similar to: packages for LIML and JIVE estimators

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 #

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 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

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,

Dear all, I am using the Instrumental Variable approach to estimate the causal effects of TWO endogenous variables in a Mendelian Randomization study. As long as point estimation is concerned, I have no problem: both "ivreg" in library "AER" and "tsls" in library "sem" do the job perfectly. The problems begin when I try to obtain confidence intervals for

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 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]]

Hello, I am interested in running generalized estimating equation models in R. Currently there are two main packages for doing so in R, geepack and gee. I understand that even though one can obtain similar to almost identical results using either of the two, that there are differences between the packages. The paper that introduces the geepack package (

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

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:

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

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

I need a script that works for Bootstrap validation. Could someone explain me when should I use the Bootstrap technical or Jackknife? Thanks -- View this message in context: http://www.nabble.com/BOOTSTRAP_CROSS-VALIDATION-tp22529500p22529500.html Sent from the R help mailing list archive at Nabble.com.

Hi everyone! In a package I'm developing, I have created a custom function to get jackknife standard errors for the parameters of a gnm model (which is essentially the same as a glm model for this issue). I'd like to add support for bootstrap using package boot, but I couldn't find how to proceed. The problem is, my data is a table object. Thus, I don't have one individual per

Hi I constructed a mixed-effects model from longitudinal repeated measurements of lab values in 22 patients seperated into two groups with the groups as fixed effect using lme. I thought about using the jackknife procedure, i. e., removing any one subject and calculating the fixed effect, to assess the stability of the fixed effect and thereby validate the model. I suppose this has been done in

I tried to compare if cch() and coxph() can generate same result for same case cohort data Use the standard data in cch(): nwtco Since in cch contains the cohort size=4028, while ccoh.data size =1154 after selection, but coxph does not contain info of cohort size=4028. The rough estimate between coxph() and cch() is same, but the lower and upper CI and P-value are a little different. Can we

Dear R-Users: I am trying to find the robust (or sandwich) estimates of the standard error of fixed effects parameter estimates using the package "lmer2". In model-1, I used "robust=TRUE" on the other, in model-2, I used "robust=FALSE". Both models giving me the same estimates. So my question is, does the robust option works in lmer2 to get the robust estimates of