similar to: rank with uniform count for each rank

Hi all, Assume that I have the following 10 data points. x=c( 46, 125 , 36 ,193, 209, 78, 66, 242 , 297 , 45) sort x and get the following y= (36 , 45 , 46, 66, 78, 125,193, 209, 242, 297) I want to group the sorted data point (y) into equal number of observation per group. In this case there will be three groups. The first two groups will have three observation and the third will

Hello all, I want to assign relative score to the predictor variables on the basis of its influence on the dependent variable. But I could not find any standard statistical approach appropriate for this purpose. Please suggest the possible approaches. Thanks in advance, Rupendra Chulyadyo Institute of Engineering, Tribhuvan University, Nepal [[alternative HTML version deleted]]

I have a problem where I need to calculate the corresponding cohort percentile ranks for each of several variables. Essentially, what I need is a function that will calculate the distribution-free percentiles from each variable's data vector, returning a corresponding vector of percentiles: e.g.: percentile.my.data<-/function/(my.data) I tried to make ecdf() perform this task but

Heeeelp Given this data x <- c(1,5,100,300,250,200,550,900,1000) > quantile(x) 0% 25% 50% 75% 100% 1 100 250 550 1000 When I run the quantile, I can only know the value of the nth percentile I want to know what's the percentile position of each items in the list Sample 1 = 100% on the list has 1 or more 5 = more than x% on the list has 5 or more 100 = more than x% on the

Hi all, I am a biostatistician and I have developed my own ranking system for clinical data. I would like to test the efficiency of it w.r.t. to other ranking systems. I would like to simulate the data and after assigning ranks to my observed scores(after neglecting dropouts), observe the type I error. If I want to do a Kruuskal Wallis type of test, what test statistic should I use to test for a

Hi R users, I have a matrix of data similar to: > y=matrix(rnorm(55),ncol=5) I would like to know to which decile each number belongs compared to the numbers in its column. Say y[1,1] is the third decile among y[1:11,1] and y[2,1] is in the second decile I would like get a matrix that would return their ranks in decile, i.e., y[1,1] -> 3 y[2,1] -> 2 Your help is much appreciated!

Dear all, I would like to aggregate a data frame (consisting of 2 columns - one for the bins, say factors, and one for the values) along bins and quantiles within the bins. I have tried aggregate(data.frame$values, list(bin = data.frame $bin,Quantile=cut2(data.frame$bin,g=10)),sum) but then the quantiles apply to the population as a whole and not the individual bins. Upon this

I have a column of 1000 datapoints from the normal distribution with mean 2 and variance 4. How can I get a histogram of these observations with 20 bins with each bin having 50 observations? -- Thanks, Jim. [[alternative HTML version deleted]]

Hi all, I am using ACS micro data (PUMS) with one of the columns as a factor for the place of birth (POBPF). I would like to create a column (POBR) containing a rank corresponding to the place of the observation in the POBPF rankings. For example, if a person is from Oregon, Oregon is the most popular Place of Birth, so each person who came from Oregon should have a 1 in their POBR.

Hello, I'm trying to bin a quantity into 2-3 bins for calculating entropy and mutual information. One of the approaches I'm exploring is the cut() function, which is what the mutualInfo function in binDist uses. When it's called in the format cut(data, breaks=n), it somehow splits the data into n distinct bins. Can anyone tell me how cut() decides where to cut? Thanks, Melissa

Hi try this function ive written it should be self explantory but let me know if you have any problems. I've only been using R for a few eeeks so apologies if its not the most efficient! rankit2<-function(rankvar,cuts,data,factor) { ranker<-rankvar ranker<-0 range<-c(1:cuts) range2<-range/cuts range3<-quantile(factor,range2) over<-length(factor) for (i in 1:over){

Hello, I have a set of data with two columns: "Target" and "Actual". A http://n4.nabble.com/file/n1584647/Sample_table.txt Sample_table.txt is attached but the data looks like this: Actual Target -0.125 0.016124906 0.135 0.120799865 ... ... ... ... I want to be able to break the data into tables based on quantiles in the "Target" column. I can see (using

I have a question concerning the Wilcoxon signed-rank test, and specifically, which R subroutine I should use for my particular dataset. There are three different commands in R (that I'm aware of) that calculate the Wilcoxon signed-rank test; wilcox.test, wilcox.exact, and wilcoxsign_test. When I run the three commands on the same dataset, I get different p-values. I'm hoping that

Hi- I'm wondering if anyone can help me with my code. I'm coming up dry when I try to get a p-value from the following code. If I make a histogram of my resampled distribution, I find the difference between by groups to be significant. I've ranked the data since I have outliers in one of my groups. mange= c(35, 60, 81, 158, 89, 130, 90, 38, 119, 137, 52, 30, 27,

Hello Everyone, I'm currently learning about quantile regressions. I've been using an optimizer to compare with the rq() command for quantile regression. When I run the code, the results show that my coefficients are consistent with rq(), but the intercept term can vary by a lot. I don't think my optimizer code is wrong and suspects it has something to do with the starting

Dear, I want to make an application to calculate quantile within a For() I tried the following without success: ej. date p_val <- matrix(sample(10, 1000, replace=TRUE), 200,5) test 1 rr <- paste("p_val$",names(p_val[1]), sep="") quant <- quantile(rr, probs = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100)/100, na.rm=FALSE, type=1) test 2 rr <-

Hi r-users, I have a matrix B and a list of 3x3 matrices (mylist). I want to calculate the quantiles in the list using each of the value of B as probabilities. The codes I wrote are: B <- matrix (runif(12, 0, 1), 3, 4) mylist <- lapply(mylist, function(x) {matrix (rnorm(9), 3, 3)}) for (i in 1:length(B)) { quant <- lapply (mylist, quantile, probs=B[i]) } But quant

Can anyone please tell me if there is a function to calculate confidence intervals for the results of the quantile function. Some of my data is normally distributed but some is also a squewed distribution or a capped normal distribution. Some of the data sets contain about 700 values whereas others are smaller with about 100-150 values, so I would like to see how the confidence intervals change

Hello, I am not only interested in finding out which genes are the most highly up- or down-regulated (which I have done using the linear models and Bayesian statistics in Limma), but I also want to know which genes are consistently highly transcribed (ie. they have a high intensity in the channel of interest eg. Cy5 or Cy3 across the set of experiments). I might have missed a straight forward

Hello all, This is in reference to "Learning to Rank" Project Idea. [I know, i made the entry a bit late, but hope you are still in interest to help out] I am looking for suggestions to help me narrowing down the choices of algorithms. I had been readily surveying on the referred algorithms for the purpose of choosing the right one. I am mentioning here some of my doubts to discuss and