similar to: problems saving and loading (PLMset) objects

dear all, I have a problem with a masked object in a package we created here. we make a package for a workflow of internal analysis of microarray data. to create the package we used: > install.packages(pkgs="affyAnalysis", repos=NULL) > R CMD INSTALL affyAnalysis Erzeuge Verzeichnisse ... Erzeuge DESCRIPTION ... Erzeuge NAMESPACE ... Erzeuge Read-and-delete-me ... Kopiere

Dear all, I am trying to apply the logistic regression to determine the limit of detection (LOD) of a molecular biology assay, the polymerase chain reaction (PCR). The aim of the procedure is to identify the value (variable "dilution") that determine a 95% probability of success, that is "positive"/"total"=0.95. The procedure I have implemented seemed to work looking

Hi everyone, I am having installation problems, but this is how it all started: I had some errors running the bioconductor package affyPLM that uses LAPACK/Blas > Pset <- fitPLM(Data) Background correcting PM Normalizing PM Fitting models /usr/local/lib/R/bin/exec/R: relocation error: /usr/local/lib/R/lib/libRlapack.so: undefined symbol: s_copy # thrown out of R .... I was using R

Hi, I upgraded to R-1.6.0 and R CMD check is behaving a bit weird. The package I am check cannot make it throught because of errors like > ##___ Examples ___: > > data(Dilution) > hist(Dilution[,1]) Error in if (log == T) { : missing value where logical needed Execution halted while the function called in the example defined in the .Rd fiel as a signature in which

I am having a similar problem understanding the data structure of the "yarn" dataset described in the "[R] data structure for plsr" posts. I have spectroscopic data I'd like to run through a PLSR and have read the tutorial series, but still do not understand the data format required for the code to process my data. My current data structure consists of a .csv file read into

Hello R-listers, I'm working in an experiment that try to determine the degree of infection of different clones of a fungus and, one of the measures we use to determine these degree is the counting of antibodies in the plasma at different dilutions, in this experiment the maximum number of dilutions was eleven. I already checked for differences on the maximum concentration of the

Hi list, Here is an example: ??? hpages at XPS15:~$ R CMD INSTALL CoreGx ??? * installing to library ?/home/hpages/R/R-4.4.r85388/site-library? ??? * installing *source* package ?CoreGx? ... ??? ** using staged installation ??? ** R ??? ** data ??? *** moving datasets to lazyload DB ??? ** inst ??? ** byte-compile and prepare package for lazy loading ??? Error : in method for

Your understanding isn't similar to mine. Mine says robust/resistant methods are for data with heavy tails, not heteroscedasticity. The common ways to approach heteroscedasticity are transformation and weighting. The first is easy and usually quite effective for dose-response data. The second is not much harder. Both can be done in R with nls(). Andy From: Quin Wills > > I am

Dear Rich, It depends how the data is generated. Although I am not an expert in ecology, I can explain it based on a biomedical example. Certain variables are generated geometrically (exponentially), e.g. MIC or Titer. MIC = Minimum Inhibitory Concentration for bacterial resistance Titer = dilution which still has an effect, e.g. serially diluting blood samples; Obviously, diluting the

Hello all I've just spent a few joyless hours trying to get EBImage to install in R. I'm running Ubuntu (Hardy Heron), fully updated (including R and Imagemagick). EBImage installation seems to work, but when I 'library(EBImage)' I get the following: - - - - Error in dyn.load(file, DLLpath = DLLpath, ...) : unable to load shared library '/home/qilin/R/i486-pc-linux-

The question is general for all functions, but here is a specific example - # For the logistic regression of the following correlated variables: C <- c(457, 1371, 4113, 12339, 37017, 111051, 333153, 999459) E <- c(0.003858377, 0.014334578, 0.014092836, 0.737950754, 0.996371828, 0.997482379, 1.005569257, 0.994382856) # The nls function: A = nls(E~(Em*C^p)/(C50^p + C^p),

hi all, Am new to R. I am having problems installing Bioconductor package in R on fedora core 4 running on AMD64 bit machine. this is the error message I get : gcc -shared -L/usr/local/lib -o affyPLM.so avg_log.o biweight.o chipbackground.o common_types.o do_PLMrlm.o do_PLMrma.o do_PLMthreestep.o idealmismatch.o LESN.o lm.o lm_threestep.o log_avg.o matrix_functions.o

>>>>> Herv? Pag?s >>>>> on Fri, 3 Nov 2023 15:10:40 -0700 writes: > Hi list, > Here is an example: > ??? hpages at XPS15:~$ R CMD INSTALL CoreGx ??? * installing > ??? hpages at XPS15:~$ R CMD INSTALL CoreGx > ??? * installing to library ?/home/hpages/R/R-4.4.r85388/site-library?

Hi I could only find some discussion on this wrt lattice graphics (which I'm not using). Apologies if I'm missing something obvious. I'd like to produce 3 rug plots under a kernel density plot for a population. The population is subdivided into 3 subpopulations, which I'd like the rug plots to highlight. Naturally, when I do 3 rug plots, they all plot over each other.

I feel the need to protest about a disturbing trend in the vibrant RoR community - name dilution. ActiveRecord is called that precisely because it is that. The name come from Martin Fowler, and it expresses a class which is a database record, only _active_ - that is with methods & behaviors (unlike a classical database record, which is completely passive.) If you look in the

I think that you misunderstood me. As far as I know, RMA does three things: background correction, quantile normalization, and summary from probes to probesets. I want the probe values after background correction and quantile normalization but before the summary. On Sat, Dec 26, 2009 at 12:07 PM, Benilton Carvalho <bcarvalh at jhsph.edu> wrote: > pm(data) > > b > > On Dec

I am using R for environmental forensics (determination of the sources and/or groupings in mixtures of organic chemicals in the field). The goal is to determine in there are groups of samples with similar/dissimilar compositions, and to assign samples to a potential source or a mixture of sources based on the composition (unmixing and source allocation). Typically there are 10 to 50 chemicals that

Hi. Day three and I’m still struggling with this. Any advice to overcome the final hurdle will be enormously appreciated. I now have all the right Java applications etc. in their right places and have managed to get rid of most errors but still get this: — Making package SJava — Building JNI header files... adding build stamp to DESCRIPTION running src/Makefile.win (cd .. ;

Hello all Simple question for the gurus. I'm trying to interactively label curves on a single plot. The labcurve() function from Hmisc seems like the way to do this (?). I just can't seem to get it to work. Toy example: > x <- 1:10 > y1 <- x^2 > y2 <- 2*x > plot(x,y1) > lines(x,y2) > labcurve(labels=c("curve1", "curve2"),

I am using "nls" to fit dose-response curves but am not sure how to approach more robust regression in R to get around the problem of the my error showing increased variance with increasing dose. My understanding is that "rlm" or "lqs" would not be a good idea here. 'Fairly new to regression work, so apologies if I'm missing something obvious.