search for: assays

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) :

...#' limitations of the `[` S4 method. #' setOldClass('long.table') #' LongTable class definition #' #' Define a private constructor method to be used to build a `LongTable` object. #' #' @param drugs [`data.table`] #' @param cells [`data.table`] #' @param assays [`list`] #' @param metadata [`list`] #' #' #' @return [`LongTable`] object containing the assay data from a #' #' @import data.table #' @keywords internal .LongTable <- setClass("LongTable", slots=list(rowData='data.table',...

Hello everyone: I'm a new member of this group. I have a question about "oneway.test". When I use "anova(lm(....))" to analysis the ANOVA, I can get the information about Sum Sq and Mean Sq. (The R code and the results are as follows.)

Dear R-helpers, I am developing a Mixed-Effects model for a study of immunoassays using 'lme4' library. The study design is as follows: 10 samples were run using 7 different immunoassays, 3 times each, in duplicates. Data attached. I have developed the following model: c.lme <- lmer(Result~SPL + (SPL|Assay/Run) -1, data=data) This model has excellent predictions...

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

Using a "by() statement, I am preparing ANOVA's for multiple experiments, and using simint() to generate confidence intervals. This works fine. simint.by.fit <- by(analytes.dfr, list(Assay = analytes.dfr$analyte ), function(data) (simint(value ~ tx, data = data,type='Tukey' ) ) ) I can separately prepare plots of the confidence intervals, and I can prepare separate plots

Hi, I'm have some data on a grid (specifically high throughput assay plates) and am interested in evaluating measures of spatial autocorrelation to flag plates for corrections. I have been using moran.test and geary.test from the spdep package. My approach is as follows: ## plate is a matrix of data coords <- expand.grid(1:32, 1:48) x <- as.numeric(plate) moran.test(x,

Hi. I have data which i would want to assess the degree of agreement between two assays, e.g., to evaluate reproducibility or for inter-rater reliability. I have used the Pearson product-moment correlation coefficient. It looks good ranginging between 0.90 to 0.998. Though this looks good. I am told the Concordance correlation coefficient will give a better picture of how reproducibl...

...commodate a negative control sample by not including it in any genotype cluster. I'm looking at both nuclear and mitochondrial DNA so hopefully it can be sophisticated enough to set the number of cluster between two or three within the array. These genotyping arrays are either 48 samples x 48 assays, 96x96, or 192x24 and it would be nice if it could accommodate any range of samples and assays. the data headings from the csv are: ID,Assay,Allele Y,Allele X,Name,Type,Auto,Confidence,Final,Converted,Allele Y,Allele X where Allele Y and Allele X are the plotted values and the vectors within the...

Hi, I need to construct a formula programaticly, and pass it to a function such as the linear mixed model lme. The help says it requires "a two-sided linear formula object describing the fixed-effects part of the model" but I do not know how to create this formula. I have tried various things using formula(x, ...), as.formula(object, env = parent.frame()) and as.Formula(x, ...)

...lasses of chemicals (a MANOVA-type analysis). Interest is focused on where response(s) are occurring, any associations among responses, and ultimately on possible biological mechanisms (the fish are tested for behavioral activity prior to this assay, and then ground up to provide RNA for microarray assays. A-lotta-data!). What I *wish* I could do is something like glm(response.matrix ~ treat/vial, family=binomial(logit), data=zf.dat) but I know this can't be done. I came across the baymvb (Bayesian analysis of multivariate binary data) package in the R contributed packages archives, but it is n...

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

describe Asset, " when destroyed" do fixtures :assets, :videos, :sites, :publish_settings before(:each) do @asset = assets(:test_asset) @mock_hook = mock("hook") @asset.video.stub!(:hook).and_return @mock_hook # error occurs here end it "should call the delete hook" do @mock_hook.should_receive(:update).with("test_video",

Hi, this should be an easy one, but I can't figure it out. I have a vector of tests, with their units between brackets (if they have units). eg tests <- c("pH", "Assay (%)", "Impurity A(%)", "content (mg/ml)") Now I would like to hava a function where I use a test as input, and which returns the units like: f <- function (x) sub("\\)",

Hi I am attempting Anova analysis to compare results from four groups (Samp1-4) which are lists of intensities from the experiment. I am doing this by first creating a structured list of the data and then conducting the ANOVA (Script provided below). Im an R beginner so am not sure if I am using this correctly. Two major questions I have are: 1) Is using the code (zzz.aov <- aov(Intensity ~

Pfizer is hiring a statistician supporting early drug discovery for Global Medicinal Chemistry and Pharmacokinetics, Dynamics and Metabolism (PDM). If interested, go to http://pfizercareers.com/ and search for job ID 985944. The location is Groton, Connecticut. Responsibilities The statistician will have a consulting-type role supporting a large pool of scientists in two large platform line

Hello, In my code I found something that looks like an anomaly, I found a reproducible example in which I am just trying to compare each row in a data frame against the first row: a<-data.frame(row=c("B","C","B"),column=c(2,2,10),assay=c("Assay1","Assay1","Assay1"),plate=c(1,1,1),stringsAsFactors=FALSE) apply(a[1:2,],1,function(x) {

#If I have two lists as follows a1<- array(1:6, dim=c(2,3)) a2<- array(7:12, dim=c(2,3)) l1<- list(a1,a2) a3<- array(1:4, dim=c(2,2)) a4<- array(5:8, dim=c(2,2)) l2<- list(a3,a4) #how can I create a new list with the mean across all arrays within the list, so all components are included? As an example for [[1]]; cbind((l1[[1]][,1]+l2[[1]][,1])/2,

...ummary: The successful applicant will integrate a dynamic team of biostatisticians, bioinformaticians, computer scientists, and programmers lead by Dr. Raphael Gottardo to support the development of software packages to analyze and integrate high throughput data from next generation immunological assays (e.g. Fluidigm, CyTOF, next generation sequencing). The position will involve the development of efficient packages for the analysis of high-throughput data, including computational algorithms development and implementation of creative solutions for large volume data management and processing. The...

Hi, I am dealing with the following problem. There are two biochemical assays, say A and B, available for analyzing blood samples. Half the samples have been analyzed with A. Now, for some insurmountable logistic reasons, we have to use B to analyze the remaining samples. However, we can do a comparative study on a small number of samples where we can obtain concentration...