similar to: bootstrap bca confidence intervals for large number of statistics in one model; library("boot")

I want to fit a series of lme() regression models that differ only in the degrees of freedom of a ns() spline. I want to use a wrapper function to do this. The models will be of the form y ~ ns(x, df=splineDF) where splineDF is passed as an argument to a wrapper function. This works fine if the regression function is lm(). But with lme(), I get an error. fitfunction() below demonstrates this.

Dear R user, I used the following to do a bootstrap. >bootObj<-boot(data=DAT, statistic=Lp.est, R=1000,x0=3) I have the following output from the above bootstrap. How can I extract  components of the output. For example, how can I extract the std.error? > bootObj   ORDINARY NONPARAMETRIC BOOTSTRAP   Call: boot(data = DAT, statistic = Lp.est, R = 1000, x0 = 3)   Bootstrap Statistics

Dear R-listers, It seems that predict() behaves differently within panel.xyplot. Am I doing something stupid? Thanks, Carlisle First, without xyplot(): > lmtest <- lm(t~s,data=subset(P100,whichLon100==1 & whichLat100==1)) > lmtest Call: lm(formula = s ~ t, data = subset(P100, whichLon100 == 1 & whichLat100 == 1)) Coefficients: (Intercept) t 33.3307

Often I perform the same task on a series of variables in a dataframe, by looping through a character vector that holds the names and using paste(), eval(), and parse() inside the loop. For instance: thesevars<-names(environmental) environmental$ToyOutcome<-rnorm(nrow(environmental)) tableOfResults<-data.frame(var=thesevars) tableOfResults$Beta<- NA

Dear all, Does someone know an R function implementing the method of Sison and Glaz (1995) (see full ref below) for computing Simultaneous confidence intervals for multinomial proportions? As alternative method, I think to boostrap the mean of each proportion and get in that way confidence interval of the mean. I observed 21 times a response that could be one out of 8 categories

There is now a tutorial on bootstrapping and other resampling methods at: http://www.burns-stat.com/pages/Tutor/bootstrap_resampling.html Corrections and other suggestions are welcome. The project started because a novice asked me about bootstrapping. My response was, "How dare you bug me while I'm playing with my cats, just google for it." My correspondent was not very impressed

Often it is useful to keep a "codebook" to document the contents of a dataset. (By "dataset" I mean a rectangular structure such as a dataframe.) The codebook has as many rows as the dataset has columns (variables, fields). The columns (fields) of the codebook may include: ? variable name ? type (character, factor, integer, etc) ? variable label

Hello everybody, when I create an object of class boot with the function tsboot() from the package boot and try to compute several types of confidence intervals with boot.ci("object of class boot created with tsboot") I obtain the warning message, that "BCa-intervals are not defined for time-series bootstraps". Does that hold in general? Or is it just not defined in

Dear R-Experts, Thanks to Prof. Bonnett, I have got an R script working to calculate confidence intervals around the semipartial correlation coefficients. Now, I would like to calculate BCa bootstrap CIs using the boot library and the boot.ci(results, type="all") function. How could I modify my R script (here below reproducible example) to get the BCa bootstrap CIs ? CIsemipartcorr

I'm using R 2.15.1 on a 64-bit machine with Windows 7 Home Premium and package 'boot'. I've found that using a number of bootstrap resamples in boot() that is less than the number of data results in a fatal error. Once the number of resamples meets or exceeds the number of data, the error disappears. Sample problem (screwy subscripted syntax is a relic of edited down a more

I'm using R 2.15.1 on a 64-bit machine with Windows 7 Home Premium. Sample problem (screwy subscripted syntax is a relic of edited down a more complex script): > N <- 25 > s <- rlnorm(N, 0, 1) > require("boot") Loading required package: boot > v <- NULL # hold sample variance estimates > i <- 1 > v[i] <- var(s) # get sample variance >

Hi everyone, I use the boot package within the MBESS package to automate much of the hard part for folks interested in performing a (simple) mediation model. The function mediation() works well, except for a (probably) unrealistic artificial data set. When I apply the bootstrap I sometimes get: Error in t.star[r, ] <- res[[r]] which is (I think) due to Inf and -Inf and NaN values produced for

Dear All, I am trying to use the bca.ci function on a boot.out object which consists a few NaN's and I want to ignore those NaN's, and get a ci only for the "normal" values. boot.out$t has R number of values for 3000 different statistics, so when I use boot.ci(boot.out, index=i) and i happens to be a column in boot.out$t with some NaN's in there I get an error message. I

i was searching for the source of bca.ci, a function of the package boot. I tried require(boot, keep.source=TRUE) but again the source was not viewable. How should i do? Best regards Meinhard Ploner ---------------- PS > version _ platform i386-apple-darwin8.8.1 arch i386 os darwin8.8.1 system i386, darwin8.8.1 status major 2

Dear List, Couple of issues while using functions from ?BCA? library: 1. I am trying to use ?lift.chart? function from ?BCA? library, but facing issues while using model where model formula is passed as formula object in glm. When model formula is written as text, then it works fine. In my case input variables and target variables are going to change dynamically, so have to used formula as

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

I've got a table of 1 row per observation. 16 questions * n subjects * 4 classrooms (2 teachers * 2 conditions) I'd like to run some simple regressions that look something like this: lm(length ~ elapsed) I'd like to do the regressions several times, split out by questionkey, student, class. What I've been doing is using a series of which's to create new data sets (e.g.