search for: ghq

Another sample problem: In the Windows version of R-2.5.0, data(GHQ,package='HSAUR') layout(1) GHQ_glm_1<- glm(cbind(cases,non.cases) ~ GHQ, data=GHQ, family=binomial()) summary(GHQ_glm_1) yfit_glm_1<- predict(GHQ_glm_1, type='response') layout(1) plot(probs ~ GHQ,pch=1,col=1,ylab='Probability(Case)', data=GHQ) lines(yfit_glm_1 ~ GHQ,...

...(0 and 1) Random factors: Site (4 non replicate sites), Year (3 time periods) Response variable: Seedling numbers (counts) / 0.25m2 plot According to the decision tree on page 131 in Bolker et al. (2008, in TREE; thanks, very useful paper!), most of my data sets should be analysed with Laplace or GHQ model with Wald t or F statistic (since it is non-normal, can?t be transformed to normality, has a mean < 5, has less than 3 random effects, and is overdispersed). I?m using the glmer {lme4} function, since it allows for Laplace or GHQ, as well as more than one random factor (glmmML {glmmML) an...

...(0 and 1) Random factors: Site (4 non replicate sites), Year (3 time periods) Response variable: Seedling numbers (counts) / 0.25m2 plot According to the decision tree on page 131 in Bolker et al. (2008, in TREE; thanks, very useful paper!), most of my data sets should be analysed with Laplace or GHQ model with Wald t or F statistic (since it is non-normal, can?t be transformed to normality, has a mean < 5, has less than 3 random effects, and is overdispersed). I?m using the glmer {lme4} function, since it allows for Laplace or GHQ, as well as more than one random factor (glmmML {glmmML) an...

..., TUD_RT double, TFN double, TFP double, TFP_RT double, OFP_PERC double, OFN_PERC double, OTD_PERC double, TFP_PERC double, TFN_PERC double, TTD_PERC double, SEX varchar(255), AGE double, EDUC double, EMP_STAT double, MAR_STAT varchar(255), NART_IQ double, BDI double, SANX_ERP double, TRAIT double, GHQ double, IES_A double, IES_I double, IES_TOT double, CAPS_BF double, CAPS_BI double, CAPS_B double, CAPS_CF double, CAPS_CI double, CAPS_C double, CAPS_DF double, CAPS_DI double, CAPS_D double, CAPS_F double, CAPS_I double, CAPS_TOT double) Error in sqlSave(channel, zz, addPK = TRUE, verbose = TRUE)...

I have uploaded a new version (0.30-2) of glmmML to CRAN today. This is a rather extensive upgrade, mostly internal. Adaptive Gauss-Hermite quadrature (GHQ) is now used for the evaluation of the integrals in the log likelihood function. The user can choose the number of points (default is 16), I _think_ that choosing 1 point will result in a Laplace approximation. The integrals in the score and hessian are evaluated by the QUADPACK function 'Rdqag...

I have uploaded a new version (0.30-2) of glmmML to CRAN today. This is a rather extensive upgrade, mostly internal. Adaptive Gauss-Hermite quadrature (GHQ) is now used for the evaluation of the integrals in the log likelihood function. The user can choose the number of points (default is 16), I _think_ that choosing 1 point will result in a Laplace approximation. The integrals in the score and hessian are evaluated by the QUADPACK function 'Rdqag...

...ed model framework. I've also attempted piecewise regression using this formula in lmer: m<-lmer(repro ~ OTHER FIXED EFFECTS + age*(age < 2) + age*(age >= 2 & age < 8) + age*(age >= 8) + (1|id) + (1|yr), data = reproduction, family = binomial, link = "logit", GHQ = TRUE) However, this gives the warning: Warning message: In mer_finalize(ans) : gr cannot be computed at initial par (65) which is not apparent if I use just two break points or I implement the model in glm. My question is essentially whether anyone can recommend a method for performing pi...

...thod is made the default. It should give results you can compare to the results from 'lmer' (for the models that glmmML can handle). * Binomial responses can now be represented as a two-column matrix with No. of successes and No. of failures, respectively, as in glm. * New functions: 'ghq' for calculating the constants used in the Gauss-Hermite quadrature. 'extractAIC.glmmML', which makes it possible to use functions like 'dropterm' (MASS) on glmmML fits. * There are three choices of distribution for the random effects: 'gaussian' (default), 'logisti...

...thod is made the default. It should give results you can compare to the results from 'lmer' (for the models that glmmML can handle). * Binomial responses can now be represented as a two-column matrix with No. of successes and No. of failures, respectively, as in glm. * New functions: 'ghq' for calculating the constants used in the Gauss-Hermite quadrature. 'extractAIC.glmmML', which makes it possible to use functions like 'dropterm' (MASS) on glmmML fits. * There are three choices of distribution for the random effects: 'gaussian' (default), 'logisti...