similar to: predict.glm -> which class does it predict?

Hi, perhaps you can help me to find out, how to find the best Lambda in a LASSO-model. I have a feature selection problem with 150 proteins potentially predicting Cancer or Noncancer. With a lasso model fit.glm <- glmpath(x=as.matrix(X), y=target, family="binomial") (target is 0, 1 <- Cancer non cancer, X the proteins, numerical in expression), I get following path (PICTURE

Hi, I'm trying to get better at things like lapply but it still stumps me. I have a function I've written, tested and debugged using individual calls to the function, ala: ResultList5 = DoAvgCalcs(IndexData, Lookback=5, SampleSize=TestSamples , Iterations=TestIterations ) ResultList8 = DoAvgCalcs(IndexData, Lookback=8, SampleSize=TestSamples , Iterations=TestIterations ) ResultList13

> - set sampling rate to 8 kHz (at least for now) > - make sure the far end signal in the playback signal is always a bit in > advance (never late) compared to the mic signal. > - Set the tail length to something around 100 ms. > > Also, if you're using two different soundcards (as I understand) for the > playback and the capture, you're *never* going to get echo

My laboratory is measuring the abundance of various proteins in the blood from either healthy individuals or from individuals with various diseases. I would like to determine which proteins, if any, have significantly different abundances between the healthy and diseased individuals. Currently, one of my colleagues is performing an ANOVA on each protein with MS Excel. I would like to analyze

Dear R-developers, I came to a somewhat unexpected behaviour of read.csv() which is trivial but worthwhile to note -- my data involves a protein named "1433E" but to save space I drop the quote so it becomes, Gene,SNP,prot,log10p YWHAE,13:62129097_C_T,1433E,7.35 YWHAE,4:72617557_T_TA,1433E,7.73 Both read.cv() and readr::read_csv() consider prot(ein) name as (possibly confused by

Hi, I'm wondering if anybody could possibly help me? I have a table with 5 tab-delimited columns. Each column has 'e-value' scores for 5 different proteins. I'd like to plot a distribution curve using hist() for the 5 different proteins and show the 5 distribution curves on the same graph in different colours. In the case, E-values will be the X-axis and frequency will be the

?s 11:46 de 16/04/2024, jing hua zhao escreveu: > Dear R-developers, > > I came to a somewhat unexpected behaviour of read.csv() which is trivial but worthwhile to note -- my data involves a protein named "1433E" but to save space I drop the quote so it becomes, > > Gene,SNP,prot,log10p > YWHAE,13:62129097_C_T,1433E,7.35 > YWHAE,4:72617557_T_TA,1433E,7.73 >

/Dear all,/// /I am very thankful, if you could tell what is the right way to write: mtext(paste(expression("R"^2),round(marco2[1,i],digits=3)," N? of proteins:",marco3[i]),side=4,cex=.6) in this case the output is: "R"^2 I tried also in this way: mtext(paste(expression(paste("R"^2)),round(marco2[1,i],digits=3)," N? of

Gene names being misinterpreted by spreadsheet software (read.csv is no different) is a classic issue in bioinformatics. It seems like every practitioner ends up encountering this issue in due time. E.g. https://pubmed.ncbi.nlm.nih.gov/15214961/ https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1044-7 https://www.nature.com/articles/d41586-021-02211-4

Hello everyone, I would like to parse very large xml files from MS/MS experiments and create R objects from their content. (By very large, I mean going up to 5-10Gb, although I am using a 'small' 40M file to test my code.) My first attempt at parsing the 40M file, using the XML package, took more than 2200 seconds and left me quite disappointed. I managed to cut that down to around 40

Hi Guy's I was wondering if someone could point me in the right direction. dbinom(10,1,0.25) I am using dbinom(10,1,0.25) to calculate the probabilty of 10 judges choosing a certain brand x times. I was wondering how I would go about simulating 1000 trials of each x value ? regards Brendan -- View this message in context:

Dear all, I am a R beginner, and I am looking for a way to do the same thing for all levels of a column in a table. Basically, I have a bunch of protein sequences composed of different amino acid residues, and each residue is represented by an uppercase letter. I want to calculate the ratio of different amino acid residues at each position of the proteins. Here is an example table: Proteins

All Is there a package or library that will, given a nucleotide sequence 1. calculate the extinction coefficient at 260 nm for (Beer-Lambert's law) 2. calculate molecular weight 3. return it's complementary sequence I was able to find several packages that can do similar calculations for an amino acid sequence for proteins but none for nucleic acids. Any pointers, etc. would be

Dear All, I have two matrices A (40 x 732) and B (40 x 1230) and would like to calculate correlation between them.  I can use: cor(A,B, method="pearson") to calculate correlation between all possible pairs. But the issue is that there is one-many specific mappings between A and B and I just need to calculate correlations for those pairs (not all). Some variables in A (proteins, say p1)

hi, is there any wait function in R. I am running one R script to plot many graphs it is in the for loop. its showing no error but its not plotting well I think i can solve this problem with a wait function. Please help me in this regards. If u need any clarification about programme. u can find the script below. best regards, Deepak.M.R Biocomputing Group University of Bologana. #!/usr/bin/R

Hi everybody, I am having a problem building a ROC curve with my data using the ROCR package. I have 10 lists of proteins such as attached (proteinlist.xls). each of the lists was calculated with a different p-value. The goal is to find the optimal p-value for the highest number of true positives as well as lowaest number of false positives. As far as I understood the explanations from the

Dear R users, i try to calculate the probabilty to survive a given time by using the estimated survival curve by kaplan meier. What is the right way to do that? as far as is see i cannot use the predict-methods from the survival package? library(survival) set.seed(1) time <- cumsum(rexp(1000)/10) status <- rbinom(1000, 1, 0.5) ## kaplan meier estimates fit <- survfit(Surv(time,

Hi, I have tried looking at the archives but havent found any answer that works till now (Sorry if i have missed anything) I am a newbie to R and i am trying to carry out hierarchical clustering using hclust -> as.dendrogram and then plotting the results as a dendrogram using the plot function plot(object). My question is : In the function "plot", can one decrease the leaf label

library(sos) (mp <- findFn('{molecular properties}')) ????? ** found 7 matches in 4 packages and opened two web pages in my default browser with (a) the 7 matches and (b) the 4 packages. The first function was something for amino acids, like you suggested.? Two others returned compound and substance information from PubChem. ????? Does this help? ????? Spencer On

Dear list, I would like to plot the empirical cumulative distribution of the time needed by a treatment to attain a certain goal. A number of experiments is run with a strict time limit. In some experiments the goal is attained before the time limit, in other experiments time expires before the goal is attained. The situation is very similar to survivial analysis with censored data. I tryed