search for: coscol

...************************************ T <- xx$timestamp[end] - xx$timestamp[start] nsamples <- end +1 - start nfr <- ceiling(nsamples/2) yy <- xx[start:end,"amplitude"] tt <- xx[start:end,"timestamp"] cosmat <- matrix(nrow=nsamples,ncol=nfr) coscol <- NULL sinmat <- matrix(nrow=nsamples,ncol=nfr) sincol <- NULL for(i in 1:nfr){ cosmat[,i] <- cos(tt*2*pi*i/T) coscol <- c(coscol,paste("cos",i,sep="")) sinmat[,i] <- sin(tt*2*pi*i/T) sincol <- c(sincol,paste("sin",i,s...

Hi all!! I am trying to plot several confidence intervals in a unique plot. That is, for each x, I have a confidence interval for a parameter related to x and I would like to plot them in the same plot, in order to compare them. The plot would look like some parallel vertical lines, each one corresponding to a x value. Their extrem points would be the confidence interval limits. I do not know if

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent

Hi all!! I am using a proportinal odds model to study some ordered categorical data. I am trying to predict one ordered categorical variable taking into account only another categorical variable. I am using polr from the R MASS library. It seems to work ok, but I'm still getting familiar and I don't know how to assess goodness of fit. I have this output, when using response ~ independent