similar to: Jackknife estimates of predict.lda success rate

Dear all When plotting the results of lda and predict.lda, I expect the axis dimensions to remain essentially the same - predict.lda projects new observations onto linear discriminants. However, I am getting different ranges of values along the x-axis when producing plots following each: plot(lda) yields values in the range approximately -3 to +4, while plot(predict.lda) yields values for the

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 #

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

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

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

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

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

Hi, I am trying to calculate net reclassification improvement (NRI) and Inegrated Discrimination Improvement (IDI) for a survival dataset to compare 2 risk models. It seems that the improveProb() in Hmisc package does this only for binary outcome, while rcorrp.cens() does take survival object, but doesn't output NRI or IDI. Can anyone suggest any other packages that can calculate NRI and IDI

Dear all I would like to replace labels for x in biplot() with symbols (points) that will visually illustrate different classes/groups. After an unsuccessful search through the documents and archives, I turn to the list for help - any suggestions that can point me in the right direction? Thanks Mat Vanderklift

Dear all, I am interested to evaluate reclassification using net reclassification improvement and Integrated Discrimination Index IDI after survival analysis (Cox proportional hazards using stcox). I search a R package or a R code that specifically addresses the categorical NRI for time-to-event data in the presence of censored observation and, if possible, at different follow-up time points. I

As describe in the Hmisc package's improveProb function, that function is for binary Y. And it's best to use category-free methods. Frank Petergodsk wrote > Hi > > I'm new to R. > > Is it possible to use the improveProb function to generate categorybased > NRI using a Cox model? > I believe I saw someone mentioning the possibility, but I can't find the

Greetings, I have generated several ROC curves and would like to compare the AUCs. The data are cross sectional and the outcomes are binary. I am testing which of several models provide the best discrimination. Would it be most appropriate to report AUC with 95% CI's? I have been looking in to the "net reclassification improvement" (see below for reference) but thus far I can only

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

Is there a routine in the boot package to get the jackknife-after- bootstrap influence values? That is, the influence values of a jackknife of the bootstrap estimates? I can see how one would go about it from the jack.after.boot code, but that routine only makes pretty pictures. It wouldn't be hard to write, but I find it hard to believe this isn't part of the package already. Thanks

Hi All, I'm not able to figure out how to perform a Jackknife test for a 2-sample dispersion test in R. Is there a built-in function to perform this or do we have to take a step by step approach to calculate the test statistic? Any help would be awesome. Thanks! -- View this message in context: http://r.789695.n4.nabble.com/Jackknife-for-a-2-sample-dispersion-test-tp4444274p4444274.html

Hi, I have the following jackknife code which is much slower than my colleagues C code. Yet I like R very much and wonder how R experts would optimize this. I think that the for (i in 1:N_B) part is bad because Rprof() said sum() is called very often but I have no idea how to optimize it. #O <- read.table("foo.dat")$V1 O <- runif(100000); k=100 # size of block to delete

Dear all, here is a example of my problem: /#data# g<-c(1,1,1,2,2,2) A<-runif(6,min=1,max=5) B<-runif(6,min=100,max=1000) C<-runif(6,min=30,max=31) D<-runif(6,min=67,max=98765) var<-cbind(A,B,C,D) label<-colnames(var) store<-data.frame(matrix(ncol=2)) colnames(store)=c("usedVar","prediction") library(MASS)#get lda for (i in c(1:4)) { for (k in

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

While doing pls I found the following problem > BHPLS1 <- plsr(GroupingList ~ PCIList, ncomp = 10, data = PLSdata, jackknife = >FALSE, validation = "LOO") when not enabling jackknife the command works fine, but when trying to enable jackknife i get the following error. >BHPLS1 <- plsr(GroupingList ~ PCIList, ncomp = 10, data = PLSdata, jackknife = >TRUE,