similar to: individual scales in random subset of pairwise distance survey

Hello, I'm curious if anyone has encounted a version of this problem (and it's solution) involving finding a consistent set of scales for subsets of survey data. The goal is to obtain peoples' rankings of pairwise similarity of a large number of items, on a 1..5 scale for example. How similar is object A to B on a 1..5 scale ___ How similar is object A to C on a 1..5

There was a BioConductor thread today where the poster wanted to find pairwise difference between columns of a matrix. I suggested the slow solution below, hoping that someone might suggest a faster and/or more elegant solution, but no other response. I tried unsuccessfully with the apply() family. Searching the mailing list was not very fruitful either. The closest I got to was a cryptic chunk

Hello, I'm using a very large data set (n > 100,000 for 7 columns), for which I'm pretty happy dealing with pairwise-deleted correlations to populate my correlation table. E.g., a <- cor(cbind(col1, col2, col3),use="pairwise.complete.obs") ...however, I am interested in the number of cases used to compute each cell of the correlation table. I am unable to find such a

I am looking for an efficient way to solve the following problem. I have a large matrix of continuous values with a small proportion of missing values. Columns correspond to variables where each variable has two measurements, call them A and B. The matrix is such that the columns are in sequence with respect to the variables. I would like to sum up the two measurements for each variable and each

Dear WizaRds, although this might be a trivial question to the community, I was unable to find anything solving my problem in the help files on CRAN. Please help. Suppose I have 4 variables and want to use all possible combinations: 1,2 1,3 1,4 2,3 2,4 3,4 for a further kmeans partitioning. I tried permutations() of package e1071, but this is not what I need. Thank you for your help and

is there a smart way of determining the number of pairwise present data in a data matrix with missings (maybe as a by-product of some statistical function?) so far, i used several loops like: for (column1 in 1:99) { for (column2 in 2:100) { for (row in 1:500) { if (!is.na(matrix[row,column1]) & !is.na(matrix[row,column2])) { pairs[col1,col2] <- pairs[col1,col2]+1

Dear all, I am exploring the possibilities for automated classification of my data. I have successfully used KNN, but was thinking about looking at SVM (which I did nto use before). I have a pairwise distance matrix of training observations which are classified in set classes, and a distance matrix of new observations to the training ones. Is it possible to use distance matrices for SVM, and

Hi the list, I need to compute Gower distance between a specific individual and all the other individual. The function DAISY from package cluster compute all the pairwise dissimilarities of a population. If the population is N individuals, that is arround N^2 distances to compute. I need to compute the distance between a specific individual and all the other individual, that is only N

R-List, I'm trying to compute pairwise distances among pairs of observations, which each pair containing data from 2 groups. There are more than 100000 unique pairs. I have programmed a distance function that has three parameters, a vector of covariates from the ith observation in Group 1, a vector of covarites from the jth observation in Group 2, and a weighting matrix. I have used

Hello, I am trying to convert list of pairwise distances to a distance matrix for spatial analysis (kriging). For instance, I have something like this for each pair pf points, and I want to convert it to a matrix: point1 point2 distance 1 1 0 1 2 4 2 2 0 2 1 4 Please let me know if there is a

I would like to randomly shuffle a distance object, such as the one created by ade4{dist.binary} below. My first attempt, using sample(jc.dist) creates a shuffled vector, losing the lower triangular structure of the distance object. How can I Ishuffle the lower triangular part of a distance matrix without losing the structure? Thanks. --Dale x1 <- c(rep(0,4),1) x2 <- c(rep(0,2),rep(1,3))

Hey Everyone, Im fresh new in R, and Im supposed to write a code to give me a correlation between two rankings. So I have two ranking lists, which contain file names, e.g.: Ranking list 1: file1.java file3.java file2.java Ranking list 2: fiile2.java file4.java file1.java I need to see how much are these two ranking lists are alike, get a correlation between them. I dont even know where to

I want to create a MDS plot with pairwise Fst values derived from a population genetics project. My Fst values are in a tab-delimited file (lower triangle only) that I view with Excel. When I use the cmdscale command I get the message: Error in cmdscale(x) : Distances must be result of dist or a square matrix In addition: Warning messages: 1: "^" not meaningful for factors in:

Hi there, I am trying to do multiple pairwise Wilcoxon signed rank tests in a manner similar to: a <- c(runif(1000, min=1,max=50), rnorm(1000, 50), rnorm(1000, 49.9, 0.5), rgeom(1000, 0.5)) b <- c(rep("group_a", 1000), rep("group_b", 1000), rep("group_c", 1000), rep("group_d", 1000)) pairwise.wilcox.test(a, b, alternative="two.sided",

I ran into a problem with "subset" while working at home that I am not sure is a bug or not. I defined a custom function to take a data frame of tree positions, sizes, and types (containing the columns TRT, COMP, PLOT, X, Y, DBH and CON) and wanted to make a stem map of the plot using different symbols and colors to represent the different types of tree stems. I copied a bit of the

Dear all, I am looking for an equivalent of the pairwise.t.test() function using a complex survey design. Any suggestions on this or how to account for weights using the pairwise.t.test itself? Thanks, V [[alternative HTML version deleted]]

I am wondering how to customize a pairwise comparisons plot of a factorial ANOVA, without doing a lot of manual manipulation of a TukeyHSD object. The customizations I'd like are: 1. The aov used log-transformed response data, but I'd like to plot the intervals on their original, untransformed scales 2. Plot all the main and interaction effects together, rather than in a separate

Hello, I have a survey in which a number of people rated a set of items on a 1..5 scale. I believe it would be desirable to argue that the people's responses are correlated, and thus that the rating task makes sense to people. Is there a standard approach to this? With only 2 people, the correlation coefficient between their responses would be an interpretable number, (though probably

Hi! I have been working several years with R but it's my first public question. I hope I'll be clear :) . This question is related to obtaining letter-based representation of non-parametric pairwise comparisons. I have a dataframe with this structure (but with quite more rows and cols): A B C factor 1 2 2 one 2 1 2 one 2 2 3 two 2 3 2 two 1 4 2 three 9 8 1 three I have no normality,

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