similar to: Bootstrap (bootSem) causes R to crash

I like to bootstrap regression models, saving the entire set of bootstrapped regression coefficients for later use so that I can get confidence limits for a whole set of contrasts derived from the coefficients. I'm finding that ordinary bootstrap percentile confidence limits can provide poor coverage for odds ratios for binary logistic models with small N. So I'm exploring BCa confidence

Hey, everyone, I am using proportional odds model for ordinal responses in dose-response experiments. For some samll data, SAS can successfully provide estimators of the parameters, but the built-in function polr() in R fails. Would you like to tell me how to make some change so I can use polr() to obtain the estimators? Or anyone can give me a hint about the conditions for the existance of MLE

Hi, I am attempting to evaluate the prediction error of a coxph model that was built after feature selection with glmnet. In the preprocessing stage I used na.omit (dataset) to remove NAs. I reconstructed all my factor variables into binary variables with dummies (using model.matrix) I then used glmnet lasso to fit a cox model and select the best performing features. Then I fit a coxph model

I am trying to work out a bootstrapped Tobit regression model. I get the coefficients all right, but they all have standard error zero. And I am unable to figure out why. I know the coefficients are correct because that's what I get when do a Tobit (without bootstrapping). Here's my code: # Bootstrap 95% CI for Tobit regression coefficients? library(boot) library(AER) # for the Affairs

Dear R users, I'm trying to use function 'refit' from lme4 and I get this error that I can't understand: > refit(dolo4.model4,cbind(uu,50-uu)) Error in function (classes, fdef, mtable) : unable to find an inherited method for function "refit", for signature "mer", "matrix" if I try: > refit(dolo4.model4,uu) Error in asMethod(object) :

I am looking for help to analyze an unbalanced AB/BA cross-over design by requesting the type III SS ! # Example 3.1 from S. Senn (1993). Cross-over Trials in Clinical Research outcome<-c(310,310,370,410,250,380,330,270,260,300,390,210,350,365,370,310,380,290,260,90,385,400,410,320,340,220) subject<-as.factor(c(1,4,6,7,10,11,14,1,4,6,7,10,11,14,2,3,5,9,12,13,2,3,5,9,12,13))

Greetings I'm a relatively new R user and I'm trying to build a latent class model. I've used the 'R Site Search' and it appears there's not much dialogue on these packages On mmlcr, I've gotten it working, but not sure if I'm using it correctly. On flexmix, I can only seem to get results for one class. I'm attaching my code below - if anyone

Hi all, I'm trying to do a simple curve fit and coming up with some interesting results I would like to get comment on. So as shown below, tsR is my explanatory and response is... well... my response. This same data in gnumeric gets fitted with the curve "response=10078.4 + 1358.67 * ln (explanatory - 2009.07) So I'm using nls with the start values supplied by gnumeric. in

Dear All, Is there is a fast way of refitting lm() when the design matrix stays constant but the response is different? For example, y1 ~ X y2 ~ X y3 ~ X ...etc. where y1 is the 1st instance of the response vector. Calling lm() every time seems rather wasteful since the QR-decomposition of X needs to be calculated only once. It would be nice if qr() was called only once and then the same

Hi again! I have a question about R. I have done gam in previous version of R with "mgcv" package and saved the workspace. This workspace contains different models and I will do prediction by these GAMs. However, I install new version of R. and use the same workspace. when I type summary(models), and the error message showed Error in Predict.matrix.cr.smooth(object, dk$data) : F is

Hello. I have got two problems in bootstrapping from dependent data sets. Given two time-series x and y. Both consisting of n observations with x consisting of dependent and y consisting of independent observations over time. Also assume, that the optimal block-length l is given. To obtain my bootstrap sample, I have to draw pairwise, but there is the problem of dependence of the x-observations

I'm not sure I'm barking up the right tree here, but would I need to make use of groups to plot two separate datasets within ONE panel in xyplot? The desired end result is a single xy plot of two separate (but similar in values and ranges). Full code follows, xyplot code at bottom #########Determine Frequencies ##########coastal_slope #needs the maptools package to read ESRI grid

Dear R users, I am new to R. I would like to calculate bootstrap confidence intervals using the BCa method for a parameter of interest. My situation is this: I already have a set of 1000 bootstrap replicates created from my original data set. I have already calculated the statistic of interest for each bootstrap replicate, and have also calculated the mean for this statistic across all the

This issue has arisen within my anova.coxph routine, but is as easily illustrated with glm. testdata <- data.frame(y= 1:5, n= c(8,10,6,20,14), sex = c(0,1,0,1,1), age = c(30,20,35,25,40)) fit <- glm(cbind(y,n) ~ age + sex, binomial, data=testdata, model=TRUE) saveit <- fit$model update(fit, .~. - age, data=saveit)

Dear All: I updated my R program as well as associated packages yesterday. Currently my R version is 2.2.1 running under WINXP SP-2. When I tried to list (summary) an nlme object that I developed before, I got the following error message: [ Error in .C("ARMA_constCoef", as.integer(attr(object, "p")), as.integer(attr(object, : C entry point "ARMA_constCoef"

Hi R-helpers, Can some one tell me how to train 'mynn' of this type?: mynn <- nnet(y ~ x1 + ..+ x8, data = lgist, size = 2, rang = 0.1, decay = 5e-4, maxit = 200) I assume that this nn is untrained, and to train I have to split the original data into train:test data set, do leave-one-out refitting to refine the weights (please straighten this up if I was wrong). I just don't know

Barrett & Ling, JASA, 1992, v.87(417), pp184-191 define general classes of influence measures for multivariate regression models, including analogs of Cook's D, Andrews & Pregibon COVRATIO, etc. As in univariate response models, these are based on leverage and residuals based on omitting one (or more) observations at a time and refitting, although, in the univariate case, the

What??s happening with this following code: require(sem) Celpe.Mod.RAM <- matrix(c( # path parametro Inicio "Produ????o -> T1", "gamma.11", NA, "Produ????o -> T2", "gamma.12", NA,

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Hi, I'm not sure if this is the proper way to ask questions, sorry if not. But here's my problem: I'm trying to do a bootstrap estimate of the mean for some survival data. Is there a way to specifically call upon the rmean value, in order to store it in an object? I've used print(...,print.rmean=T) to print the summary of survfit, but I'm not sure how to access only rmean