similar to: Constrained Optimized Binning Procedure....implementation help/idea needed.

Is there some function in R that does what Interactive Grouping node in SAS Enterprise Miner does? It makes variable binning using WOE(weight of evidence) for scoring modeling. I’ve found 3 binning function in R : rattle::binning http://finzi.psych.upenn.edu/R/library/rattle/html/binning.html ks::binning http://finzi.psych.upenn.edu/R/library/ks/html/binning.html sm ::binning

On 06-Nov-09 23:30:12, gug at fnal.gov wrote: > Full_Name: Gerald Guglielmo > Version: 2.8.1 (2008-12-22) > OS: OSX Leopard > Submission from: (NULL) (131.225.103.35) > > When I attempt to use the hist() function to bin integers the behavior > seems > very odd as the bin boundary seems inconsistent across the various > bins. For > some bins the upper boundary

Hi, Thank you for responding quickly and explaining the behavior. By adding "include.lowest=TRUE,right=FALSE" and manually including breaks that resolved the simple test case. Next I updated my more complex data set, which already had manually defined breaks, and that resolved my issues there too. I have now gone in and updated all my functions which use hist() so I

How can perform logarithmic binning in the scatterplot? I could only take the log of the variables and plot them, but I am sure that is not the way. I have a very huge data, and would want to plot those high density scatterplots and code then with different colors for the bins/density. -- View this message in context:

Hi, First, thank you to Duncan Mackay for getting me started processing dates with R. Unfortunately, I need to do a little more than I initially expected. I have 5K lines of data that look like this: ID AREA DATE 0001 Center 2010-10-15 0002 Center 2010-01-02 0003 NorthWest 2010-02-05 0004 SouthWest 2010-05-11 I would like to create a script to create crosstabs like

Full_Name: Gerald Guglielmo Version: 2.8.1 (2008-12-22) OS: OSX Leopard Submission from: (NULL) (131.225.103.35) When I attempt to use the hist() function to bin integers the behavior seems very odd as the bin boundary seems inconsistent across the various bins. For some bins the upper boundary includes the next integer value, while in others it does not. If I add 0.1 to every value, then the

Hi, I'm trying to setup some complicated binning with statistics and could use a little help. I've found the bin2 function from the ash package, but it doesn't do everything I need. My intention is to copy some of their code and then modify as needed. I have a vector of two columns: head(data) r1 r2 [1,] 0.03516559 0.03102128 [2,] 0.02162539 0.14847034

Newbie here. Many apologies in advance for using the incorrect lingo. I'm new to statistics and VERY new to R. I have a "nx2" matrix , I want to sort the values based on the average of 2 columns and put k lowest (or highest) values in bin1, second k high/low values in bin2, and so on (bins would be of the same dimensions). I should also know what the first index (or position) of

Hello I have a large series of data value -- effectivly say the point across the x-axis where a pitch crosses home plate. What I want to do is find the % of ground balls at various distances across home plate. I therefore need to 'bin' the two data sets I have - plate location for ground balls and plate location for all other outcomes. Question is how can I set up a series of bins

The standard chisq.test() and fisher.test() functions, when applied to two distributions (to determine whether the same underlying distribution applies to both) requires one to pre-bin the distributions. Is there a library function (either built-in or in a package) that acts more like the ks.test() function, in that one can simply pass the two distributions and have it do the necessary binning as

I am trying to do binning on three variables (3d binning). The bin boundaries are specified by the user separately for each variable. I used the bin2 function in the 'ash' package for 2d binning that involves only two variables but didn't any package for similar binning with three variables. Are there any packages or codes available for 3d binning?? Thank you. -- View this message in

Hi, I need to do some binning which to date I've done just writing subset commands. I'm now wondering if there are any good packages that have some good pre-designed functions for multi-variable binning using say 4 or 5 variables, sometimes binning on 3 or more levels of each variable, and then supporting some sort of reporting mechanism to tell me how many data points fell into each

Hi, I'm looking for a super-duper fast mean/sum binning implementation available in R, and before implementing z = binnedMeans(x y) in native code myself, does any one know of an existing function/package for this? I'm sure it already exists. So, given data (x,y) and B bins bx[1] < bx[2] < ... < bx[B] < bx[B+1], I'd like to calculate the binned means (or sums)

First, thanks to everyone who helped me get to grips with R in (x)emacs (I get confused easily). Special thanks to Stephen Eglen for continued support. My question is about non-linear binning, or density functions over distributions governed by a power law ... y ~ mu*x**lambda # In one of its forms # (can't find Pareto in the online help) Looking at the following

Hello, Currently I am using R for building a logistic model using numerical and nominal data as predictors. Before doing the regression, the predictors are grouped. The groups I determine manually by trying to maximize the information value (which is an indicator for the discriminatory power of the variable) under the condition that enough data are within each group (approx. 5%). Is there a

Hello everyone, I have a data set, and I need to bin my data using a bin width of say g(n). Would anyone be willing to tell me how to do this in R? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Simple-question-on-binning-data-tp2202644p2202644.html Sent from the R help mailing list archive at Nabble.com.

Hello, Suppose I have the dataset shown below. The amount of observations is too massive to get a nice geom_point and smoother on top. What I would like to do is to bin the data first. The data is indexed by Time (minutes from 1 to 120 i.e. two hours of System benchmarking). Option 1) group the data by Time i.e. minute 1, minute 2, etc and within each group create bins of N consecutive

I have a simulated matrix of dates that I generated from a probability function. Each column represents a single iteration. I would like to bin each run _separately_ by decades and dump them into a new matrix where each column is the length of all decades a single run with the number dates binned by decade. I have successfully done this for a single vector of dates, but not for a matrix:

I am trying to produce frequencies in defined intervals however I can't seem to figure out how to get R to bin my data the way I want it to. I have several thousand lengths of fish that I want to be binned as follows: Ex. Length Bin 209 200 219 210 431 430 727 720 That is, bins with any length equal to or greater than the lower

Hi all, I have a situation where I have 16 bins. I generate a random number and then want to know which bin number the random number falls in. Right now, I am using a serious of 16 if() else {} statements which get very complicated with the embedded curly braces. Is there a more efficient (i.e., easier) way to go about this? boundaries<-(0:16)/16 rand<-runif(1) Which bin number (1:16)