search for: assayed

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) :

Hello R-devel, I am currently attempting to implement an API similar to data.table wherein single bracket subsetting can accept an unquoted expression to be evaluated in the context of my object. A simple example from the data.table package looks like this: DT <- data.table(col1 = c('a', 'b', 'c'), col2 = c('x', 'y', 'z')) DT[col1 ==

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 - the Rsquared of

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 reproducible the assay

I know that there are quite a few packages out that there for cluster analysis. The problem that I am facing is finding a package that will not incorporate all my samples into clusters but just the samples that fit a threshold (that I have not set yet and may need help finding the right level) for genotyping. It should be able to "no call" samples outside the clusters. It also needs to

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, ...)

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

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,

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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 concentrations using both A and