search for: lmefit

...ve. ############### library(nlme) set.seed(1) ncows <- 5; nweeks <- 45; week <- 1:nweeks mcurve <- 25 + 0.819*week - 0.0588*week^2 + 0.000686*week^3 cow.eff <- rnorm(ncows) week <- rep(week, ncows) cow <- gl(ncows,nweeks) yield <- mcurve + cow.eff[cow] + rnorm(ncows*nweeks) lmefit <- lme(yield ~ poly(week,3), random = ~1|cow) summary(lmefit) # seems OK someweeks <- seq(5,45,by=5) new <- data.frame(week=someweeks) predicts <- predict(lmefit, new, level=0) print(predicts) # not even close #plot(week, yield, las=1) #lines(someweeks, predicts) ############### -- *...

Hello,   I am trying to calculate predicted values derived from one dataset into a hypothetical dataset. I tried this line of code:   graphdata$fmgpredvalues <- predict(Acs250.3.4, graphdata)   and received the following error message:   ERROR: ZXend[1], drop = FALSE] %*%lmeFit$beta   I have made sure all variable names are the same between the two datasets and all factors are appropriately labeled.   I appreciate any insight.   Thanks,   Brittany   [[alternative HTML version deleted]]

Hi, I fitted a linear model: fit <- lm(y ~ a * b + c - 1 , na.action='na.omit') Now I want to extract only the a * b effects with confidence intervals. Of course, I can just add the coefficients by hand, but I think there should an easier way. I tried with predict.lm using the 'terms' argument, but I didn't manage to do it. Any hints are appreciated, best, joerg

Hello, I ran two lme analyses and got expected results. However, I saw something suspicious regarding p-level for fixed effect. Models are the same, only experimental designs differ and, of course, subjects. I am aware that I could done nesting Subjects within Experiments, but it is expected to have much slower RT (reaction time) in the second experiment, since the task is more complex, so it