search for: dilution

Dear R users: I have some difficulties analizing data with mixed effects NLME and the last version of R. More concretely, I have a repeated measures design with a single group and 2 experimental factors (say A and B) and my interest is to compare additive and nonadditive models. suj rv A B 1 s1 4 a1 b1 2 s1 5 a1 b2 3 s1 7 a1 b3 4 s1 1 a2

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 at the figure obtained from the sample set 1; however the figure obtained from the sample set 2 shows that interpolation is not correct. A...

...test R on a presumably up to date Linux server. 'Doing something silly I'm sure, but can't see why my saved PLMset objects come out all wrong. To use an example: Setting up an example PLMset (I have the same problem no matter what example I use) > library(affyPLM) > data(Dilution) # affybatch object > Dilution = updateObject(Dilution) > options(width=36) > expr <- fitPLM(Dilution) This works, and I'm able to get the probeset coefficients with coefs(expr). until I save and try reloading: > save(expr, file="expr.RData") > rm(expr) #...

Consider the Assay data where block, sample within block and dilut within block is random. This model can be fitted with (where I define Assay2 to get an ordinary data frame rather than a grouped data object): Assay2 <- as.data.frame(Assay) fm2<-lme(logDens~sample*dilut, data=Assay2, random=list(Block = pdBlocked(list(pdIdent(~1), pdIdent(~sample-1),pdIdent(~dilut-1))) )) Now, block

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 antibodies in function of each clone of the fungus. However one measure of interest is the area under the curve (AUC) for the counting of antibodies in function...

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 'log=T' is stated... If I comment out this example I end up with an another similar error i...

Try this data(Assay) as1 <- lme(logDens~sample*dilut, data=Assay, random=pdBlocked(list( pdIdent(~1), pdIdent(~sample-1), pdIdent(~dilut-1)))) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) I'm

...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 R containing 15 columns (a charcoal dilution series going from 100% to 0%) and 1050 rows of absorbance data from 400 nm to 2500 nm at 2 nm interval. I think I need to transpose the data such that the specific wavelengths become my columns and dilutions are defined in rows, but after that point I am lost. Should I (and how do I) make my absor...

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 samples will generate the following concentrations: 1, 1/2, 1.4, 1/8, 1/16, ... (or the reciprocal: 1, 2, 4, 8, 16, ...) It makes no sense to compute the arithmetic mean. Results are usually reported as some...

Hello all: I am interested in computing what the multilevel modeling literature calls a multiple membership model. More specifically, I am working with a data set involving clients and providers. The clients are the lower-level units who are nested within providers (higher-level). However, this is not nesting in the usual sense, as clients can belong to multple providers, which I understand

Hello R guru's I am a newbie to R, In my research work I usually generate a lot of ELISA data in form of absorbance values. I ususally use Excel to calculate the concentrations of unknown, but it is too tedious and manual especially when I have 100's of files to process. I would appreciate some help in creating a R script to do this with minimal manual input. s A1-G1 and A2-G2 are

On Mon, Jun 15, 2009 at 9:40 PM, Duncan Sands<baldrick at free.fr> wrote: > you can call the libgcc/libunwind routines directly.  There was an > example of this on the mailing list by Talin not long ago. I'll look into this. Thanks. > That said, > it wouldn't be too hard to support "unwind" in the code generators. > It would basically mean creating

Hi dear R-listers, I'm trying to fit a 3-level model using lme in R. My sample size is about 2965 and 3 factors: year (5 levels), ssize (4 levels), condition (2 levels). When I issue the following command: > lme(var~year*ssize*condition,random=~ssize+condition|subject,data=smp,method ="ML") I got the following error: Error in logLik.lmeStructInt(lmeSt, lmePars) :

Dear List, The gbm package on Win 7 produces different results for the relative importance of input variables in R 32-bit relative to R 64-bit. Any idea why? Any idea which one is correct? Based on this example, it looks like the relative importance of 2 perfectly correlated predictors is "diluted" by half in 32-bit, whereas in 64-bit, one of these predictors gets all the importance

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 ActiveR...

...al source or a mixture of sources based on the composition (unmixing and source allocation). Typically there are 10 to 50 chemicals that have been analyzed in all of the samples. In most cases concentrations are converted to proportion of total as we are interested in composition rather that simple dilution. I have had great success with ratio analysis; simple exploratory analysis such a property property plots etc; and cluster analysis such as principal components analysis (PCA) and hierarchal cluster analysis. I have also been experimenting with glyph representation, k-means clusters, and similar p...

I am having a problem with asterisk voice mail messages that seems to be intermittent. Though the problem occurs most of the time, on rare occasions it will work fine - rare enough that I can't pin down what it is that works. The problem is that voice mail message get played back garbled. Occasionally, I can make out moments of a voice or another sound that may be in the actual message,

Hi useRs, In trying to take R to engineering undergraduate students, I have been looking for context that would make R more accessible to the said audience. Though R is primarily a statistical tool, I would want to demonstrate the use of R for certain engineering courses (Design of Machine Elements - gear design, ball bearings, etc.) which would generate interest in it and provide students a way

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On 31 March 2016 at 21:41, Mehdi Amini via llvm-dev <llvm-dev at lists.llvm.org> wrote: > TLDR: I totally support considering compile time regression as bug. Me too. I also agree that reverting fresh and reapplying is *much* easier than trying to revert late. But I'd like to avoid dubious metrics. > The closest I could find would be what Chandler wrote in: >