search for: assay

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',...

...ot;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.) > anova(lm(BackCalac~factor(Assay),data=Control)) Analysis of Variance Table Response: BackCalac Df Sum Sq Mean Sq F value Pr(>F) factor(Assay) 4 270.846 67.711 56.219 1.345e-10 *** Residuals 20 24.088 1.204 But it defau...

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 = pdB...

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$analyte ), function(data) (simint(value ~ tx, data = data,type='Tukey' ) ) ) I can separately prepare plots of the confidence intervals, and I can prepare separate plots or use for/do loop e.g. plot( simint.by.fit$"Assay 1" ) plot( simint.by.fit$"Assay 2&...

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) mo...

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 reproducib...

...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 th...

...ataMini.csv", header=TRUE, sep=",", dec=".") colnames(data) library(nlme) if(weight_significant) { if(gender_significant) { if(weight_gender_interaction_significant) { model=lme(test_variable~Genotype + Weight + Gender + Weight*Gender, random=~1|Assay.Date, data, na.action="na.exclude", method="REML") } else { model=lme(test_variable~Genotype + Weight + Gender, random=~1|Assay.Date, data, na.action="na.exclude", method="REML") } } else { .... etc What I want to do: f...

Greetings ~ I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R. The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems (yolk sac, jaw, snout, body axis, pericardial edema, etc.) for each fish. The...

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,rando...

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("\\)", "", sub("\\(", "",sub("[[:alnum:]]+","",...

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 ~

...olism (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 groups. Typical projects include: assay characterization studies, the analysis of high content screening data, data analysis of chemical structures and properties, image analysis of assay results, the analysis of assay screening data, etc. The statistician will have to demonstrate leadership in influencing and improving drug discovery b...

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) { all(x==a[1,]) }) apply(a[1:3,],1,function(x) { all(x==a[1,]) }) > > 1 2 TRUE FALSE > 1 2 3 FALSE FALSE FALSE > The second result is not...

#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 concentratio...