search for: dilutes

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

Hi I'm running the latest 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)

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

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

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

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

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 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

...unwind the stack down to the nearest 'unwind label' of an invoke. Thread-local storage, global variable, or any other approach one might think of to carry the exception information is a frontend policy decision. Adding this support to 'unwind' complicates its implementation and also dilutes its usefulness.

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

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

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: >