similar to: a basic question about standardization?

what command to use to standardize my data to mean = 0, and SD=1. the data I want to standardize/centroid is in column format. So I'm thinking of using: col.mean.stdised=apply(data.df,2,function(z){z-mean(z)}) but, what command should I use to put my SD =1? Thanks! -- View this message in context: http://r.789695.n4.nabble.com/what-command-to-use-for-standization-tp2236074p2236074.html

Dear all, So far i could do (in an informal way) to draw a Standardized Resisual plot in the following way- --------------------- >x <- c(104.1, 106.6, 105.5, 107.5, 109.6, 113.3, 115.5, 117.7, 119.9, 122.1, 124.3, 126.5, 128.2) >y <- c(53732, 52912, 57005, 61354, 67682, 71602, 71961, 75309, 82931, 93310, 102161, 103068, 108927) >

Greetings, Experimenting with the cluster package, and am starting to scratch my head in regards to the *best* way to standardize my data. Both functions can pre-standardize columns in a dataframe. according to the manual: Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the variable's mean absolute deviation. This

To whom it may concern: I am trying to better understand the functionality of 'R' when making arima predictions to avoid any "Black Box" disadvantages. I'm fitting a seasonal arima model using the following command (having already loaded 'stat' package). arimaSeason <- arima(Data,order=c(1,0,1),seasonal=list(order=c(1,0,1),period=12)) I can then generate

Hi, I try to understand how the generalized hyperbolic distribution is standardized. One reference is the rugarch vignette, page 16-18: http://cran.r-project.org/web/packages/rugarch/vignettes/Introduction_to_the_rugarch_package.pdf I looked at the code of the dsgh function in the fBasics package: > dsgh function (x, zeta = 1, rho = 0, lambda = 1, log = FALSE) { if (length(zeta) == 3) {

Dear all, We are excited to announce that the our CELLAR working group charter for the standardization of FFV1, Matroska, and FLAC has been made available for external review. What this means is that it is now under review and available for comment to the wider IETF community. Please send any comments you might have(including statement of support)to the IESG mailing list by November 9, 2015.

Dear members of the flac-dev list, I'm writing to ask for feedback from the FLAC community regarding the possibility of standardizing the FLAC specification through the Internet Engineering Task Force. I'm working with MediaArea on the PREFORMA project which focuses on building conformance checkers for Matroska and FFV1. Since Matroska and FFV1 are not yet formally standardized, this

Hello, The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0 and beta1 via GLM) 2) with lambda, estimate alpha via ML estimation 3) with updataed alpha, replicate 1) and 2) until alpha is converged to a value I coded 1) and 2) (it works), but faced some

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} (

Dear All I'm attempting to speed up my model building procedure, but need some help with the loops I've created...please bear with me through the explanation! My basic model call is something like: m0sulf.nlme<-nlme(conc~beta0*exp(-beta1*day)+beta2*exp(-beta3*day), data=m0sulf, fixed=(beta0+beta1+beta2+beta3~1),

Hi all, I am trying to create simulated data for exploring reclassfication measures in a logistic setting with two continuous predictors and I would like to set the average population probability of outcome rather than the logistic beta0. Is there a way to find a beta0 that will generate the desired average population probability of outcome given x,y and their odds ratios? #Here is an

SAS Enterprise Miner recommendeds to standardize using X / STDEV(X) versus [X ? mean(X)] / STDEV(X) Any thoughts on this? Pros Cons Philip

I use "while" loop but it produces an errro. I have no idea about this. Error in "[<-"(`*tmp*`, i, value = numeric(0)) : nothing to replace with The problem description is The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0

Dear all,I am a new user of WinBUGS and need your help. After running the following code, I got parameters of beta0 through beta4 (stats, density), but I don't know how to get the prediction of the last value of h, the variable I set to NA and want to model it using the following code.Does anyone can given me a hint? Any advice would be greatly appreciated.Best

Hi all-- We are planning an intervention study for adolescent alcohol use, and I am planning to use simulations based on a hurdle model (using the hurdle() function in package pscl) for sample size estimation. The simulation code and power code are below -- note that at the moment the "power" code is just returning the coefficients, as something isn't working quite right. The

Background: I administer a cluster of RedHat EWS 3U4 Linux workstations at a university. I built R 2.1.0 from source: ./configure \ --prefix=/sscc/opt/R-2.1.0 \ --with-blas=no \ 2>&1 \ | tee NUInstall.configure R is now configured for i686-pc-linux-gnu Source directory: . Installation directory: /sscc/opt/R-2.1.0 C compiler:

Dear Users, I am new to both of things, so do not blame me too much... I am busy with semiparametric regression and use WinBUGS to sample posteriors. The code to call Winbugs is as follows: data <- list("y","X","n","m") #My variables inits.beta <- rep(0,K) inits.beta0 <- 0 inits <-

Dear list, After running for a while, it crashes and gives the following error message: can anybody suggest how to deal with this? Error in if (ratio0[i] < log(runif(1))) { : missing value where TRUE/FALSE needed ################### original program ######## p2 <- function (Nsim=1000){ x<- c(0.301,0,-0.301,-0.602,-0.903,-1.208, -1.309,-1.807,-2.108,-2.71) # logdose

Dear all, I am trying to evaluate the optimisation behaviour of a function. Originally I have optimised a model with real data and got a set of parameters. Now I am creating simulated data sets based on these estimates. With these simulations I am estimating the parameters again to see how variable the estimation is. To this end I have written a loop which should generate a new simulated data

Respected Sir, With reference to my mail to you and the reply mail by you dated 9th and 16th January, 2013, I am sending the reproducible code in the attached document named " MODIFIED ANS ". I am also attaching the txt file named "hazModel", which is required to save in my documents folder to run the program. The file also contains the error message