similar to: bias of a boot statistic

All - I have an uneven set of replicates and would like to sample from this set X number of times to generate a mean for each grouping variable. I was thinking the boot package would be the thing to use, but now I'm not so sure ... given the discussion here: http://finzi.psych.upenn.edu/Rhelp10/2010-June/243828.html Given these data (a small subset): > dput(x) structure(list(numSpp

This probably has a super easy answer...but I claim newbie status! (I did search help lists but this question is hard to isolate keyword-wise) Basically, I'm trying to figure out how to parse the results from executing boot(). I'm mainly interested in assigning the standard error estimate to a scalar variable. For example: --------------------------------------------------------------

Hello, I am trying to carry out a bootstrap analysis (using the boot package) on a table and cannot work out how to get the results I need! I have a table ("d2") with 4 columns: "ID_code", "Age", "Quarter" and "StomWt". Age (0-5) and Quarter (1-4) are my strata Therefore I wish to estimate the confidence intervals for the mean StomWt for each Age

What in the world am I missing?? > x<-rnorm(20) > mean(x) [1] -0.2272851 > results<-boot(x,mean,R=5) > results[2] $t [,1] [1,] -0.2294562 [2,] -0.2294562 [3,] -0.2294562 [4,] -0.2294562 [5,] -0.2294562 Jeff Morris Ortho-Clinical Diagnostics A Johnson & Johnson Co. Rochester, NY Tel: (585) 453-5794 [[alternative HTML version deleted]]

Hi, suppose I have a matrix (or dataframe) as a result from subsetting. mat <- matrix(1:20,ncol=2) mat[c(3,6,9),] <- NA cc <- complete.cases(mat) sub <- mat[cc,,drop=FALSE] sub <- sub * 2 #some caluculations with sub. now I would like to expand sub somehow so row 3,6, and 9 would be filled with NAs but the rest should be in place again. Is there a simple function for this?

Hi, This is probably really simple, but I am clearly not R-minded, I have read the help files, and reread them, and I still can't work out what to do... I have a data frame (d) with 3 columns (age (0-5), quarter (1-4) and x). I want to estimate the precision of my mean x by age and quarter, so I want to carry out a bootstrap for each group. I am trying to do this within a loop, so I don't

R 2.3.1 Windows XP Question: How can I obtain the values of BIAS and STD. ERROR from a bootstrap. Background: I am running a bootstrap: result2<-boot(1:400,regSEvssample,R=5000) and obtain the following results: Bootstrap Statistics : original bias std. error t1* 1.876602 -0.0001368616 0.1630380 I would like to get the values of ORIGINAL and BIAS. I can get the value of

Dear R Users, My question is overall and not necessarily related to R. Suppose we face to a situation in which MSE( Mean Squared Error) shows desired results but Bias shows undesired ones, Or in advers. How can we evaluate the results. And suppose, Both MSE and Bias are important for us. The ecact question is that, whether there is any combined measure of two above metrics. Thank you so

I am doing boot with a large database, thus want to use?simple=TRUE to reduce the memory used. I alreday set up sim="ordinary", stype="i" , but I don't know how to set "n=0". In fact, I don't know what does "n=0" mean? For example, a<-c(1:1000) b<-c(2:1001) c<-cbind(a,b) library(boot) table<-function(c,indices){ d<-c[indices,]

Hey there, I was wondering if someone could tell me if there's a package or command that allows me to compute a GINI coefficient using a vector of weights. Also the coefficient should be bias corrected. Diego Rojas [[alternative HTML version deleted]]

Hi folks, I'm trying to estimate bias-corrected percentile (BCP) confidence intervals on a vector from a simple for loop used for resampling. I am attempting to follow steps in Manly, B. 1998. Randomization, bootstrap and monte carlo methods in biology. 2nd edition., p. 48. PDF of the approach/steps should be available here: https://wyocoopunit.box.com/s/9vm4vgmbx5h7um809bvg6u7wr392v6i9 If

El mi?., 19 sept. 2018 a las 14:43, Duncan Murdoch (<murdoch.duncan at gmail.com>) escribi?: > > On 18/09/2018 5:46 PM, Carl Boettiger wrote: > > Dear list, > > > > It looks to me that R samples random integers using an intuitive but biased > > algorithm by going from a random number on [0,1) from the PRNG to a random > > integer, e.g. > >

Hello again I have a file from SPSS in .sav format. when I run library(foreign) cvar<-as.data.frame(read.spss("c:\\NDRI\\cvar\\data\\cvar2rev3.sav")) I get a warning Warning message: c:\NDRI\cvar\data\cvar2rev3.sav: Compression bias (0) is not the usual value of 100. The data appear to be OK, but I am concerned. (I tried searching the archives and the documenation for data

> From: Duncan Murdoch <murdoch.duncan at gmail.com> > Let's try it: > > > m <- (2/5)*2^32 > > m > 2^31 > [1] FALSE > > x <- sample(m, 1000000, replace = TRUE) > > table(x %% 2) > > 0 1 > 399850 600150 > > Since m is an even number, the true proportions of evens and odds should > be exactly 0.5.

Dear R-Help List, I'm trying to implement Firth's (1993) bias correction for log-linear models. Firth (1993) states that such a correction can be implemented by supplementing the data with a function of h_i, the diagonals from the hat matrix, but doesn't provide further details. I can see that for a saturated log-linear model, h_i=1 for all i, hence one just adds 1/2 to each count,

On Thu, Nov 14, 2013 at 01:48:17AM -0800, edwintorok wrote: > Following the link to builder.ml from your blogpost I noticed the make_random_password () function, and I have some suggestions, well nitpicks really. See the 2 commits from this pull request. > > 1. Using Ocaml&#39;s buffered I/O means that one make_random_password() call reads 64k bytes from /dev/urandom which drops the

Hi, I'm trying to fit a model which has a response bias, but also a day to day bias. If I try to simulate the data, I don't get the right values with optim, and also I can't use the function to give a prediction interval. My simulated data are: DF <- as.data.frame(cbind(x=rep(1:10,2),dag=rep(1:2,each=10))) bias <- c(-0.2,0.5) DF$y <- ((DF$x-0.1) * 5)+2 +

I have to the treatment effect base on the observational data.And the treatment variable is multinomial rather than binary.Because the treatment assignment is not random,so the selection-bias exists.Under this condition,what's the best way to estimate the treatment effect? I know that if the treatment is binary,I can use propensity score matching using MatchIt package.But what about

Dear R users: In Prof. Harrell's library rms, calibrate.rms plot the Bias-corrected Probability and Apparent Probability. The latter one can be retrieved from class calibrate.default. But how to retrieve the former one. BW *Yao Zhu* *Department of Urology Fudan University Shanghai Cancer Center Shanghai, China* [[alternative HTML version deleted]]

hi i want to analyze Right Censore-Length bias data under cox model with covariate. what is the package ? tank you. [[alternative HTML version deleted]]