0.121 > f_powerhp:f_typenoconflict 0.136 -0.182 -0.085 -0.702 > f_powerlow:f_typenoconflict 0.089 -0.064 -0.153 -0.462 0.325 > > Standardized Within-Group Residuals: > Min Q1 Med Q3 Max > -12.43735991 0.06243699 0.22966010 0.33106978 2.23942234 > > Number of Observations: 688 > Number of Groups: 86 > > > Strange thing is that when you convert the estimates to probabilities, they > are quite far off. For control, no conflict (intercept), the estimation from > glmer is 1.5 -> 81% and for glmmPQL is 1.14 -> 75%, whereas the observed is: > 68%. > > Am I doing something wrong? > > Any help is very much appreciated. > Sam. > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Bert Gunter Genentech Nonclinical Biostatistics ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
