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Dear R users this may be a simple question - but i would appreciate any thoughts does anyone know how you would get one lower and one upper confidence interval for a set of data that consists of proportions. i.e. taking a usual confidence interval for normal data would result in the lower confidence interval being negative - which is not possible given the data (which is constrained between

...[1,]) [1] "numeric" Why not matrix? What am I missing here? Is there a way to keep the same class? The reason for the question is that I want to implement a k-step ahead prediction for my own routines and R wrecks does not seem to like [1,] as shown below. > predict(fit10,testX[1,]) Error in knnregTrain(train = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, : dims of 'test' and 'train differ > predict(fit10,testX[1:2,]) [1] 81.00 76.36 Many thanks Ed [[alternative HTML version deleted]]

...lysis with both random intercept and slope, or only random slope? zz<-groupedData(y~time | Subject,data=data.frame(data), labels = list( x = "Time", y = "y" ), units = list( x = "(yr)", y = "(mm)") ) plot(zz) fit10<-lme(zz) summary(fit10) Linear mixed-effects model fit by REML Data: zz AIC BIC logLik -123.1942 -115.2010 67.5971 Random effects: Formula: ~time | Subject Structure: General positive-definite StdDev Corr (Intercept) 6.054897e+00 (Intr) time 4.16...

...of smoothing (e.g. number of knots for the spline). This appears to be the case even for the example on the vgam help page. data(hunua) fit2 = vgam(agaaus ~ s(altitude, df=2), binomialff, hunua) > coef(fit2) (Intercept) s(altitude, df = 2) -1.1661259280 0.0003932463 fit10 = vgam(agaaus ~ s(altitude, df=10), binomialff, hunua) > coef(fit10) (Intercept) s(altitude, df = 10) -1.1661259280 0.0003932463 2. There may be a bug when trying to run gam (mgcv) and vgam(VGAM) together. When I run vgam first, I am unable to get smoothed paramete...

Hi, I am looking at the change in N concentration in plant roots over 4 time points and I have fit a spline to the data using ns and lme: fit10 <- lme( N~ns(day, 3), data = rcn10G) I may want to adjust the model a little bit, but for now, let's assume it's good. I get output for the fixed effects: Fixed: N ~ ns(day, 3) (Intercept) ns(day, 3)1 ns(day, 3)2 ns(day, 3)3 1.15676524 0.14509171 0.04459627 0.09334428 and...

Hi, I am looking at the change in N concentration in plant roots over 4 time points and I have fit a spline to the data using ns and lme: fit10 <- lme( N~ns(day, 3), data = rcn10G) I may want to adjust the model a little bit, but for now, let's assume it's good. I get output for the fixed effects: Fixed: N ~ ns(day, 3) (Intercept) ns(day, 3)1 ns(day, 3)2 ns(day, 3)3 1.15676524 0.14509171 0.04459627 0.09334428 and coe...