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Is there any R software package can do this ? …

I have some prior knowledge of grouping, but this may be incorrect or is not sufficient as I need larger number of groups (i.e. subgroups). For example in the following data I have 3 groups in additio …

First some background: R is good choice and have so many clustering methods in different packages. … $x y <- c(rep(1,400), rep(2,200)) res <- orclass(x, y, k = 3, l = 4, k0 = 15, a = 0.75) res # compare results table(res$predict.train$class, y) You may also be interested in HDclassif (An R …

I have a noisy data of two variables like this. x1 <- rep(seq(0,1, 0.1), each = 3000) set.seed(123) y1 <- rep (c(0.2, 0.8, 0.3, 0.9, 0.65, 0.35,0.7,0.1,0.25, 0.3, 0.95), each = 3000) set.seed(1234) …

I have two variables - X and Y and I need to make cluster maximum (and optimal) = 5. Let's ideal plot of variables is like following: I would like to make 5 clusters of this. Something like this: …

I am trying to fit a bivariate cluster model with X and Y. What I would like to do is discard (make not clustered / un-grouped) that are far from the cluster center (for example $\mu$ + 2*standard dev …

The R package mice can handle categorical data for univariate cases using logistic regression and discriminant function analysis (see the link). If you use SAS proc mi is way to go [see link]. …

The mixed.solve or kin.blup functions from R package rrBLUP can do the type of analysis you are talking. In addition to reference manual in CRAN, the official website consists of vignettes. …

sample(1:nrow(myd), round(nrow(myd)/2,0), replace = FALSE) test.id <- setdiff(1:nrow(myd), training.id) myd_train <- myd[training.id,] myd_test <- myd[test.id,] I am using lm.ridge from MASS R …

I have a situation where there are n individuals and p features (variables). I do have their cluster information. Here is an example: myd <- data.frame ( sub1 = c(1, "AB", "AB", "BB", "BB", "AA", "B …

I am trying fit different kernels to calculate similarity matrix in R. … matrix : X <- matrix(rep(0,200*1000),200,1000) set.seed(123) for (i in 1:200) { X[i,] <- ifelse(runif(1000)<0.5,-1,1) } What kernels can be used to generate a similarity matrix and how can I do this in R …

The following data are just an example for working through the issue (in R), but my real X data (xmat) are correlated with each other and correlated to different degrees with the y variable (ymat). … I provided R code, but my question is not about R but rather about the methods. Xmat includes X variables V1 to V100 while ymat includes a single y variable. …

The following function is based on the paper "Imputation of Missing Values for Unsupervised Data Using the Proximity in Random Forests" by Tsunenori Ishioka in eLmL 2013. Please follow the paper for …

Different implementation software are available for lasso. I know a lot discussed about bayesian approach vs frequentist approach in different forums. My question is very specific to lasso - What are …

Corresponding data matrix in R (dummy example for workout): miss.mat <- matrix (c(5:11, 6:10, NA,12, 7:13, 8:14, 9:12, NA, 14:15, 10:16),ncol=7, byrow = TRUE) miss.mat [,1] [,2] [,3] [,4] [,5] [, … any R program / package suggestions to do this type of analysis ? …