similar to: Normalizing data

Hi, I have processed measurements of a rough surface to a heigh-height correlation plot. What the meaning of this exactly is, is not important. Only that it is a plot that had two (almost ) linear parts when plotted on a logaritmic scale. In this plot, I want to draw the best fitting lines for these linear parts but I just can't get it done. It is easy when the scales are linear but as you

Hello, I am fitting a coxph model with factors. I am running into problems when using 'survfit'. I am unsure how R is treating the factors when I fit, say: > DATA<-data.frame(time.sec,done,f.pom=factor(f.pom),po,vo) > final<-coxph(Surv(time.sec,done)~f.pom*vo+po,data=DATA) > final.surv<-survfit((final), individual=T,conf.type="log-log")

Hey all, I''m wondering how people are using acts_as_taggable. Two things I''d like to do that aren''t supported out of the box are tag normalization and user attribution. I can''t imagine I''m the first to think about doing this. >From a schema perspective these things both seem simple, just put the normalized tag in the tags table, and have a join

To run my data in another program my data cannot exceed a kurtosis of 0.8. I'm wondering if there is a package that can determine if the kurtosis for a trait is equal to or greater than 0.8 and then determine the appropriate normalizing methods to reduce the kurtosis to less than 0.8. I would also need to have record of what normalizing procedures were done for each trait Katherine Willmore

Hi everybody, I am a beginner in the steps of pre-processing and data analysis of non-targeted metabolomic profiling experiments. Anyone knows if there exists some tool for normalizing this type of data (raw data or XCMS matrix data) in R repositories? Many thanks in advance. Best regards, Núria ------------------------------------------------------- Núria Queralt

Hi I am writing some functionality for a multivariate PDF. One problem is that evaluating the normalizing constant (NC) is massively computationally intensive [one recent example took 4 hours and bigger examples would take much much longer] and it would be good allow for this in the design of the package somehow. For example, the likelihood function doesn't need the NC but (eg) the

Hi, I have a plot whole tick values along the axis have a certain range 0 - x . I need to normalize this range without changing my data files. for e.g., if my plot has tick values at 10,20,30,40,50... i have to make this 2,4,6, etc. but without changing the plot data... I am hoping I can add something to the plot command that goes like tick values divided by a quantity. Any help is appreciated.

I am wondering if it possible to normalize the slope of a linear regression to its intercept to allow for valid between-group comparisons. Here is the scenario: I need to compare the slopes of biomass increase among NAFO divisions of Northwest Atlantic cod. However, the initial division biomass is a confounding factor that may influence the slope of the regression model. How can I normalize the

Hey all, I've been googling trying to find a tool that I can use to normalize a directory full of WAV files. I found a reference to normalize in the atrpms repo but it wants to clobber several of the base rpms. Does anyone know of a tool in CentOS 6 that can normalize a directory full of WAV files that I can install without hosing up my system? -- _ ?v? /(_)\ ^ ^ Mark LaPierre

Hi all, I tried to do normalization of affymetrix data with bioconductor on a Linux server. When I read in the cel files all seemed ok. But the next step caused an error. With Win XP all works fine. Did anyone experience similar problems? Thanks, Thomas > PI <- ReadAffy() > PI AffyBatch object size of arrays=712x712 features (14 kb) cdf=ATH1-121501 (??? affyids) number of

Dear list I have a large data frame with house prices. Prices have been converted to price per m2, to be able to compare. Prices are spread over almost 10 years, each price is tagged with the year it relates to. I would like to 'normalize' all the prices to, eg. 2010 prices. The data frame looks like this > head(dafP) Y P 1 2004 11199.60 2 2005 15310.10 3 2005 19491.50 4

 Hello everyone, Apologies in advance, as this is partially a stats question and partially an R question.  I have been using a GAM to model the activity level of bats going into and coming out from a forested edge.  I had eight microphones set up in a line transect at each of eight sites, and I am hoping to construct a model for each of 7 species.  My count data has a reverse J-shaped skew and

Hi, Can someone tell me how to normalize a data set so that the mean of the set is 0 and the variance is 1. As I understand, when you calculate the principle components of a data set through correlation as < princomp( dataset, cor=T ) > then a similar calculation is performed. I would like to know how I can perform such a calulation directly. Any help would be greatly appreciated. Many

Hi, I'm working on my dial plan and I'd like to parse all dialed numbers to convert them to the format I want. For example if someone dials 0112345678 or 0033112345678 I would like to convert it to +33112345678 and then match the number to my exten => +33XXXXXXXX statements. Is this possible with asterisk? I tried different ways (using _., s, ...) but I can't seem to get that

Hi All, I have a for loop inside the function and I cannot get UUU to give me an updated grid.dens object when I run the function (it does update when I run just the for loop). Here's a simplified version of my function: UUU=function(pop, grid.dens) { for (i in 1:10){ Food=grid.dens[pop$yloc[i],pop$xloc[i]] #use initial grid.dens values Consumed=(pop$weight[i]*0.25) Left=Food-Consumed

Dear developers, the current implementation of hist.default() calculates 'density' (and 'intensities') as dens <- counts/(n*h) where h has been calculated before as h <- diff(fuzzybreaks) which results in 'fuzzy' values for the density, see e.g. > tmp <- hist(1:10,breaks=c(-2.5,2.5,7.5,12.5),plot=FALSE) > print(tmp$density,digits=15) [1]

Hi I am a newbie to R but have tried a number of ways in R to do this and can't find a good solution. (I could do it out of R in perl or awk but would like to know how to do this in R). I have a large data frame 49 variables and 7000 observations however for simplicity I can express it in the following data frame Base, Image, LVEF, ES_Time A, 1, 4.32, 0.89 A, 2, 4.98, 0.67 A, 3, 3.7, 0.5

Hi all, I've got a density graph made with the following commands: win.graph(width=13,height=6) par ( fin=c(13,3) ,mai=c(1,1,0.5,0.5) ,mfrow=c(1,2) ,cex.axis=1.5 ,cex.lab=1.5) dens<-density(DATA1.y[2,]-mean(DATA1.y[2,]),kernel="gaussian") xlimit<-range(dens$x) ylimit<-range(dens$y) hist( DATA1.y[2,]-mean(DATA1.y[2,]) ,xlim=1.1*xlimit ,xlab=expression(q[e])

Hi list, After a lot of tweaking i have managed to create a histogram with an overlaying density plot. The histogram shows a sample of birth weights of babies and the density plot shows birth weights from a much larger reference populaton. My data is divided in 0.1 Kg bins so in the code below binweigh=0.1. The trouble with the current graph is that it is not very clear since the density plot

Hi, is it possible in one of the libraries on linear methods to constrain the coefficients to be positive? Thanks Chris -- Christoph M. Friedrich | mailto:friedrich at computer.org Gesellschaft f?r Modulfermenterbau mbH (GfM mbH) | http://www.tussy.uni-wh.de/~chris Alfred-Herrhausen Str. 44 ; D-58455 Witten, Germany