similar to: betareg help

Dear all, I want to perform paired Wilcoxon signed ranks test on my data. I have pairs defined by ID and TIME variables. How can I calculate difference in variables q1, q2 in each pair? TIME ID q1 q2 1 1187 3 2 1 1706 3 3 1 1741 2 4 2 1187 3 2 2 1706 3 3 2 1741 2 4 Please, any clue! :) -- ************************** Vlatka Matkovic Puljic +32/ 485/ 453340 [[alternative HTML version

Hi, I have dataset n1 and v1 (years). when i ask median(year) [1] NA but if i put summary of dataset n1: summary(n1) R produces median (together with min/max/mean....) why it is so? -- ************************** Vlatka Matkovic Puljic 095/8618 171 [[alternative HTML version deleted]]

Hi, I have numerical variable that I want to recode into categories '0' and '1 and more' and do analysis with that data. I have tried various of possibilities to do so, but I am sucked and nothing is working. recode(Q12, "0='A';1:30='B'") cut(Q12, breaks=c(0,1,30), lables=c('0', '1 and more')) cat(Q12, "0=0;1-33=1") What should

**Hi, I wanted to create a network of drugs that are being studied together. So, I created a file as a graph. But plot of my network is not corect! It looks like to me that something else is lying behind this network links created. Could someone help me with this? Thanx! a<-read.graph(file=file.choose(), format="edgelist") plot.igraph(a) File (number present ID of drug): 15 2 10

I have a file: x y z 0.025 0.025 1.65775 0.025 0.050 1.62602 0.025 0.075 1.63683 0.025 0.100 1.91847 0.025 0.125 2.00913 0.025 0.150 1.82222 0.025 0.175 1.70901 0.025 0.200 1.39759 0.025 0.225 1.39089 0.025 0.250 1.04762 If I read the file like this: data<-read.table("file.dat") How do I access the vectors x,y,z that are inside the dataframe data? I studied Venables and

Hi, I have v1 (date of test) and v2 (year of birth). v1 v2 15.5.2008 88 18.6.2009 54 I want R to use only year in v1; and v2 see as 1988 and calculate age in v3. any ideas how to do that? -- ************************** Vlatka Matković Puljić 095/8618 171 [[alternative HTML version deleted]]

Hi I'm trying to fit a model in betareg and I'm getting errors, but have no idea what they mean or how to solve them. Does anyone have experience with this? > model <- betareg(ACT ~ ST*SoilT, data = actDL_F) Warning messages: 1: In sqrt(W) : NaNs produced 2: In sqrt(W) : NaNs produced 3: In sqrt(1 + phihat) : NaNs produced data summaries don't give any na's or problems I

Dear R-helpers: We have had fun using betareg to fit models with proportions as dependent variables. However, in the analysis of these models we found some wrinkles and don't know where is the best place to start looking for a fix. The problems we see (so far) are that 1. predict ignores newdata 2. anova does not work Here is the small working example: ---------------------------- x

Folks, The betareg package appears to be polished and works well. But I would like to look at the exact formulas for the underlying model being estimated, the likelihood function, etc. E.g. if one has to compute \frac{\partial E(y)}{\partial x_i}, this requires careful calculations through these formulas. I read "Regression analysis of variates observed on (0,1): percentages, proportions and

Hello, I have a dataset with proportions that vary around a fixed mean, is it possible to use betareg to look at variance in the dispersion parameter while keeping the mean fixed? I am very new to R but have tried the following: svec<-c(qlogis(mean(data1$scaled)),0,0,0) f<-betareg(scaled~-1 | expt_label + grouped_hpi, data=data1, link.phi="log",

Dear all, I get an error using betrag on this data set :http://dl.dropbox.com/u/1866110/dump.csv. I run it like this regression f2.1=betareg(Y~X1+X2,data=dump) summary(f2.1) I get : Call: betareg(formula = Y ~ X1 + X2, data = dump) Standardized weighted residuals 2: Error in quantile.default(x$residuals) : missing values and NaN's not allowed if 'na.rm' is FALSE In addition:

Hi, In using the betareg package, I encounter the following error message: Error in lm.wfit(x, linkfun(y), weights, offset = offset) : NA/NaN/Inf in foreign function call (arg 4) Any help will be most appreciated. Thanks in advance. Alex

Dear useRs, version 2.2-2 of the "betareg" package has just been released on CRAN http://CRAN.R-project.org/package=betareg accompanied by an article in the Journal of Statistical Software http://www.jstatsoft.org/v34/i02/ The package provides beta regression for data in the unit interval (0, 1) such as rates and proportions. The manuscript replicates several practical

Dear useRs, version 2.2-2 of the "betareg" package has just been released on CRAN http://CRAN.R-project.org/package=betareg accompanied by an article in the Journal of Statistical Software http://www.jstatsoft.org/v34/i02/ The package provides beta regression for data in the unit interval (0, 1) such as rates and proportions. The manuscript replicates several practical

Dear all, Lets say I have the following data frame: > df1 <- data.frame(Show=c('Star Trek', 'Babylon 5', 'Dr Who'), Size=c(0.7, 0.0, 0.701), Date=as.Date(c('2007-08-03', '2007-08-03', '2007-08-03'), format='%Y-%m-%d')) > df2 <- data.frame(Show=c('Star Trek', 'Dr Who', 'Torchwood'), Size=c(0.8, 0.85,

Hi all, I am running the following model: > glm89.nb <- glm.nb(AvGUD ~ Year*Trt*Micro) where Year has 3 levels, Trt has 2 levels and Micro has 3 levels. However when I run it has a zero inflated negative binomial (as I have lots of zeros) I get the below error message: > Zinb <- zeroinfl(AvGUD ~ Year*Trt*Micro |1, data = AvGUD89, dist = "negbin") Error in optim(fn =

Hello, I have a data frame with 1425 observations, 539 of which are zeros. I am trying to fit the following ZINB: f3<-formula(Nbr_Abs~ Zone * Year + Source) ZINB2<-zeroinfl(f3, dist="negbin", link= "logit", data=TheData, offset=log(trans.area), trace=TRUE) Zone is a factor with 4 levels, Year a factor with 27 levels, and Source a factor with 3 levels. Nbr_Abs is counts

Hi, When I run the survfit function, I want to get the restricted mean value and the standard error also. I found out using the "print" function to do so, as shown below, print(km.fit,print.rmean=TRUE) Call: survfit(formula = Surv(diff, status) ~ 1, type = "kaplan-meier") records n.max n.start events *rmean *se(rmean) median 200.000

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

Dear r-helpers, After successfully running require(nlme) vfr.lmL <- lmList( estimate ~ (slant + respType + visField + hand)^2 | subject, vfr ) pairs(vfr.lmL, id = 0.01, adj = -0.5) # Pinheiro & Bates (p. 141) produces the following error: Error in sprintf(gettext(fmt, domain = domain), ...) : object "form" not found Any guesses as to what I may have done wrong?