similar to: A model for disease progression

When I alter the levels of a factor, why does it alter the names too? f <- factor(c(A="one",B="two",C="one",D="one",E="three"), levels=c("one","two","three")) names(f) -- gives [1] "A" "B" "C" "D" "E" levels(f) <-

Dear All, Writing sooner than I thought I'd need to. I'm using R 2.4 on Mac OS X, with RODBC, PostgreSQL 8.1 and Actual's ODBC driver. I have all my data in Filemaker 8.5, but it is automatically exported into PostgreSQL for analysis as Filemaker's ODBC and JDBC access is awful, slow and has a tendency to crash. I have disability data where for each patient there is a survival

Why does > curve(function(y) y^2, from=0,to=1) not work, whereas > myf <- function(y) y^2 > curve(myf, from=0,to=1) work? For the former, I get the error message Error in curve(function(y) y^2, from = 0, to = 1) : 'expr' must be a function or an expression containing 'x' I'm using R1.7.0 under Windows XP. Damon Wischik.

I am having trouble with ESS+R. I don't know if it is an ESS problem or an R problem, so I'm posting to this mailing list in the first instance. I am using R 1.6.2, Windows XP (latest updates installed), XEmacs 21.4, and ESS 5.1.21. I have experienced the same problem under Windows 2000 and recent versions of Emacs and ESS (though my current Windows XP installation is from scratch, and

I woud like to plot cumulative histograms. Specifically, I have data like Sex M M F M F F M F Height 6 6.3 6.1 5.5 7.2 6.2 5.9 6.0 .... and I want to plot a histogram of the distribution of all heights, colouring the histogram bars according to sex, for example | o | oo o | o oo ** o o = observations of women | o o*o***o * = observations of

There have been a number of posts to this list by people having trouble with the HTML search engine. Often these troubles are caused by incorrect setups (user hasn't installed Java properly, or Java is disabled, or Javascript is disabled). Sometimes the trouble persists even when Java is installed properly. I have written an alternative HTML search engine, which is based on Javascript rather

I want to plot biplots. I have seen the function biplot, but there are some extra features I would like, that I do not know how to achieve. 1. My observations, and my variables, fall into groups. Is there a way to, say, plot the observations in several different colours, according to which group the observation falls into? Similarly with the variables? I am used to the lattice idiom, in which one

I am writing to get a better handle on a warning I am getting from a coxph analysis I am doing. I am analysing age of onset of dementia *after* the onset of parkinson disease. My data looks like: age.park age.dem age.death censor x1 x2 x3 x4 1 76 87 88 0 16 33 E3 E3 2 75 84 84 0 33 36 E3 E3 3 77 81 81 1 NA NA

----- Original Message ----- From: "Richard A. O'Keefe" <ok at cs.otago.ac.nz> To: <paul at datavore.com> Sent: Thursday, September 04, 2003 2:56 AM Subject: Re: [R] Overlaying graphs > I do not know how to overlay the curve graphic on top of hist graphic. > > Do you know about the "add=TRUE" option for plot()? > > I am hoping to show visually

Hi, * I am using read.table command as follow kegg<-read.table("c:/IDs.tab",header =TRUE,quote= "'", sep="\t") * Fragment of file is as follow: ID Pathway 04916 Melanogenesis 04920 Adipocytokine signaling pathway 04930 Type II diabetes mellitus 04940 Type I diabetes mellitus 04950 Maturity onset diabetes of the young 05010

I was at a lecture the other day and I saw a presentation of very neat (short) animation modeling epidemic disease spread over a map region. When I ask what software they used they mentioned SAS. Do you know if there are equivalent resources in R to model the spread of disease with animation output? My search in R-help and google didn't lead to any document (though I found a couple of

Please explain me as what it means and how this analysis can be done using R and which library(ies) are needed. Thanks -- View this message in context: http://www.nabble.com/Regression-using-age-and-Duration-of-disease-as-a-continous-factors-tp24574133p24574133.html Sent from the R help mailing list archive at Nabble.com.

Hi can you please give guidance for generating future prediction values from existing heart disease data and we want to know the which type of models are using for generating the future prediction. can you please tell me how can i write the formula for future predictive values. can you see the following attachment and send me solution for that problem..... -- Thanks and Regards, Bala Chand

I'm trying to produce empirical bayes estimates based on the lognormal model in disease mapping Is there a way this can be done in R? thanks Oarabile

Danardono <daodao99 at student.umu.se> wrote: > > While waiting for 2.1, for those who need function[s] for this > survival-splitting business, as I do, this 'survcut' function below > might be helpful. > It is not an elegant nor efficient function but it works, at least for > some examples below. > Ditto the following, for the case where there are multiple

I have some data which I am trying to fit with a negative binomial distribution. I have found the glm.nb function from MASS. I have reason to believe that the mean parameter mu depends on certain factors, and that the shape parameter theta depends on others. If, say, the factors are P and Q, it might be that mu ~ P:Q and theta ~ P (where mu ~ P:Q means that mu is a function of the pair (P,Q))

I have three time series, x, y, and z, and I want to analyse the relations between them. However, they have vastly different resolutions. I am writing to ask for advice on how to handle this situation in R. x is a stimulus, and y and z are responses. x is a rectangular pulse 4 sec long. Its onset and offset are known with sub-millisecond precision. The onset varies irregularly -- it doesn't

I get strange results when I try to format() an NA character string. > x <- 'sometext' > x[1] <- NA > format(NA, width=32) # displays sensibly, right-justified > format(x,width=32) # displays sensibly, left-justified > format(x,width=33) # displays "" > format(x,width=36) # R exits abnormally with code 5 > version platform i386-pc-mingw32

I am having trouble with optim. It claims to have converged to a minimum, yet it has in the course of the optimization visited many points which are closer to optimal. I would be grateful for any explanation of this behaviour. I'm trying to estimate the parameters in the model X ~ Binomial(1,p) * NegBin(mu,theta). So I define a log likelihood function, and invoke optim thus: o <- optim

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