similar to: Multivariate binary response analysis

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

Hi, I want to create a data frame similar to the following, but greatly scaled up: df <- data.frame(aaa= c("a","b","c"), integer(3), integer(3)) names(df)[2:3] <- paste("var",1:2,sep="") which yields aaa var1 var2 1 a 0 0 2 b 0 0 3 c 0 0 I would not relish having to paste 'integer(3)' 5000 times :(

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.)

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

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

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 ==

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, ...)

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) :

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

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("\\)",

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) {

Is there a good way in R to impute values which exist, but are less than the detection level for an assay? Thanks, Jonathan Williams OPTIMA Radcliffe Infirmary Woodstock Road OXFORD OX2 6HE Tel +1865 (2)24356

Hi: I have data from an assay in the form of two vectors, one is response and the other is a predictor. When I attempt to fit a 5 parameter logistic model with "nls", I get converged parameter estimates. I also get the same answers with "gnls" without specifying the "weights" argument. However, when I attempt to use the "gnls" function and try to

Dear R users I have built the following model m1<-lmer(y~harn+foodn+(1|ass%in%pop%in%fam),family = "quasibinomial") where y<-cbind(alive,dead) where harn and foodn are categorical factors and the random effect is a nested term to represent experimental structure e.g. Day/Block/Replicate ass= 5 level factor, pop= 2 populations per treatment factor in each assay, 7 reps

Hello R experts: When I tried to read my data into R, it does not take # sign A subset of Exp.txt is: Experiment name assay id Varname (A1)DBA TPA 6h/DBA Acetone rep1(A1) #3 4090 A90C1 (A2)DBA TPA 6h/DBA Acetone rep2(A2) #3 4091 A91C1 The command is: Exp <- read.delim2("Exp.txt",check.names=F,as.is=T) It is excuted but gave me all the NAs. Can you all drop me a hint? Thanks

I'm looking for software that makes plots such as fig 4 (a)-(e), fig 5 anf fig 7 of Gray, Robert, "Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer Prognosis," 1992, J Am Stat Assoc, pp 942-51. In other words, I'm looking for software that takes survival data and a continuous covariate as input and computes a curve giving log hazard

I have hundreds of humans who have undergone SNP genotyping at hundreds of loci. Some have even undergone the procedure twice or thrice (kind of an internal control). So obviously I need to find those replications, and confirm that the results are the same. If there is discordance then I need to address it. I tried to use the aggregate function nr.attempts

Dear R-helpers, I have a dataset named "qu", organized as follows: Sample Run Replicate Value 1 1 1 25 1 1 2 40 1 1 3 33 1 1 4 29 1 2 1 37 1 2 2 44 1 2 3 45 1 3 1 25 1 3 2 40 1 4 1 33 1 4 2 29 1 4 3 25 2 ... Basically, a sample was run on an assay multiple times within a single day. Each of these results is "Replicate". Then run was repeated several times in

The Gottardo lab at the Fred Hutchinson Cancer Research Center in Seattle has an opening for a programmer. To apply: https://erecruit.fhcrc.org/psp/RECRUIT/EMPLOYEE/HRMS/c/HRS_HRAM.HRS_CE.GBL?Page=HRS_CE_JOB_DTL&Action=A&JobOpeningId=24122&SiteId=2&PostingSeq=1 The position description is below: About Us Fred Hutchinson Cancer Research Center, home of three Nobel laureates,