search for: sandsky

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, :

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.

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

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.

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

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

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

...rsistence of model in R to a file (Sharma, Dhruv) 71. quantile (liujb) 72. Re: replicating dataframe rows (David Huffer) 73. Re: quantile (jim holtman) 74. box-whisker plot from pre-summarized data? (Avram Aelony) 75. Re: quantile ( (Ted Harding)) 76. count data with a specific range (sandsky) 77. Re: adding labels to tapply results (Dieter Menne) 78. Re: Hazard plot (Dieter Menne) 79. Re: Double integration - Gauss Quadrature (Earl F. Glynn) 80. Re: Double integration - Gauss Quadrature (Earl F. Glynn) 81. Re: count data with a specific range (Peter Alspach) 82. Re: box-whi...

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