search for: kuss

...y=1)' for 'dend1[[2]]' I have not found a way to solve this. Any suggestions? Patrick hc <- hclust(dist(USArrests), "ave") (dend1 <- as.dendrogram(hc)) par(mfrow=c(2,2)) plot(dend1) plot(dend1[[1]],edgePar=list(lty=3)) plot(dend1[[2]],edgePar=list(lty=1)) -- Patrick Kuss PhD-student Institute of Botany University of Basel Sch??nbeinstr. 6 CH-4056 Basel +41 61 267 2976

....2019,0.1975,0.1490,0.1917, 0.2180,0.2064,0.1943,0.2139,0.1320) X <- data.frame(alt,H) lm.res <- summary(lm(H~alt,data=X)) lm.res p1 <- lm.res[[4]][[8]] p1 lm.res.2 <- summary(lm(H~alt+I(alt^2),data=X)) lm.res.2 str(lm.res.2) # where is p p2 <- lm.res.2[[???]][[????]] -- Patrick Kuss PhD-student Institute of Botany University of Basel Sch??nbeinstr. 6 CH-4056 Basel +41 61 267 2976

....color. Any suggestions? Thanks, Patrick x <- c(1,3,4,6,7,3,2,8,3,6,5,5) y <- c(0,5,4,2,7,6,4,5,3,7,9,2) r <- c(0.1,0.4,0.3,0.2,0.5,0.3,0.2,0.4,0.3,0.2,0.1,0.2) a <- c(0,4,2,3,5,6,8,2,9,10,11,6) symbols(x,y,circles=r,bg=heat.colors(17),xlim=c(0,10),ylim=c(0,10),inches=F) -- Patrick Kuss PhD-student Institute of Botany University of Basel Sch?nbeinstr. 6 CH-4056 Basel +41 61 267 2976

Dear R-Experts, I was wondering how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function

...nut needs an official IANA enterprise-ID. Currently Luiz is using the enterprise-ID from his employer, which is obviously not a good idea. Does anybody mind if I apply for a ent.-ID for myself and assign nut a sub-ID from my ID given by IANA? Any better ideas? Regards Reinhard -- Reinhard Kuss, office <at> edv-profi24 <dot> at

Dear all, I'm analyzing a binary outcome using glm() with a binomial distribution and a logit link, and have now reached the point where I'd like to do some model checking. Since my data are in binary form I'd like to collapse over the cross-classification of the factors before the model checking. Are there any nice and simple ways doing this? If so, how? If not, I'd be

Hi, I want to fit some psychophysical data with cumulative gaussians. There is quite a convenient toolbox for matlab called 'psignifit' (formerly known as 'psychofit'). It allows the lower bound of the sigmoid to vary slightly from zero, aswell as the upper bound to vary from one. with these two free parameters, the fitted function is less sensitive to noisy data and outliers.