similar to: Categorical variable in a custom nonlin function with gnm

Hello, I'm wondering how I can merge two featuresets into one. My dataset is two sets of microarray data and it looks like followings: > rawData $v1 TilingFeatureSet (storageMode: lockedEnvironment) assayData: 2197815 features, 59 samples element names: channel1, channel2 protocolData rowNames: LT290677RU_D1_2011-02-16 LT286300LU_D1_2010-07-24 ... LT003990RU_D1_2010-11-04 (59

Dear R-devel, I've made some potential extensions to writeForeignSAS in 'foreign' that I wanted to pass along if anyone is interested. I've attached the diff -u output against the version found in foreign_0.8-15 and an .R file with my changes. (In this .R file, the function is named writeForeignSAS7 to simplify testing/comparisons.) I've tried to alter the current

Hi everybody, I have a problem when trying to do the quality control with the packages simpleaffy and affyQCReport with the drosophila chip 2.0 At first I got the messeage, that the *.qcdef file is not there. I followed the instructions in tha manual and created the file like that: array drosophila2cdf alpha1 0.05 alpha2 0.065 spk bioB AFFX-r2-Ec-bioB-3_at spk bioC AFFX-r2-Ec-bioC-3_at spk bioD

Hi, I am having troubles with creating an eSet and would appreciate any help on the following problem. I am trying to create an eSet using the following code pd <- read.table(file="pdata.txt",header =TRUE,row.names=1); colnames(pd) <- c("type","tumor","time","id"); pdN <- list(type =

Hello Everyone, I am writing programs in R from 7 months and I am able to solve most of the errors/issues except for this current post. My Task is to read a Microsoft Excel file(textE_to_affy.csv) which contains the Microarray Expression Values collected from the Illumina Microarray experiment. These collected intensity values need to be normalized(Rank Invariant Normalization) by using the R

Dear R-helpers & bioconductor Sorry for cross-posting, this concerns R-programming stuff applied on Bioconductor context. Also sorry for this long message, I try to be complete in my request. I am trying to write a subset method for a specific class (ExpressionSet from Bioconductor) allowing selection more flexible than "[" method . The schema I am thinking for is the following:

Hi everyone, I'm trying to perform the following regressions in order to compare linear vs. sigmoidal fit of the relationship between my dependent variable (y) and one explaining parameter (x2), both including the confounding effects of a third variable (x1): quasi-pois-lin <- glm(y ~ x1 + x2, family = quasipoisson(link="identity"), data=fit) quasi-pois-sig <- gnm(y ~ x1 +

Hi, I am quite new to R and would like to do nonlinear regressions with Poisson distributed data. I would like to estimate paramters of an equation of this type: FR = [c*NO * exp(a+b*NO)] / [(c+NO)*(1+exp(a+b*NO))] a,b and c are parameters, NO are input values I found both the gnm and gnlr3 function which should be able to do this regression but I can't manage to make it work. How can I

Hi everyone! In a package I'm developing, I have created a custom function to get jackknife standard errors for the parameters of a gnm model (which is essentially the same as a glm model for this issue). I'd like to add support for bootstrap using package boot, but I couldn't find how to proceed. The problem is, my data is a table object. Thus, I don't have one individual per

Hi, I am an old hand at chemistry but a complete beginner at statistics including R computations. My question is whether you can carry out nonlinear multivariate regression analysis in R using neural networks, where the output variable can range from -Inf to + Inf., unlike discriminant analysis where the output is confined to one or zero. The library nnet seems to work only in the latter

Hi all, I'm trying to fit a nonlinear (logistic-like) regression, and I'd like to get some recommendations for which package to use. The expression I want to fit is something like: y ~ A * exp(X * Beta1) / (1 + exp(-(x + X * Beta2 - xmid)/scal)) Basically, it's a logistic function, but I want to be able to modify the saturation amplitude by a few parameters (Beta1) and shift the

I have been trying to port the extension 'nlme' ("Classes and methods to fit and display linear and nonlinear mixed effects models.")to R found on the Statlib S site. I am having some difficulty. The c code requires at least S.h and nonlin.h. R has S.h but not nonlin.h. I'm only a recent convert to R, and have not used S nor SPlus. Any help is greatly

Dear R-users I hope someone could help me on this problem. I want to create a multiple kiteChart showing the real values with a scalebar on each indicating the scale . Here are some sample data to show what I want to achieve. Y <- read.table(textConnection("Sample1 Sample2 60 20 150 50 300

Always reply to the list. I do not do private consulting. (I have cc'ed this to the list). I still think this belongs on stackexchange, not r-help. I think you need to read up on the mathematics of spline bases. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his

Hi, I'm using a Cox-Regression to estimate hazard rates on prepayments. I'm using the "pspline" function to face non-linearity, but I have no clue how to interpret the result. Unfortunately I did not find enough information on the "pspline" function wether in the survival package nor using google.. I got following output: * library(survival)* > > > >

... and is there also such a nice tool (like spss.get) for exporting data frames to SPSS? write.table does not keep the data frame labels - neither did the other exporting tools that I found. Thanks! Michael [[alternative HTML version deleted]]

Hello, I have various datasets of zoo time series (merged into single frames via the "merge" command) such as date var0 var1 var2 var3 08/07/1996 652.54 0.223922 0.515819 0.502638 08/08/1996 662.59 0.997841 0.000383 0.999806 06/09/1996 655.68 0.901685 0.569763 0.866333 08/10/1996 700.64 0.268855 0.244701 0.329285 08/11/1996 730.82 0.438407 0.501427 0.461374

Hello R-helpers, I would like to fit a non-linear function to data (Discrete X axis, over-dispersed Poisson values on the Y axis). I found the functions gnlr in the gnlm package from Jim Lindsey: this can handle nonlinear regression equations for the parameters of Poisson and negative binomial distributions, among others. I also found the function nls2 in the software package

On examining non-linearity of Cox coefficients with penalized splines - I have not been able to dig up a completely clear description of the test performed in R or S-plus. >From the Therneau and Grambsch book (2000 - page 126) I gather that the test reported for "linear" has as its null hypothesis that the spline coefficient is the same at the center of basis. Thus, in the example

Hi, recently I saw but did not pay too much attention to a question that concerned regression with positive coefficients. In Splus, thereis the nnls() function that can be used if I am not wrong, but what about R ? Now I have the same problem: doing a logistic regression under constraint that coefs are non negative. What can I do with R? is there a (weighted) nnls() counterpart available? Thanks