similar to: Estimation of S.E. based on bootstrapping (functions with two or more arguments)

Hi, I have used this command : resamples<-lapply(1:1000,function(i) sample(lambs,replace=F)) resamples2<-lapply(resamples,Cusum) to get a list of 1000 samples of my data. The function Cumsum is defined as follows: Cusum<-function(x){ SUM<-cumsum(x)-(1:length(x))*mean(x) min<-min(cumsum(x)-(1:length(x))*mean(x)) max<-max(cumsum(x)-(1:length(x))*mean(x)) diff<-max-min

Say I train a model in caret, e.g.: RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1) How can I save this to disk and load it later in R? How about an object of the class "resamples"? resamps <- resamples( list( RF = RFmodel, SVM = SVMmodel, KNN = KNNmodel, NN = NNmodel )) Thanks,

Dear List, I am using R to learn bootstrapping concepts. Not to be oblivious to the contributed packages of boot and bootstrap, I have opted to do the following as a way to hone my R skills. One example I am trying to work through is the following: x <- 1:20 Observed <- sample(x,20) Resamples <- replicate(1000,sample(Observed,replace=TRUE)) Boot.Means <- apply(Resamples,2,mean)

I am trying to get R to resample my dataset of two columns of age and length data for fish. I got it to work, but it is not resampling every replicate. Instead, it resamples my data once and then repeated it 5 times. Here is my dataset of 9 fish samples with an age and length for each one: Age Length 2 200 5 450 6 600 7 702 8 798 5 453 4 399 1 120 2 202 Here is my code which resamples my

Hello. I've got a dataset that may have outliers in both x and y. While I am not at all familiar with robust regression, it looked like the function lmrob in package robustbase should handle this situation. When I try to use it, I get: Too many singular resamples Aborting fast_s_w_mem() Looking into it further, it appears that for an indicator variable in one of my interaction terms, 98%

Hi, I'd like some advice on bootstrapping in R. I have a species x with 20 individuals and a factor containing 0 and 1's (in this case 5 zeros and 15 ones). I want to compare the frequency of the occurrence of 1 with a probability value. This code seems to work to do this in R. attach(test) p <- c(0.5272, (1-0.5272)) sp1_1 <- length(subset(x, x==1)) sp1_0 <- length(subset(x,

Hello All, My algorithm as follows; y <- c(1,1,1,0,0,1,0,1,0,0) x <- c(1,0,0,1,1,0,0,1,1,0) n <- length(x) t <- matrix(cbind(y,x), ncol=2) z = x+y for(j in 1:length(x)) { out <- vector("list", ) for(i in 1:10) { t.s <- t[sample(n,n,replace=T),] y.s <- t.s[,1] x.s <- t.s[,2] z.s <- y.s+x.s out[[i]] <- list(ff <- (z.s), finding=any (y.s==y[j])) kk

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

How many distinct values can rnorm return? I assume that rnorm manipulates runif in some way, runif uses the Mersenne Twister, which has a period of 2^19937 - 1. Given that runif returns a 64 bit precision floating point number in [0,1], the actual period of the Mersenne Twister in a finite precision world must be significantly less. One of the arguments for Monte Carlo over the bootstrap is

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

Hello, My question likely got buried so I am reposting it in the hopes that someone has an answer. I have thought more about the question and modified my question. I hope tha my specific question is: I am attempting to create a bootstrap procedure for a finite sample using the theory of Rao and Wu, JASA (1988) that replicates within each strata (h) n_h - 1 times. To this end, I require a

The outcome variable in my dataset is cost. I prefer not to transform it as readers will really be interested in pounds, not log(pounds) or sqrt(pounds). I have fitted my model using lme and now wish to use boot on it. I have therefore plagiarised the example in the article in RNews 2/3 December 2002 after loading the dataset I source this file

Hi all, I'm wanting to be able to find and store the z-score of coxph below: - modz=coxph(Surv(TSURV,STATUS)~RAGE+DAGE+REG_WTIME_M+CLD_ISCH+POLY_VS, data=kidneyT,method="breslow") I know summary(modz) will give me this, but how do i extract the standard error or z-score values in a similar way to obtaining the coefficients by coef(modz) ? I think it must be something to do with

Dear R Help list, My question is regarding extracting the standard error or Z-score from a cph or coxph call. My Cox model is: - modz=cph(Surv(TSURV,STATUS)~RAGE+DAGE+REG_WTIME_M+CLD_ISCH+POLY_VS, data=kidneyT,method="breslow", x=T, y=T) I've used names(modz) but can't see anything that will let me extract the Z scores for each coefficient or the standard errors in the same

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 >

I'm trying to: Resample with replacement pairs of distinct rows from a 120 x 65,000 matrix H of 0's and 1's. For each resampled pair sum the resulting 2 x 65,000 matrix by column: 0 1 0 1 ... + 0 0 1 1 ... _______ = 0 1 1 2 ... For each column accumulate the number of 0's, 1's and 2's over the resamples to obtain a 3 x 65,000 matrix G. For those

Hi dear all, i have a data (data.frame) which contain y and x coloumn(i.e. y x 1 0.58545723 0.15113102 2 0.02769361 -0.02172165 3 1.00927527 -1.80072610 4 0.56504053 -1.12236685 5 0.58332337 -1.24263981 6 -1.70257274 0.46238255 7 -0.88501561 0.89484429 8 1.14466282 0.34193875 9 0.58827457 0.15923694 10 -0.79532232 -1.44193770 ) i changed

Hi I'm using boot package for some analysis on linear regression coeficients. My problem is that I can not compute bca intervals, I get an error message > bca.ci(blm8901,index=1) Error in if (!all(rk > 1 & rk < R)) warning("Extreme Order Statistics used as Endpoints") : missing value where logical needed The problem is the empinf.reg function that is

Hello, I've started using R few months ago and I really like it. I need to estimate standard deviation of certain statistics (some measures of poverty). I found a really simple program, and I just need to check whether it's OK and really calculates what it's supposed to. Let's suppose e. g. head-count index (as one of the simplest measures of poverty) which is calculated as a

I'm implementing a custom bootstrap resampling procedure in R. This procedure resamples clusters of data points obtained by different subjects in an experiment. Since the bootstrap samples need to have the same size as the original dataset, `target.set.size`, I select speakers compute their data point contributions to make sure I have a set of the right size. set.seed(1)