similar to: Help with Memory Problems (cannot allocate vector of size)

Hi I am analysing a dataset of 40 samples each with 90,000 intensity measures for various peptides. I am trying to identify the Biomarkers (i.e. most significant peptides). I beleive that PLS with jack knifing, or alternativeley CMV(cross-model-validation) are multivariateThe 40 samples belong to four different groups. I have managed to conduct the plsr using the commands: BHPLS1 <-

Hi I am attempting to use plsr which is part of the pls package in r. I amconducting analysis on datasets to identify which proteins/peptides are responsible for the variance between sample groups (Biomarker Spoting) in a multivariate fashion. I have a dataset in R called "FullDataListTrans". as you can see below the structure of the data is 40 different rows representing a

Hi When I call for the loadings of my plsr using the command, x <- loadings(BHPLS1) my loadings contain variable names rather than numbers. >str(x) loadings [1:94727, 1:10] -0.00113 -0.03001 -0.00059 -0.00734 -0.02969 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:94727] "PCIList1" "PCIList2" "PCIList3" "PCIList4" ... ..$ : chr

Hi I have implented boxplots in my script to create box plots BoxplotsCheck <- readline(prompt = "Would you like to create boxplots for any Feature? (y/n):")   if (BoxplotsCheck  == "y"){     BoxplotsFeature <- readline(prompt = "Which Feature would you like to create a Boxplot for?:")     BoxplotsFeature <- as.numeric(BoxplotsFeature)     BoxplotsData

Hi, this is my R-Script library(pls) file <- "C:\\TXT\\brix.txt" d <- as.matrix(read.table(file, header=T, sep=",", row.names = NULL)) plsdata <- data.frame(NIR=c(1:nrow(X))) plsdata$NIR <- I(d[,3:603]) plsdata$Brix <- d[,2] results <- plsr(Brix ~ NIR, data=plsdata) after the last string i have this error > results <- plsr(Brix ~ NIR, data=plsdata)

Hi I am attempting to do a loadings plot from a plsr object. I have managed to do this using the gasoline data that comes with the pls package. However when I conduct this on my dataset i get the following error message. >plot(BHPLS1, "loadings", comps = 1:2, legendpos = "topleft", labels = "numbers", >xlab = "nm") Error in

Hi R-experts, I want to use the jackknife function from the bootstrap package onto a linear model. I can't figure out how to do that. The manual says the following: # To jackknife functions of more complex data structures, # write theta so that its argument x # is the set of observation numbers # and simply pass as data to jackknife the vector 1,2,..n. # For example, to jackknife #

All, I am working with NIR spectral data and it was great to find that the example in ?plsr also used spectral data. Unfortunately, I am having difficulty figuring out how the "yarn" dataset is structured to allow for the plsr model to read: library(pls) data(yard) yarn.oscorespls <- mvr(density ~ NIR, 6, data = yarn, validation = "CV", method = "oscorespls")

Hi dear all, Can aynone help me about delete-d jackknife usually normal jackknife code for my data is: n <- nrow(data) y <- data$y z <- data$z theta.hat <- mean(y) / mean(z) print (theta.hat) theta.jack <- numeric(n) for (i in 1:n) theta.jack[i] <- mean(y[-i]) / mean(z[-i]) bias <- (n - 1) * (mean(theta.jack) - theta.hat) print(bias) but how i can apply delete-d jackknife

Below is a SIMEX object that was generated with the "simex" function from the "simex" package applied to a logistic regression fit. From this mountain of information I would like to extract all of the values summarized in this line: .. ..$ variance.jackknife: num [1:5, 1:4] 1.684 1.144 0.85 0.624 0.519 ... Can someone suggest how to go about doing this? I can extract the

Dear all I have used the lda and predict functions to classify a set of objects of unknown origin. I would like to use a jackknife reclassification to assess the degree to which the outcomes deviate from that expected by chance. However, I can't find any function that allows me to do this. Any suggestions of how to generate the jackknife reclassification to assess classification accuracy?

Hi dear all, Can someone help me about detection of outliers using jackknife after bootstrap algorithm? -- View this message in context: http://r.789695.n4.nabble.com/jackknife-after-bootstrap-tp3041634p3041634.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]]

Dear R friends I´m interested into apply a Jackknife analysis to in order to quantify the uncertainty of my coefficients estimated by the logistic regression. I´m using a glm(family=’binomial’) because my independent variable is in 0 - 1 format. My dataset has 76000 obs, and I´m using 7 independent variables plus an offset. The idea involves to split the data in let’s say 5 random subsets and

Hi, First, thanks to those who helped me see my gross misunderstanding of randomForest. I worked through a baging tutorial and now understand the "many tree" approach. However, it is not what I want to do! My bagged errors are accpetable but I need to use the actual tree and need a single tree application. I am using rpart for a classification tree but am interested in a more unbaised

Folks, Thanks for the help with the hier.part analysis. All the problems stemmed from an import problem which was solved with file.chose(). Now that I have the variables that I'd like to use I need to run some GLM models. I think I have that part under control but I'd like to use a jackknife approach to model validation (I was using a hold out sample but this seems to have fallen out

Dear R users, I?m student of Master in Statistic and Data analysis, in New University of Lisbon. And now i?m writting my dissertation in variance estimation.So i?m using Survey Package to compute the principal estimators and theirs variances. My data is from Incoming and Expendire Survey. This is stratified Multi-stage Survey care out by National Statistic Institute of Mozambique. My domain of

This is not a criticism. I'm just curious. Is there an effort to keep R comparable to S+? Or are the two languages diverging? I am doing what probably legions have done before me, and legions will after me...using R on examples from text books written with S+ code. Most of the time everything appears to be equivalent. And then there are amazing divergences in commands. For instance: S:

I'm using the lme function in nmle to estimate the variance components of a fully nested two-level model: Y_ijk = mu + a_i + b_j(i) + e_k(j(i)) lme computes estimates of the variances for a, b, and e, call them v_a, v_b, and v_e, and I can use the intervals function to get confidence intervals. My understanding is that these intervals are probably not that robust plus I need intervals on the

Dear all, After a thorough research, I still find myself unable to find a function that does linear regression of 2 vectors of data using the "total least squares", also called "orthogonal regression" (see : http://en.wikipedia.org/wiki/Total_least_squares) instead of the "ordinary least squares" method. Indeed, the "lm" function has a

Hi! Some time ago, I replaced calls to library() with calls to requireNamespace() in my package logmult, in order to follow the new CRAN policies. But I just noticed it broke jackknife/bootstrap using several workers via package parallel. The reason is that I'm running model replicates on the workers, and the formula includes non-standard terms like Mult() which are provided by gnm. If gnm