similar to: reshape help

> I am using a random intercept model with SITEID as random and NAUSEA as > outcome. > > I tried using a dataset without missing values and changed my model > statement accordingly but still get the same error. Follwoing in an excerpt. > > anal.data <- read.table("nausea.txt", header=T, sep="\t") > > list(names(anal.data)) > [[1]] > [1]

Hi:   I am using the following code to read a data set in txt in R and using the follwoing model. But it seems to give me an error ' expected variable name error pos 134022'. Any help is greatly appreciated.   Code: anal.data <- read.table("nausea.txt", header=T, sep="\t") list(names(anal.data)) attach(anal.data) n.samples <- dim(anal.data) [1] # number of data

Hi All: I had a basic question to ask. I am running R2WinBUGS so that I could automate the running of my model using 1000 simulated datasets. Below is the code I am using. The only problem I am having is the bugs output that comes out shows my parameters as nos with 1 decimal place after. I would want to have the parameters with 5 places after decimal. How would I specify that in my code for

I am using a random intercept model with SITEID as random and NAUSEA as outcome. Thanks. Anamika I tried using a dataset without missing values and changed my model statement accordingly but still get the same error. Follwoing in an excerpt. > anal.data <- read.table("nausea.txt", header=T, sep="\t") > list(names(anal.data)) [[1]] [1] "SITEID"

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){

I've been doing a simple time-series analysis looking at the relationship between daily pneumonia hospitalizations and daily temperature. To mimic some of the literature, I've been including a time-trend to try to account for normal cyclical trends in hospitalization. So I've been using a function that looks something like this: gam(pneucount ~ temp_f +

Hi All: I am trying to generate Beta-Binomial data with regressors using R. I have used the following code to generate Beta-Binomial data. Now I want to add a covariate to the equation. I would then like to use the simulated data to run the Beta-Binomial model with covariates on it. Appreciate any help. set.seed(111) k<-20 n<-60 x<-NULL p<-rbeta(k,3,3)# so that the mean nausea rate

Hi: I have asked this question on Cross-Validated. So it might be a cross posting but havent received any responses to it. I am trying to see which distribution will best fit the data I am working on. The dataset is as following: Site Nausea headache Abdominal Distension 1 17 5 10 2 12

HI All: Does anyone know the code behind the qbeta function in R? I am using it to calculate exact confidence intervals and I am getting 'NaN' at places I shouldnt be. Heres the simple code I am using: k<-3 > x<-NULL > p<-rbeta(k,3,3)# so that the mean nausea rate is alpha/(alpha+beta) > min<-10 > max<-60 > n<-as.integer(runif(3,min,max)) > for(i in

R-experts: I would like to explore the partial proportional odds models of Peterson and Harrell (Applied Statistics 1990, 39(2): 205-217) for a dataset that I am analyzing. I have not been able to locate a R package that implements these models. Is anyone aware of existing R functions, packages, etc... that might be used to implement the partial proportional odds models? Brant Inman

Hi there, I am using JRClient to build logistic regression model in the following manner : Rconnection c = new Rconnection(); c.eval("KSN<-read.table(\"/Users/amine/Documents/Research/ Tools/R/D2R1.txt\",header=T,sep=\",\")"); c.eval("result <- glm(Nausea ~ Kaletra*Sustiva, family = binomial(link = logit), data =KSN)");

Hi all, I'm new to R and have the following problem: I have a 2 factor design (a has 2 levels, b has 3 levels). I have an object kidney.aov which is an aov(y ~ a*b), and when I ask for model.tables(kidney.avo, se=T) I get the following message along with the table of effects: Design is unbalanced - use se.contrast() for se's but the design is NOT unbalanced... each fator level

Dear R People: I have the following data frame: > x.df date cond freq 1 04/01/09 Fever 12 2 04/02/09 Fever 11 3 04/03/09 Fever 10 4 04/04/09 Fever 13 5 04/05/09 Fever 6 6 04/01/09 Rash 6 7 04/02/09 Rash 10 8 04/03/09 Rash 9 9 04/04/09 Rash 10 10 04/05/09 Rash 8 11 04/01/09

I am working on a simple pilot project comparing the capability of SQL, SAS and R to perform a rolling mean per the following instructions. I have completed the SQL and SAS analysis, so now it's R's turn. Calculate mean values of x (x=count) for each date in the dataset where mean = the average count of days [t-9] through day [t-3] for each date/illness combination. Dataset aggpilot

Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

hi, everyone: i have a question on the reshape function. i have the following dataset : gene tissue patient1 patient2 patient3............. _________________________________________________ gene1 breast 10 20 50 gene2 breast 20 40 60 gene3 breast 100 200 300 which i hope to convert to the following format: gene patientID

Why does mongrel_rails insist on loading ~/.irbrc with each request? a) I''m curious why it loads it at all (I assume there''s no way of getting an inline breakpointer??) b) Why re-load it? It causes problems with any constants that are used in .irbrc... (alternatively, how do I avoid re-assigning to a constant?) Jon -------------- next part -------------- A non-text

I have the model below, for which I run a logistic regression including the interaction term (NSAID*Diuretic) ------------------------ fit1=glm(resp ~ nsaid+diuretic+I(nsaid*diuretic), family= binomial,data=w) NSAID Diuretic Present Absent 0 0 185 6527 0 1 53 1444 1 0 42 1293 1 1 25 253 Coefficients Std. Error z value Pr(>|z|) (Intercept) -3.56335 0.07456 -47.794 < 2e-16 ***

Trevor, Thanks for your reply. That doesnot help Any other suggestions? Anamika On Tue, Aug 10, 2010 at 11:58 AM, Trevor Davies <tdavies@mathstat.dal.ca>wrote: > I think you need to load is R2WinBUGS again. > > require(R2WinBUGS) > > Trevor > > > Hi All: > > > > I am getting an error while trying to plot in R2Winbugs > > > > *Error in

Dear List, How do I extract the approximate Wald test for the frailty (in the following example 17.89 value)? What about the P-values, other Chisq, DF, se(coef) and se2? How can they be extracted? ######################################################> kfitm1 Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id, dist = "gauss"), data = kidney)