> glm.24.pred<-predict(glm.24,newdata=nestday, type="response", SE.fit=T)
What is SE.fit? The help says se.fit. That _may_ be a problem. However, I think the real problem is that the link function argument includes a reference to vc.apfa$days that is appropriate for fitting, not prediction. One way out might be (untested) attach(va.apfa) glm.24 <- glm(formula = Success ~ NestHtZ + MeanAge + I(MeanAge^2) + I(MeanAge^3), family = logexposure(ExposureDays = days), data = vc.apfa) detach() attach(nestday) glm.24.pred<-predict(glm.24,newdata=nestday, type="response", SE.fit=T) detach() so that 'days' refers to the appropriate dataset both when fitting and predicting. (This is bending glm() to do something it was not designed to do, so some hoop-jumping is needed.) On Thu, 20 Apr 2006, Jessi Brown wrote: > An update for all: > > Using the combined contributions from Mark and Dr. Ripley, I've been > (apparently) successfully formulating both GLM's and GLMM's (using > the MASS function glmmPQL) analyzing my nest success data. The beta > parameter estimates look reasonable and the top models resemble those > from earlier analyses using a different nest survival analysis > approach. > > However, I've now run into problems when trying to predict the daily > survival rates from fitted models. For example, for a model > considering nest height (NestHtZ) and nest age effects (MeanAge and > related terms; there is an overall cubic time trend in this model), I > tried to predict the daily survival rate for each day out of a 67 day > nest cycle (so MeanAge is a vector of 1 to 67) with mean nest height > (also a vector 67 rows in length; both comprise the matrix "nestday"). > Here's what happens: > >> summary(glm.24) > > Call: > glm(formula = Success ~ NestHtZ + MeanAge + I(MeanAge^2) + I(MeanAge^3), > family = logexposure(ExposureDays = vc.apfa$days), data = vc.apfa) > > Deviance Residuals: > Min 1Q Median 3Q Max > -3.3264 -1.2341 0.6712 0.8905 1.5569 > > Coefficients: > Estimate Std. Error z value Pr(>|z|) > (Intercept) 6.5742015 1.7767487 3.700 0.000215 *** > NestHtZ 0.6205444 0.2484583 2.498 0.012504 * > MeanAge -0.6018978 0.2983656 -2.017 0.043662 * > I(MeanAge^2) 0.0380521 0.0152053 2.503 0.012330 * > I(MeanAge^3) -0.0006349 0.0002358 -2.693 0.007091 ** > --- > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > (Dispersion parameter for binomial family taken to be 1) > > Null deviance: 174.86 on 136 degrees of freedom > Residual deviance: 233.82 on 132 degrees of freedom > AIC: 243.82 > > Number of Fisher Scoring iterations: 13 > >> glm.24.pred<-predict(glm.24,newdata=nestday, type="response", SE.fit=T) > Warning message: > longer object length > is not a multiple of shorter object length in: plogis(eta)^days > > > Can anyone tell me what I'm doing wrong? > > cheers, Jessi Brown > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
