search for: biasness

Hello all, After more digging I was able to find out how to do this. The answer came from an example here: https://stackoverflow.com/questions/3541713/how-to-plot-two-histograms-together-in-r yr_1997<-data.frame(bias=ann_bias$bias1997) yr_1998<-data.frame(bias=ann_bias$bias1998) yr_1999<-data.frame(bias=ann_bias$bias1999) yr_2000<-data.frame(bias=ann_bias$bias2000)

Hi all, I can not seem to get what I want using the Lattice package to generate an array of histograms of spatialgrid dataframe data. I can use the sp package and spplot to generate an array of maps that display an array of spatialgrid dataframe data -- that's good. I have:

Glad you found an answer, though it looks more self-educational than efficient (see suggestions below). In the future, follow the recommendations of the Posting Guide: use plain text, and provide a reproducible example. Some elaborations on what "reproducible" means are [1][2][3]. One issue here was that you did not include sample data to work with (I have assumed below that ann_bias has

Question: How can I get access to the bias value of a boot statistic? Details: Boot function: boot(data, statistic, R, sim="ordinary", stype="i", strata=rep(1,n), L=NULL, m=0, weights=NULL, ran.gen=function(d, p) d, mle=NULL, ...) When I create an object, containing the bootstrap statistic (object <- boot (....))I can call it and will get an output

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

Dear R users, I want to calculate the bias and variance of an estimator for several values of two parameters a and b. For example : b1 b2 a1 bias bias variance variance a2 bias bias variance variance Can one do array of arrays ? I have tried and it did not work. Actually I would like to get this array for several values of the number of observations. That

I have constructed the following functions and need a little clarification: ### function to fit the model parameters jml.fit <- function(dat, con = 1e-3, bias=FALSE, ...){ do stuff ... } ### default function which calls jml.fit jml.default <- function(dat, con = 1e-3, bias=FALSE, ...){ result <- jml.fit(dat, con = 1e-3, bias) result$call <- match.call() class(result) <-

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 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

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 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

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 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. > 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

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 +

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]]

On 19/09/2018 9:09 AM, I?aki Ucar wrote: > 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)

Greetings, I would like to fit a multiple linear regression model in which the residuals are expected to follow a multivariate normal distribution, using weighted least squares. I know that the data in question have biases that would result in correlated residuals, and I have a means for quantifying those biases as a covariance matrix. I cannot, unfortunately, correct the data for these biases.

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

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

> 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.