Dear Felix, I have no idea about the calculation of your accuracy, sensitivity, etc, but the sums:
dat <- read.table(file="clipboard") #read in your data as dataframe dat dat$comp <- apply(dat,1,function(x) sum(x[-c(1,22)]==as.numeric(x[22]))) Good luck Bart drflxms wrote: > > Dear R-colleagues, > > this is a question from a R-newbie medical doctor: > > I am evaluating data on inter-observer-reliability in endoscopy. 20 > medical doctors judged 42 videos filling out a multiple choice survey > for each video. The overall-data is organized in a classical way: > observations (items from the multiple choice survey) as columns, each > case (identified by the two columns "number of medical doctor" and > "number of video") in a row. In addition there is a medical doctor > number 21 who is assumed to be a gold-standard. > > As measure of inter-observer-agreement I calculated kappa according to > Fleiss and simple agreement in percent using the routines > "kappam.fleiss" and "agree" from the irr-package. Everything worked fine > so far. > > Now I'd like to calculate specificity, sensitivity and accuracy for each > item (compared to the gold-standard), as these are well-known and easy > to understand quantities for medical doctors. > > Unfortunately I haven't found a feasible way to do this in R so far. All > solutions I found, describe calculation of specificity, sensitivity and > accuracy from a contingency-table / confusion-matrix only. For me it is > very difficult to create such contingency-tables / confusion-matrices > from the raw data I have. > > So I started to do it in Excel by hand - a lot of work! When I'll keep > on doing this, I'll miss the deadline. So maybe someone can help me out: > > It would be very convenient, if there is way to calculate specificity, > sensitivity and accuracy from the very same data.frames I created for > the calculation of kappa and agreement. In these data.frames, which were > generated from the overall-data-table described above using the > "reshape" package, we have the judging medical doctor in the columns and > the videos in the rows. In the cells there are the coded answer-options > from the multiple choice survey. Please see an simple example with > answer-options 0/1 (copied from R console) below: > > video 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 > 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 > 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 > 6 6 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 > 7 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 > 9 9 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 0 1 0 > 10 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 12 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 13 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 14 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 15 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 16 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 17 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 18 18 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 > 19 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 20 20 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 21 21 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 > 22 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 23 23 0 1 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1 0 0 0 0 > 24 24 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 0 0 1 > 25 25 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 > 26 26 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 > 27 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 28 28 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 29 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 30 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 31 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 32 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 33 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 34 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 35 35 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 36 36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 37 37 0 1 1 0 1 0 0 1 0 0 0 0 1 1 1 0 1 0 0 1 1 > 38 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > 39 39 0 1 0 0 1 0 0 1 0 1 1 0 1 1 0 0 1 1 0 1 1 > 40 40 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1 0 0 1 > 41 41 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 > 42 42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > > What I did in Excel is: Creating the very same tables using > pivot-charts. Comparing columns 1-20 to column 21 (gold-standard), > summing up the count of values that are identical to 21. I repeated this > for each answer-option. From the results, one can easily calculate > specificity, sensitivity and accuracy. > > How to do this, or something similar leading to the same results in R? > I'd appreciate any kind of help very much! > > Greetings from Munich, > Felix > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- View this message in context: http://www.nabble.com/convenient-way-to-calculate-specificity%2C-sensitivity-and-accuracy-from-raw-data-tp19251644p19252232.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
