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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 that the direct MLE, which is obtained by maximizing a likelihood function, is equalvant to the indirect

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 the R help mailing list archive at Nabble.com.

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. That is, column 1: sample ID column 2-10: data I want to find an average on each column (2-10) > apply(raw_data,2,mean) Error in Math.data.frame(list(sample_id = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, :

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

...nchester.ac.uk> Fax-to-email: +44 (0)870 094 0861 Date: 29-Sep-08 Time: 22:33:33 ------------------------------ XFMail ------------------------------ ------------------------------ Message: 76 Date: Mon, 29 Sep 2008 14:41:44 -0700 (PDT) From: sandsky <realstone at hotmail.com> Subject: [R] count data with a specific range To: r-help at r-project.org Message-ID: <19732290.post at talk.nabble.com> Content-Type: text/plain; charset=us-ascii 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...

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 can help me? Data=matrix(1:10,5,2) a=rep(0,5); b=rep(0,5); for (i in

Hi, I have a vector with 4 elements, e.g., tau_i=c(100,200,300,400), but potentially tau_i[0]=0. In a "for" loop, tau_i=c(100,200,300,400) m=4 tau_i[0]=0 # <------- ? P_i=1 for(i in 2:m) { P_i = P_i*(tau_i[i-1]-tau_i[i-2]) } Error in P_i = P_i * (tau_i[k - 1] - tau_i[k - 2]): replacement has length zero Unfortunately, I can add this potential element into

Hello Dear, I am trying to generate samples by using Monte Carlo simulation. For example, 1000 samples, Exponential distribution (f(x), lambda=0.0005, 0<=x<=360) Is there any package for Monte Carlo or just use random sample generation function? Many thank you for your help in advance, Jin -- View this message in context:

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

Hello Dear, I have a function, like z=f(x,y), and try a surface plot with this function. But, on the reference of "wireframe" requires data option, so I generated x and y, and computed z with them. But, still I have a problem to draw a surface plot. The code and errors are ################################################## mle_beta0=64.43707; mle_beta1=-24365.16; # generating for

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 are the code and results, 1) SAS Genmod: % r: # of failure % k: size of a risk set data

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

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, L) : function cannot be evaluated at initial parameters Thank you in advance -- View this