similar to: Rank samples by breaks in hist and assign result as factor

Hello, For insect mortality data I'm trying to get an R script that will take the data from the raw form and convert it to Lx (% survival) for a number of treatments. The raw data has the number of days lived for each individual for the respective treatment. Thus, for example, when R selects the data for a single treatment I end up with the following vectors: >day=seq(from=0,to=6)

I have 11 vectors representing insect survival probabilities in response to different levels of toxins at 10 concentrations lx100=c(1,1,1,.8,.5,.4,.2,0) day100=c(0,1,2,3,4,5,6,7,8) lx90=c(1,1,1,1,.9,.8,.6,.4,.2,.1,0) day90=c(0,1,2,3,4,5,6,7,8,9,10) #...and so on10% and a zero (control) series lx0=c(1,1,1,1,1,1,.9,.9,.8,.8,.6,.5,.4,.3,.2,.1,.1,0)

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 I'm getting a weird result when I try to switch from a normal box plot to a notched one. The ends of the box fold down toward the median giving a horned appearance. Is just the sample itself? It is small, but the un-notched plot looks okay. Anyway to fix this? e7=as.vector(c(234,37,98,116,47)) boxplot(e7, plot=TRUE, notch=TRUE) Thanks very much.

Hi, I'm looking for a way to get white boxplots on a black background. The following is insufficient because although the box is white, I can't figure out how to change the whisker color to white. x <- rnorm(100) par(bg = "black") boxplot(x) boxplot(x, col = "white", notch=T) Is there no way to specify inverse colors and then not change background etc.? I'm

Is there a grayscale equivalent to alpha levels in rgb? Example: I have the following to make red transparent circles overlap with previously plotted blue symbols. symbols(x=sites$long,y=sites$lat,circles=log(sites$prop.nem +1),add=T,inches=F,bg=rgb(red=1,green=0,blue=0, alpha=0.5),fg=rgb(red=1,green=0,blue=0, alpha=0.5)) I'm having a hard time coming up with a grayscale equivalent.

Hi, I'm using package "caret" to rank predictors using random forest model and draw predictors importance plot. I used below commands: rf.fit<-randomForest(x,y,ntree=500,importance=TRUE) ## "x" is matrix whose columns are predictors, "y" is a binary resonse vector ## Then I got the ranked predictors by ranking

Dear Sir, I am Pankaj Singhal from Jaipur, India. I am very much interested and strongly looking forward in getting involved in this project Learning-to-Rank. My previous experience in this field is good. Last semester I did a similar job of ranking the URLs of the given huge dataset based on their attribute values. The dataset consisted hundreds of thousands of URLs and each url

Congratulations to all the CentOS folding at home team members for breaking through the 200 world ranking barrier. Its taken a bit of time but we've all helped to do it. Our next target is obviously to achieve 150th in the world ranking. But, to help achieve that we're going to need more members and more machines. So, if you think it sounds interesting and you want to learn more, then

Hello, What is the best way to get ranks for a vector of values, limit the range of rank values and create equal count in each group? I call this uniform ranking...uniform count/number in each group. Here is an example using three groups: Say I have values: x = c(3, 2, -3, 1, 0, 5, 10, 30, -1, 4) names(x) = letters[1:10] > x a b c d e f g h i j 3 2 -3 1 0 5 10 30 -1 4 I

Hi, I'm wondering if anyone can point me to a function that will allow me to do a ranking that treats ties differently than rank() provides for? I'd like a method that will assign to the elements of each tie group the largest rank. An example: For the vector 'v', I'd like the method to return 'rv' v: 1 2 3 3 3 4 5 5 6 7 rv: 1 2 5 5 5 6 8 8 9 10 Thanks,

Could someone give me an idea on how to do rolling ranking, i.e. rank in the moving window of last 100 numbers in a long vector? I tried naive solution like roll.rank<-function(v, len){ r<-numeric(length(v)-len+1) for(i in len:length(v)) r[i-len+1]<-rank(v[(i-len+1):i])[len] r } However, it turns out pretty slow even on my rather able Linux box. For

Hello, I would like to work with Orange as part of GSoC 2012(and continue henceforth). Apologies for joining in a bit late- i was waiting to get a proper grasp of things before discussing it here. Currently I am a Masters students in Mathematics with my bachelors in Computer Science[integrated dual degree]. Over the last year and a half, I have worked on a few ML projects and have a couple of

Hello All: I have a vector of data, z > z [1] 0.1 0.1 0.1 0.1 0.2 0.2 0.3 0.3 0.3 0.4 0.5 0.5 0.5 0.7 0.7 0.7 0.9 0.9 1.1 [20] 1.1 1.2 1.3 1.4 The first 4 elements have values of 0.1 followed 2 elements with values 0.2. When I invoke rank(z), I expected to get (1+2+3+4)/4 = 2.5 for the first 4 elements in the ranking and (5+6)/2 = 5.5 for elements 5 and 6. But what I do

Hello all, When building a CART model (specifically classification tree) using rpart, it is sometimes interesting to know what is the importance of the various variables introduced to the model. Thus, my question is: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the

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

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 have a small matrix where the columns represents a ranking and the values are the number of times each ranking was obtained eg 1 2 3 x 1 2 0 y 0 1 2 z 2 0 1 I'd like to be able to return an average of the ranking obtained average x 1.67 y 2.67 z 1.67 Whats the nicest way to do this? I'm new to the language and looking for an elegant solution :) Thanks Ben

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

--- included message ---- Thus, my question is: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the rpart package)* ---end ---- Consider the following printout from rpart summary(rpart(time ~ age + ph.ecog + pat.karno, data=lung)) Node number 1: 228 observations,