search for: meanag

...osureDays)$linkinv, variance=variance, dev.resids=dev.resids, aic=aic, mu.eta=logexp(ExposureDays)$mu.eta, initialize=initialize, validmu=validmu, valideta=logexp$valideta), class = "family") } Now, here's how it works in a GLM: > apfa.glm.1<-glm(Success~MeanAge+I(MeanAge^2), family=logexposure(link="logexp", ExposureDays=apfa4$Days), data=apfa4) > summary(apfa.glm.1) Call: glm(formula = Success ~ MeanAge + I(MeanAge^2), family = logexposure(link = "logexp", ExposureDays = apfa4$Days), data = apfa4) Deviance Residuals: Min...

...ch i tried using the expression function. It partly works, but it prints only the first replacement label inside the plotting region and forgets the rest...what am I missing? Thank you xyplot(resid(mod4)~factor(distance),aspect=1.0,cex=1.0,xlab="Distance",ylab="Residuals",data=meanAG, span=1, panel=function(x,y,span){ panel.grid(h=0, v=-1) panel.xyplot(x,y,cex=1.0,points="jitter") panel.loess(x,y, span) panel.axis(side="bottom",at=TRUE, labels=c(expression(Bray-Curtis^{1}),expression(Bray-Curtis^{2}),expression(Canberra),expression(Gower),expression(Hellinge...

...rors at the adjusted means for Gender using values from predict. So I attempted to get them directly from the residuals as follows. The data is at the end of the email. While there is a match for the males there is a large difference for the females indicating that what I am doing is wrong. # meanAge <- mean(dd$Age) meanAgeM <- mean(dd$Age[d$Gender=="M"]) meanAgeF <- mean(dd$Age[d$Gender=="F"]) # determine adjusted means for the males and females at meanAge using predict # set up data frame to get predicted values at meanAge evalDF <- data.frame(Age = meanAge...

I appreciate the help I've been given so far. The issue I face is that the data I'm working with has 53000 rows, so in calculating distance, finding all recids that fall within 2km and summing the population, etc. - a) takes too long and b) have no sense of progress. Below is a loop that reads each recid one at a time, calculates the distance and identifies the recids that fall within 2

I had found the author's (Wolfgang Viechtbauer) earlier meta-analytic code in R, MiMa, useful. so I have been exploring metafor using an example dataset from MiMa. metafor provides a lot more. However, MiMa provided parameter estimates, standard errors, z values, etc. for individual moderators in the meta-analysis, but I don't see how to obtain these from metafor. Have you any help

Hi Dear R-users I have a database with 18000 observations and 20 variables. I am running cox regression on five variables and trying to use survfit to plot the survival based on a specific variable without success. Lets say I have the following coxph: >library(survival) >fit <- coxph(Surv(futime, fustat) ~ age + rx, data = ovarian) >fit what I am trying to do is plot a survival