Changing your definition of cl to clase let me replicate the problem. If you set a random seed just before running knn() the results are consistent so that indicates that the function is drawing a random number at some point.
You should probably contact the package maintainer, but your toy data set is trivially simple. You have 40 total observations, but X1 has only 3 different values and X2 has only 2 different values so there are only 6 different combinations. The distance matrix on your training set has 435 distances, but only 5 different values! As a result there are many, many tied values so the algorithm probably uses a random method of selecting which 3 to use. ------------------------------------- David L Carlson Department of Anthropology Texas A&M University College Station, TX 77840-4352 -----Original Message----- From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of itziar irigoien Sent: Thursday, November 19, 2015 7:10 AM To: r-help@r-project.org Subject: [R] knn - random result although use.all=TRUE Dear all, I have this toy example to work with k-nn classification approach. (My data, code and results are at the end of the message) Working with knn function in library class and setting the parameter use.all=TRUE, I would not expect a random answer. Nevertheless I get a different answer each time I apply it. Could anyone help me finding out what is going on? Thanks, Itziar Irigoien # Generate data n <- 40 n1 <- 16 n2 <- n-n1 cl <- rep(1:2, c(n1, n2)) set.seed(37) X1 <- sample(1:3, n, replace=TRUE, prob=rep(1/3, 3)) set.seed(36) aux1 <- sample(1:2, n1, replace=TRUE, prob=c(0.9, 0.1)) set.seed(38) aux2 <- sample(1:2, n2, replace=TRUE, prob=c(0.2, 0.8)) X2 <- c(aux1, aux2) X2 <- X2+3 X2[3] <- 5 #Select training and testing sets set.seed(36) t <- sample(1:40, 30, replace=FALSE) train <- cbind(X1[t], X2[t]) test <- cbind(X1[-t], X2[-t]) out <- knn(train, test, clase[t], k=3, l=0, use.all=TRUE, prob=TRUE) table(out, clase[-t]) sum(diag(table(out, clase[-t])))/10 # Results I obtained > out <- knn(train, test, clase[t], k=3, l=0, use.all=TRUE, prob=TRUE) > table(out, clase[-t]) out 1 2 1 1 2 2 0 7 > sum(diag(table(out, clase[-t])))/10 [1] 0.8 > out <- knn(train, test, clase[t], k=3, l=0, use.all=TRUE, prob=TRUE) > table(out, clase[-t]) out 1 2 1 1 4 2 0 5 > sum(diag(table(out, clase[-t])))/10 [1] 0.6 ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
