search for: obsevations

...can be used to estimate 95% confidence interval for some statistics, e.g. variance, median, etc. I have someone suggesting that by resampling certain proportion of the total samples (e.g. 80%) without replacement, we can also get the estimate of confidence intervals. Here we have an example of 1000 obsevations, we would like to estimate 95% confidence intervals for odds ratio for a diagnostic test, can I use resampling 80% of the observations without replacement, instead of bootstrap, to do this? If not, why is it wrong to do it this way? Thanks

Hello, I noticed something odd when working with data frames and xts objects. If I read in a CSV file, R creates a nice data.frame. This works well. If I then convert to an XTS object, I see that all the values in the data are now quoted. My data is a mix of numeric and character. This is usually seen when converting a data.frame to a matrix, as R will treat all the data as the same class.

Dear R-helper, I want to plot the following-like data: x y 1 1 1 1 1 2 1 3 1 3 1 4 ...... In the plot that produced, I don't want to show the usual circles or points. Instead, I want to show the number of replicates at that point. e.g. at the position of (1,1), there are 2 obsevations, so a number '2' will be displayed in the plot. Is my narrative clear? Is there a way to make the plot in R?

...vations are represented by their numbers (the line of the data matrix), but I would need to change it. I would like to have the possibility, to represent on a biplot, the observations, by their name, or , by the group or the cluster they belong. For instance: tab is a data.frame (4 variables x 52 obsevations) pc.tab_princomp(tab,cor=TRUE) biplot.princomp(pc.tab) km_kmeans(tab,3,100) cl_cutree(hclust(dist(tab)),k=3) # I would like to obtain: biplot.princomp(pc.tab, nomi=cl) #or biplot.princomp(pc.tab,nomi=km$cluster) Like that I could see how the clusters are spread on the PCA. So, my question...

Greetings list, I am new to programming in R, and am using nnet() function for a project on neural networking. Firstly I wish to ask if there is any pdf explaining the algorithm nnet uses, which could tell me what the objects of the nnet class, like 'conn', 'nconn, 'nsunits', n and 'nunits' mean, and how weights are calculated. The package pdf has little or no

We have been running Samba 2.2.x for some time without problems. Servers are Solaris 2.7 and RH Linux. Clients are a mix of WinNTsp4 and Win2K. Server setting is for plaintext (un-encryted passwords). On upgrading the Samba server to 3.0.0, (smb.conf file unchanged): All WinNt clients fail to authenticare. Win2k clients authenticate correctly. On reverting back to Samba 2.2.x, all cleints