similar to: Using sample() to sample one value from a single value?

Hello; Our survey is structured as : To be investigated area is divided into 6 regions, within each region, one urban community and one rural community are randomly selected, then samples are randomly drawn from each selected uran and rural community. The problems is that in urban/rural stratum, we only have one sample. In this case, how to do bootstrap? Any comments or hints are greatly

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

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]]

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 was wondering if there is a permutation test available in R for linear models with continuous dependent covariates. I want to do a test like the one shown here. bmi<-rnorm(100,25) x<-c(rep(0,75),rep(1,25)) y<-rnorm(100)+bmi^(1/2)+rnorm(100,2)*x+bmi*x H0<-lm(y~1+x+bmi) H1<-lm(y~1+x+bmi+x*bmi) anova(H0,H1) summary(lm(y~1+x+bmi)) But I want to use permutation testing to

Dear R experts, I have defined a class "myclass" and would like the slots to be extractable not only by "@" but also by "$". I now try to write a method for "$" that simply executes the request object at slotname, whenever someone calls object$slotname for any object of class "myclass". I don't manage to find out how I can provide this

I've been playing with passing arguments to .C(), and found that replacing as.double(x) with if(is.double(x)) x else as.double(x) saves time and avoids one copy, in the case that x is already double. I suggest modifying as.double to avoid the extra copy and just return x, when x is already double. Similarly for as.integer, etc. [[alternative HTML version deleted]]

I did some benchmarking of data frame code, and it appears that R 3.0.0 is far worse than earlier versions of R in terms of how many large objects it allocates space for, for data frame operations - creation, subscripting, subscript replacement. For a data frame with n rows, it makes either 2 or 4 extra copies of all of: 8n bytes (e.g. double precision) 24n bytes 32n bytes

Dear experts, I am asking for help with a question regarding to stratified bootstrap. My dataset is a longitudinal dataset (3 measurements per person at year 1, 4 and 7) composed of multiple clinic centers and multiple participants within each clinic. It has missing values. I want to do a bootstrap to find the standard errors and confidence intervals for my variance components. My model is a

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

Sometimes one might like to obtain pointwise bootstrap bias-corrected, accelerated (BCA) confidence intervals for a large number of statistics computed from a single dataset. For instance, one might like to get (so as to plot graphically) bootstrap confidence bands for the fitted values in a regression model. (Example: Chiu S et al., Early Acceleration of Head Circumference in Children with

If my sample size is small is there a particular switch option that I need to use with t.test so that it calculates the t ratio correctly? Here is a dummy example? á =0.05 Mean pain reduction for A =27; B =31 and SD are SDA=9 SDB=12 drgA.p<-rnorm(5,27,9); drgB.p<-rnorm(5,31,12) t.test(drgA.p,drgB.p) # what do I need to give as additional parameter here? I can do it manually but

Hello all, I am trying to run a two sample t-test on a matrix which is a 196002*22 matrix. I want to run the t-test, row-wise, with the first 11 columns being a part of the first group and columns 12-22 being a part of the second group. I tried running something like (temp.matrix being my 196002*22 matrix) t.test(temp.matrix[,1:11],temp.matrix[,12:22],paired=TRUE) or somthing like

hello everyone. please bear with me if this is very easy... I have a data set with many trials, and frames within each trial. I would like to pull out one random frame from each trial. here is an example. i have 4 unique trials (file), and various frames within each (time_pred). I would like to randomly sample 4 rows, but 1 from each trial (file). this sample data is called "h"

Hi there, I want to generate different samples using the followindg code: g<-sample(LETTERS[1:2], 24, replace=T) How can I specify that I need 12 "A"s and 12 "B"s? Thank you, Judith ____________________________________________________________________________________ Be a better friend, newshound, and

Hello, I learnt there's an advanced R/Splus course in Boston this january. Anyone got the announcement? please kindly forward it to me. Best, Eugene

PATCH 1 ======= On my machine tc does not parse filter "sample" for the u32 filter. Eg: tc filter add dev eth2 parent 1:0 protocol ip prio 1 u32 ht 801: \ classid 1:3 \ sample ip protocol 1 0xff match ip protocol 1 0xff Illegal "sample" The reason is a missing memset. This patch fixes it. diff -Nur iproute-20051007.keep/tc/f_u32.c iproute-20051007/tc/f_u32.c

I apologize for this post. I am new to R (two days) and I have tried and tried to calculated confidence intervals for medians. Can someone help me? Here is my data: institution1 0.21 0.16 0.32 0.69 1.15 0.9 0.87 0.87 0.73 The first four observations compose group 1 and observations 5 through 9 compose group 2. I would like to create a bootstrapped 90% confidence interval on the difference of

Below is a version of [.data.frame that is faster for subscripting rows of large data frames; it avoids calling duplicated(rows) if there is no need to check for duplicate row names, when: i is logical attr(x, "dup.row.names") is not NULL (S+ compatibility) i is numeric and negative i is strictly increasing "[.data.frame" <- function (x, i, j,

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