search for: unbiasedly

Hi, Can anyone help me with the following. I have been using R for Monte Carlo simulations and got some results I couldn't explain. Therefor I performed following short test: -------------- mean.sds <- NULL sample.sizes <- 3:30 for(N in sample.sizes){ dum <- NULL for(I in 1:5000){ x <- rnorm(N,0,1) dum <- c(dum,sd(x)) } mean.sds<- c(mean.sds,mean(dum)) }

On Feb 2, 2014, at 6:18 PM, Andrew Trick <atrick at apple.com> wrote: >> The result of such a system would produce weights for every block in the above CFG as '1.0', or equivalent to the entry block weight. This to me is a really useful metric -- it indicates that no block in the CFG is really more or less likely than any other. Only *biases* in a specific direction would

I don?t know if my question is answerable, but it is worth a try. I have a data set that I am trying to analyze in R for a course and the instructions were to get a standard deviation which I already computed in R and use that number and change it to a biased standard deviation?.(I have the two equations and I understand the difference between the two and how the unbiased has the degree of

Hello, I have a vector of values with significant autocorrelation, and I want to calculate an unbiased standard deviation that adjusts for the autocorrelation. The formula linked below purports to provide what I want: https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation#Effect_of_autocorrelation_.28serial_correlation.29 However, rather than just implementing this equation in

I wonder whether the functions for skewness and kurtosis in the e1071 package are based on correct formulas. The functions in the package e1071 are: # -------------------------------------------- skewness <- function (x, na.rm = FALSE) { if (na.rm) x <- x[!is.na(x)] sum((x - mean(x))^3)/(length(x) * sd(x)^3) } # -------------------------------------------- and #

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. https://github.com/wch/r-source/blob/tags/R-3-5-1/src/main/RNG.c#L808 Many other languages use various rejection sampling approaches which provide an unbiased method for sampling, such as in Go, python, and others

Greetings, My question is more algorithmic than prectical. What I am trying to determine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity,

I need to calculate a large number of t statistics, and would like to do so via matrix operations. So far I have figured out a way to calculate the mean of each row of the matrix: d <- matrix(runif(100000,1,10), 1000, 10) # some test data s <- rep(1,ncol(d)) # a sum vector to use for matrix multiplication means <- (d%*%s)/ncol(d) This is at least 1 order of magnitude faster than

Hi all, I'm using nnet() for non-linear regression as in Ch8.10 of MASS. I understand that nnet() by default optimizes least squares. I'm looking to have it instead optimize such that the mean error is zero (so that it is unbiased). Any suggestions on how this might be achieved? Cheers, Mike -- Mike Lawrence http://artsweb.uwaterloo.ca/~m4lawren "The road to wisdom? Well,

Mark Leeds <mleeds at mlp.com> wrote (to S-News): > does anyone know the R equivalent of the SPlus rowVars function ? Andy Liaw <andy_liaw at merck.com> replied: > More seriously, I seem to recall David Brahms at one time had created an R > package with these dimensional summary statistics, using C code. (And I > pointed him to the `two-pass' algorithm for variance.)

In PR 22718, we are looking at issues with long running applications producing non-representative frequencies. For example, in these two loops: int g = 0; __attribute__((noinline)) void bar() { g++; } extern int printf(const char*, ...); int main() { int i, j, k; for (i = 0; i < 1000000; i++) bar(); printf ("g = %d\n", g); g = 0; for (i = 0; i < 500000; i++)

Hi people, If there is a fn "colMeans" why isn't there a "colSDs" etc Thanks, Phil. -- Philip Rhoades Pricom Pty Limited (ACN 003 252 275) GPO Box 3411 Sydney NSW 2001 Australia Mobile: +61:0411-185-652 Fax: +61:2:8923-5363 E-mail: pri at chu.com.au -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Right now, all profile information is funneled through two analysis passes prior to any part of the optimizer using it. First, we have BranchProbabilityInfo, which provides a simple interface to the simplest form of profile information: local and relative branch probabilities. These merely express the likelihood of taking one of a mutually exclusive set of exit paths from a basic block. They are

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. > >

On 9/20/18 1:43 AM, Carl Boettiger wrote: > For a well-tested C algorithm, based on my reading of Lemire, the unbiased > "algorithm 3" in https://arxiv.org/abs/1805.10941 is part already of the C > standard library in OpenBSD and macOS (as arc4random_uniform), and in the > GNU standard library. Lemire also provides C++ code in the appendix of his > piece for both this and

Is it possible to compute the variance of every column in a matrix without a loop? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch

I am using bagging to perform Bagged Regression Trees on count data (bird abundance in Britain and Ireland, in relation to climate and land cover variables). Predictions from the final model are visually believable but I would really like a diagnostic equivalent to classification success that can be used to decide if a model is adequate. Whereas with classification data an error rate is returned,

The 53 bits only encode at most 2^{32} possible values, because the source of the float is the output of a 32-bit PRNG (the obsolete version of MT). 53 bits isn't the relevant number here. The selection ratios can get close to 2. Computer scientists don't do it the way R does, for a reason. Regards, Philip On Wed, Sep 19, 2018 at 9:05 AM Duncan Murdoch <murdoch.duncan at

Dear list, I did simulations in which I generated 10000 independent Bernoulli(0.5)-sequences of length 100. I estimated p for each sequence and I also estimated the conditional probability that a one is followed by another one (which should be p as well). However, the second probability is significantly smaller than 0.5 (namely about 0.494, see below) and of course smaller than the direct

I am trying to install SrcStatConnectorSrv(2) and rcom from local zip files. I have successfully downloaded both files (from CRAN for rcom CRAN Other for SrcStatConnectorSrv) and installed rcom. However, I get the following error message when I try to install SrcStatConnectorSrv (the version dated 21-Aug-2006: > utils:::menuInstallLocal() Error in gzfile(file, "r") : unable to open