[R] Re moving rows which do not satisfy a condition

Hello, Considering 5 points in X-Y plain. Data is a 5*2 matrix (5 rows for samples 2 columns for X and Y) With a distance from the origin, if a distance 7, remove the row from the Data. After calculating the distance for each point, I can't forward because of this Removing problem. Anyone

[R] re ading transposed data in read.csv

I have a data set (csv); e.g., ID samp1 samp2 samp3 samp4 G1 2332 12 87 G2 8545 49 76 G3 1246 39 28 G4 7326 18 13 and read it: data1-(read.csv(Datafolder/rawdata.csv,header=T)) It is fine with

[R] how to compute other columns without a column for sample name

Data has the first row for variable name and the first column for sample name. I want to take Log for all data, but how to compute without the first column for sample name. log.raw_data=log(raw_data,base=2) Error in Math.data.frame(list(sample_id = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, :

Re: [R] IWLS vs direct ML estimation

the numerical solution. Do you have any idea for this? Thank you, Jinsuk Lee Research Associate Dept. of Industrial Engineering, Arizona State University, USA office: (480)-965-8468 [EMAIL PROTECTED] Mike Prager wrote: sandsky [EMAIL PROTECTED] wrote: I am thinking about IWLS vs ML estimation

[R] IWLS vs direct ML estimation

Hi, I am thinking about IWLS vs ML estimation. When I use glm() for a 2-parameter distribution (e.g., Weibull), I can otain the MLE of scale parameter given shape parameter through IWLS. Because this scale parameter usually converges to the MLE. In this point, I am wondering: i) can you say

Re: [R] count data with some conditions

) #adding 0 to TRUE or FALSE creates 1 or 0. [1] 4 -- David Winsemius, MD Heritage Labs. On Oct 31, 2008, at 7:56 PM, sandsky wrote: Hi there, I have a data set: a=cbind(5,2,4,7,8,3,4,11,1,20) I want to count # of data, satistfying a[1]a[2:10]. Anyone helps me solving this case

[R] function for standard normal cumulative distribution

Is there anyone knowing a function for standard normal cumulative distribution? Φ(z=-0.1)=? also Φ(z=?)=0.025 -- View this message in context: http://www.nabble.com/function-for-standard-normal-cumulative-distribution-tp20282804p20282804.html Sent from the R help mailing list archive at

[R] count data with some conditions

Hi there, I have a data set: a=cbind(5,2,4,7,8,3,4,11,1,20) I want to count # of data, satistfying a[1]a[2:10]. Anyone helps me solving this case? Thank you in advance, Jin -- View this message in context: http://www.nabble.com/count-data-with-some-conditions-tp20275722p20275722.html

Re: [R] count data with a specific range

] (20,30] 2 4 4 On Mon, Sep 29, 2008 at 5:41 PM, sandsky [EMAIL PROTECTED] wrote: Hi there, The data is data-c(2,6,13,26,19,25,18,11,22,25) I want to count data for these rages: [0~10]: [11~20]: [21-30]: Is anyone can help me? Thank you in advance -- View

[R] count data with a specific range

Hi there, The data is data-c(2,6,13,26,19,25,18,11,22,25) I want to count data for these rages: [0~10]: [11~20]: [21-30]: Is anyone can help me? Thank you in advance -- View this message in context: http://www.nabble.com/count-data-with-a-specific-range-tp19732290p19732290.html Sent from

[R] Error: function cannot be evaluated at initial parameters

I have an error for a simple optimization problem. Is there anyone knowing about this error? lambda1=-9 lambda2=-6 L-function(a){ s2i2f-(exp(-lambda1*(250^a)-lambda2*(275^a-250^a)) -exp(-lambda1*(250^a)-lambda2*(300^a-250^a))) logl-log(s2i2f) return(-logl)} optim(1,L) Error in optim(1,

[R] Error in [-(`*tmp*`, i, value = numeric(0)) :

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

[R] Loop for the convergence of shape parameter

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,

[R] Genmod in SAS vs. glm in R

Hello, I have different results from these two softwares for a simple binomial GLM problem. From Genmod in SAS: LogLikelihood=-4.75, coeff(intercept)=-3.59, coeff(x)=0.95 From glm in R: LogLikelihood=-0.94, coeff(intercept)=-3.99, coeff(x)=1.36 Is there anyone tell me what I did wrong? Here