similar to: Survivors and bugs ...

Hi all I try to make a weibull survival analysis on R. I know make this on GLIM, and now I try to make the GLIM exercice GLEX8 on R to learning and compare the test. The variables are: time censor group bodymass In GLIM I make: $calc %s=1 $ to fit weibull rather than exponential $input %pcl weibull $ $macro model group*bodymass $endmac$ $use weibull t w %s $ Then, GLIM estimate an alpha for the

Hi , I am trying to get some estimator based on lognormal distribution when we have left,interval, and right censored data. Since, there is now avalible pakage in R can help me in this, I had to write my own code using Newton Raphson method which requires first and second derivative of log likelihood but my problem after runing the code is the estimators were too high. with this email ,I provide

Dear R users, This is a basic question. I want to fit a Weibull distribution. fitdistr(data, "weibull") works and it is a maximum likelihood fitting. Is it a good method ? Or is it better to write a function for the log-likelihood and the gradient and to use a numerical routine ? Fitdistr works for uncensored data, but what can I use for censored (and uncensored) data ? Thank you

Un bon mot s'il vous plait. I have coded an R routine to decompose messy data following Murray Aitkin and Brian Francis's NPMLE GLIM macros for the normal distribution only but extended to incorporate censoring and variance heterogeneity. Essentially it is a wrapper for nlm() in the M-part while the E-part re-estimates the weights in the same way as the GLIM macros. The big problem is

I've successfully reformatted data frames from long to wide with reshape2, but this time I'm getting errors that I want to understand and resolve. Here's the data frame structure and the results of the melt() and dcast() functions: str(waterchem) 'data.frame': 128412 obs. of 8 variables: $ site : Factor w/ 64 levels "D-1","D-2","D-3",..: 1 1

Hi Starting to use R as a serious tool, I have come across a programming problem that I can't see the answer too yet. Can someone advise me plese. The problem is that I want to plot a series of lines which represent short term growths. All the data is in a single vector and I can indicate the index via a second vector. In GLIM, if the second vector is a factor, a single $GRA Size Year

I start by doing a simple gaussian tobit by MLE: x1 <- runif(1000) # E() = 0.5 x2 <- runif(1000)*2 # E() = 1 x3 <- runif(1000)*4 # E() = 2 ystar <- -7 + 4*x1 + 5*x2 + rnorm(1000) # is mean 0 y <- ystar censored <- ystar <= 0 y[censored] <- 0 library(AER) m <- tobit(y ~ x1 + x2, left=0, data=D) summary(m) Which gives: Call:

I have water chemistry data with censored values (i.e., those less than reporting levels) in a data frame with a narrow (i.e., database table) format. The structure is: $ site : Factor w/ 64 levels "D-1","D-2","D-3",..: 1 1 1 1 1 1 1 1 ... $ sampdate: Date, format: "2007-12-12" "2007-12-12" ... $ preeq0 : logi TRUE TRUE TRUE TRUE TRUE

After some futile searches, I decided to ask the list to see if any of the sages out there would have an answer: I have a function I wrote a few years ago in S, which calls glim numerous times. I'd like to port it to R, but glm works differently from glim, which takes as part of its input an X design matrix. I probably could write a function to convert glim to glm, but hope this

Hi all, In my continuous transition of GLIM to R I try to make a glm with binomial errors. The data file have 3 vectors: h -> the factor that is ajusted (have 3 levels) d -> number of animais alive (the response) n -> total number of animals To test proportion of alive, make d/n. In GLIM: $yvar d$ $error binomial n$ $fit +h$ scale deviance = 25.730 (change = -9.138) at cycle 4

I have a question about constructing the likelihood function where there is censoring at level 1 in a two-level random effects sum. In a conventional solution, the likelihood function is constructed using the density for failures and the survivor function for (in this case, right) censored results. Within (for example) an R environment, this is easy to do and gives the same solution as survreg

Hello, I'm trying to plot connected time series of two variables in a lattice plot: xyplot(y1 + y2 ~ t, data=size, type="b") y2 has missing data for some of the observations and some points are therefore not connected. It would make theoretical sense to connect the points - is there a way of doing that? (Without filling the obserations using package 'zoo'). Thanks,

I've downloaded R for windows (9.0.1) and it is great! I've converted all my lecture notes for my GLM course to run on R (they are available on my web page below). I must admit I particularly like the default contrast options, which are identical to GLIM. Also I like the gl function - very useful! I have a couple of questions/bugs: 1. predict.glm doesn't work, but predict.lm does -

Dear Experts, In glim() of S Plus, one of the returned values is "var", the estimated variance matrix of coefficients. However, in glm() of R (there is no glim() in R), "var" is not one of the returned values. Anyone know what could I get the varience matrix of coefficients in glm() in R? As a novice in R and S+, I'd appreciate your help Sincerely, Charlie Liu

Hello and Happy New R Two points: 1 Noting the existence of 0.63.2 as a tgz file on CRAN, but being careful or lazy depending on how you want to see it, I also note that the binaries for Redhat stop at 0.63.1 on RH 5.1. I recently got the RH 5.2 Power Tools where I was pleased to see R 0.62.4 included, lots of libraries including V&R. This had been compiled into an rpm - does anyone know

Hello and Happy New R Two points: 1 Noting the existence of 0.63.2 as a tgz file on CRAN, but being careful or lazy depending on how you want to see it, I also note that the binaries for Redhat stop at 0.63.1 on RH 5.1. I recently got the RH 5.2 Power Tools where I was pleased to see R 0.62.4 included, lots of libraries including V&R. This had been compiled into an rpm - does anyone know

Hi Are there any functions that de-convolute data into a given number of clusters, rather like the NPMLE GLIM macros from Murray Aitkin and Brian Francis? Basically I would like to code into R the same approach but include the possiblility of some data being censored. In principle the formulae are the same (just replace the likelihood function) but I haven't managed to get my head round the

First of all congratulations on reaching alpha status. I was wondering if you people are implementing multiple layer support into the codec. I don't know if it should be in the container (ogg) or codec (theora) but perhaps the syncing requires at least support in the codec. For example a commercial: a cup of hot chocolate with steam escaping. Little action, low bandwidth. Then comes the

Dear R community, ? I am happy to announce the publication on CRAN of the revengc package: https://cran.r-project.org/web/packages/revengc/index.html The statistical package revengc was designed to reverse engineer censored, decoupledcensus data into a likely hhs x area uncensored contingency table for estimating interior residentialoccupancy.? ? For basic examples:

Dear R community, ? I am happy to announce the publication on CRAN of the revengc package: https://cran.r-project.org/web/packages/revengc/index.html The statistical package revengc was designed to reverse engineer censored, decoupledcensus data into a likely hhs x area uncensored contingency table for estimating interior residentialoccupancy.? ? For basic examples: