search for: jackknif

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 jac...

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(bi...

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? (BTW, commands I used to generate...

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]]

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 upper level results like fit.simex$coefficients but I have had no success getting at the lower levels. Tom > str(fit.simex) List of 24 $ coefficients : Named num [...

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 spl...

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 of favor). I'd appreciate it if someone could just point me in the right direction for the jackkife analysis given a particular distribution, coefficients, etc. Thanks, Jeff ********************...

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, validation = "LOO") Error:...

...plication. I am using rpart for a classification tree but am interested in a more unbaised estimator of error in my tree. I lack sufficent data to train and test the tree and I'm hoping to bootstrap, or rather jacknife, an error estimate. I do not think the rpart.object can be applied to the jackknife function in bootstrap but can I do something as simple as: for(i in 1:number of samples){ remove i from the data run the tree compare sample[i] to the tree using predict create an error matrix} This would give me a confussion matrix of data not included in the tree's constuction. Am...

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&...

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"...

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-dispe...

...ng and Expendire Survey. This is stratified Multi-stage Survey care out by National Statistic Institute of Mozambique. My domain of analysis is Maputo City, the Capital of Mozambique. I just compute the sampling errors using Survey Package, but i have some droubles: 1. How to compute Bootstrap and Jackknife Bias of estimates, like mean? 2. How to see each replicate estimate of the parameter? 3. Is it possible to use the Sitter algoritm in Bootstrap for stratified and multi-stage sampling in Survey Package? Or is possible to use the pseudo-population (obtained by replication the sample) in estimatio...

Greetings, I?ve fit the following mixed effects model using the NLME package: hd.impute.lme <- lme(I(log(HEIGHT_M - 1.37)) ~ SPECIES + SPECIES:I(1/(DBH_CM + 2.54)), random = ~ I(1/(DBH_CM + 2.54)) | PLOTID, data = trees, na.action = na.exclude) I would now like to extract a jackknife estimate of model error. I tried the following code, however, the estimate produced seems too low. ss.ok <- 0 for (i in 1:dim(trees)[1]) lme.msc <- ss.ok + sum(sd(lme(I(log(HEIGHT_M - 1.37)) ~ SPECIES + SPECIES:I(1/(DBH_CM + 2.54)), random = ~ I(1/(DBH_CM + 2.54)) | PLOTID,...

...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 intraclass correlation coefficients: v_a/(v_a + v_b + v_e) and (v_a + v_b)/(v_a + v_b + v_e). I would like to use a bootstrap or a jackknife estimate of these confidence intervals. Is there any package available? I've tried to write the R code myself but I'm not sure if I understand exactly how to do it. The way I've tried to do a bootstrap estimate is to sample with replacement for i units, then sample with replacement th...

Hi, after looking around I was not able to get info about these two questions: 1. Is there a function to have a "jackknifed corrected var/cov estimate" (as described in MacKinnon and White 1985) in a standard OLS regression? 2. Does R possess a LAD (Least Absolute Deviation) regression function? Any help? Thanks -- ======================================================== Angelo Secchi P...

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

Dear R-users, I have two samples with individuals that are in more than one of the samples and individuals that are only in one sample. I have been trying to do a pooled hazard model, stacking one sample below the other, with aftreg and time-dependent covariates. The idea behind is to see aggregate effects of covariates, but need to control for ther effects of same individuals in both samples

...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: stdev R: sd why this difference? Other examples... S: Bootstrap R: Boot S: Jackknife R: NA S: T, F R: TRUE, FALSE For those who want to use open source R but still use the excellent S+ literature...these differences can be confusing. I can also appreciate the stress on authors attempting to express code that works in both languages. REX

...r > subjects NOT in the subcohort, so the correct coxph call is > > coxph(Surv(edrel, rel) ~ stage + histol + age + > offset(-100*(subcohort==0)) + cluster(seqno), data =ccoh.data) > > This gives the same coefficients as the cch example, along with the > infinitesimal jackknife or "robust" variance estimate. > > Terry Therneau > > >