similar to: Bioconductor. MA plot for qPCR array

Hi! Is There a way to manually create an affybatch object from qPCR array data? -- View this message in context: http://r.789695.n4.nabble.com/Creating-affybatch-objects-from-matrix-data-from-qPCR-array-tp3921559p3921559.html Sent from the R help mailing list archive at Nabble.com.

I want to use function cbind.na at library(qpcR) I install package qpcR and I can use functions such m1 <- pcrfit(reps, 1, 2, l5) > AICc(m1) [1] -102.5843 but when i try cbind.na(1, 1:7) i take message Error: could not find function "cbind.na" Thanks -- View this message in context: http://r.789695.n4.nabble.com/library-qpcR-cbind-na-tp4023339p4023339.html Sent from the

I am using R on a Windows XP professional platform. The following code is part of a bigger one CODE press=function(y,x){ library(qpcR) models.press=numeric(0) cat("\n") dep=y print(dep) indep=log(x) print(indep) yfit=dep-PRESS(lm(dep~indep))[[2]] cat("\n yfit\n") print(yfit) yfit.orig=yfit presid=y-yfit.orig press=sum(presid^2)

Hi every one, How is it possible to create a matrix with 3 dimensions from several 2 dimensional matrice? Is it possible that each of "elementary/building block" matrices could be called by its corresponding original name? Thanks alot. -- View this message in context:

Hi everyone. Newbie to statistics. I have 40 matrices of ~400 values. how may I determine whether the distribution follows zips law? response <-sample (1:20,400*4, replace= TRUE) Thank you vry much. -- View this message in context: http://r.789695.n4.nabble.com/A-introductory-question-about-Zips-law-Newbie-to-statistics-tp4521190p4521190.html Sent from the R help mailing list archive at

Hi all, I want to do this: B.list$aa= (a loop containing My.fun acting on the reults of second function on a A.list$aa)) or, overally B.list$aa = function (A.list$aa) B.list and A.list has many sublists aa, ab and.... Is there a way I can apply the function and loop on all sublists of A.list and get B.list? Thanks in advance. -- View this message in context:

Inline. -- Bert On Tue, Apr 26, 2016 at 2:25 PM, Andr? Luis Neves <andrluis at ualberta.ca> wrote: > Ok. I`m trying to run a Poisson glmm with an observation-level random > intercept. But I`m getting the following error for the 'Baci' variable: > > 'Error: (maxstephalfit) PIRLS step-halvings failed to reduce deviance in > pwrssUpdate'. I guess this message

Ok. I`m trying to run a Poisson glmm with an observation-level random intercept. But I`m getting the following error for the 'Baci' variable: 'Error: (maxstephalfit) PIRLS step-halvings failed to reduce deviance in pwrssUpdate'. I guess this message is because the baci variable is not a an integer, and cannot be transformed into an integer as R has a threshold of 2x10^9 even in

Hi! How can I fit a loess curve to an array (384 x 2). How can I obtain the equation for thi fi? Thanks in advance. -- View this message in context: http://r.789695.n4.nabble.com/How-to-it-a-loess-curve-and-obtain-the-equation-in-R-tp3927622p3927622.html Sent from the R help mailing list archive at Nabble.com.

I am using LOF.test() function from the qpcR package and got the following result: > LOF.test(nlregmod3) $pF [1] 0.97686 $pLR [1] 0.77025 Can I conclude from the LOF.test() results that my nonlinear regression model is significant/statistically significant? Where my nonlinear model was fitted as follows: nlregmod3 <- nlsr(formula=y ~ theta1 - theta2*exp(-theta3*x), data =

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, Many apologies for sending this twice. I accidentally hit the send button before I finished writing my mail. I am new to R and I hope someone can help me with my problem. I am trying to draw a side by side barplot. There is a main experiment and there are many sub experiments within the main experiment. I would like to draw a bar plot showing the number and type of sub_experiments done for

Hej, Calling newuoa (from the minqa package) makes R crash when the package rgl is loaded first. This however only on certain selected data. The data used for testing (saved to 'bugs.R'): xvals = c(1,2,4,5,7,8,9,10,11,12,14,15,16,18,19,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36) yvals =

Hi, I am looking to build even quintiles for a set of data. I managed to get it done, but I would like to know if there is a more direct way to write the data from my loop output x in the bottom of the code into the "empty" matrix p1, which I filled with NA''s. The way I am doing it at the moment is, more or less adding the matrix x after p1 and then deleting in a second step

> On Apr 10, 2016, at 9:38 AM, David Winsemius <dwinsemius at comcast.net> wrote: > >> >> On Apr 10, 2016, at 3:11 AM, Murray Efford <murray.efford at otago.ac.nz> wrote: >> >> Martin - >> Thanks, but although hatvalues() is useful for calculating PRESS, I can't find anything directly relevant to my question in the influence help pages. After

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'm having a problem getting the panel.average function to work as I expect it to in a lattice plot. I wish to draw lines between the averages of groups of y-values at specific x-values. I have created a dataset below which is similar to my real data. I also show an example of using panel.loess in place of panel.average; it performs in a manner similar to what I want panel.average to do

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) :

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

Hi I get the following error when I try and get the AICc for a gls regression using qpcR: > AICc(gls1) Loading required package: nlme Error in n/(n - p - 1) : 'n' is missing My gls is like this: > gls1 Generalized least squares fit by REML Model: thercarnmax ~ therherbmax Data: NULL Log-restricted-likelihood: 2.328125 Coefficients: (Intercept) therherbmax 1.6441405