similar to: IWLS vs direct ML estimation

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.

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.

What kind of special magic does glm have? I'm working on a logistic regression solver (L-BFGS) in c and I've been using glm to check my results. I came across a data set that has a very high condition number (the data matrix transpose the data matrix) that when running my solver does not converge, but the same data set with glm was converging ( I love R :) ). I noticed that glm using

Dear R users, I use the glm() function to fit a generalized linear model with gamma distribution function and log link. I have read in the help page that the default method used by R is "glm.fit" (iteratively reweighted least squares, IWLS). Is it possible to use maximum likelihood method? Thanks Silvia Narduzzi Dipartimento di Epidemiologia ASL RM E Via di S. Costanza, 53 00198

Hi, Does anyone know if there is a function to find the maximum likelihood estimates of glm using Newton Raphson metodology instead of using IWLS. Thanks Valeska Andreozzi -------------------------------------------------------- Department of Epidemiology and Quantitative Methods FIOCRUZ - National School of Public Health Tel: (55) 21 2598 2872 Rio de Janeiro - Brazil

Dear R-Help List, I'm trying to implement Firth's (1993) bias correction for log-linear models. Firth (1993) states that such a correction can be implemented by supplementing the data with a function of h_i, the diagonals from the hat matrix, but doesn't provide further details. I can see that for a saturated log-linear model, h_i=1 for all i, hence one just adds 1/2 to each count,

Full_Name: saraux Version: 2.6.1 OS: Windows vista Submission from: (NULL) (193.157.180.37) I would like to compute a glm with a distribution of poisson, using a maximum of likelihood method. But it seems not to work with a distribution of poisson. The same code with another distrubution (binomial for example) works. Here is the command I typed:

Full_Name: Carmen Fernandez Version: 1.3.1 OS: Submission from: (NULL) (138.251.202.115) I think there is a problem when computing Pearson residuals, in that they seem to be computed at the raw residuals divided by the square root of the corresponding diagonal element of the weight matrix W evaluated at the last step of the iterative model fitting procedure (IWLS), instead of dividing by the

I have a file with 4 columns per line, all pipe delimited. $ wc -l cmie_firm_data.text 89325 cmie_firm_data.text $ ls -al cmie_firm_data.text -rw-r--r-- 1 ajayshah ajayshah 4415637 Jul 5 15:25 cmie_firm_data.text $ awk -F\| '(NF != 4)' cmie_firm_data.text $ head cmie_firm_data.text All figures are for the year 20030331||| Company|GVA Less Interest (Rs. thousand)|Interest (Rs.

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

Dear all, I would like to know if I can compare by a significance test 2 models with different kind of parameters. Perhaps I am wrong but I think that we can only compare 2 models if one is a sub model of the other. Med venlig hilsen / Regards João Fadista Ph.d. studerende / Ph.d. student AARHUS UNIVERSITET / UNIVERSITY OF AARHUS Det Jordbrugsvidenskabelige Fakultet / Faculty of

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

Good Morning ??? I have a small question regarding the function subset. I am copying data from one table but I just want to collect data from a user. When do I take the view, presents the results I want. The problem arises when can I make the tab. for RES_ID, introduces me to zero results do not envision noVIEW val_user='16' x.sub <-

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

jlindsey at alpha.luc.ac.be writes: > The following is a bug or feature in recent versions. Try the > following with a clean workspace: > > y <- cbind(rpois(20,4),rpois(20,4)) > df <- data.frame(y=y,x=rnorm(20)) > colnames(df) > colnames(model.frame(y~x,data=df)) > > Now start over with a clean workspace and try > > df <-

Hi, is there a possibility to calculate the different "types" of residuals directly using the multinom function from MASS as it is possible for the functions gam, glm using type="deviance" or "working" or "pearson" or "response"? I tried it but got always the "response" type, I guess. thanx Matthias

can anyone tell me how can i control the method of estimation (i.e. scoring method or Newton raphson method) in glm and compute deviance function ? -- View this message in context: http://r.789695.n4.nabble.com/changing-method-of-estimation-in-GLM-tp3170836p3170836.html Sent from the R help mailing list archive at Nabble.com.

Does anyone know what algorithm R uses in glm, family=binomial (i.e. a logit model)? I assume that it's in the source somewhere, but I wasn't able to find it. I'd like to know what file it's in (in a unix distribution of R). Thanks for your help. --------------------------- Barnet Wagman wagman at enteract.com 1361 N. Hoyne, 2nd floor Chicago, IL 60622 773-645-8369