similar to: nls() singular graident matrix error

Hi I have two processes which take with a certain probability (p1 and p2) x number of years to complete (age1 and age2). As soon as thge first process is completed, the second one begins. I want to calculate the time it takes for the both processes to be completed. I have the following script which gives me the answer, butI think there must be a more elegant way of doing the calculations

Hello, Simply put, I'm trying to call a function "testme" with value "age=NA". I wish to use "dotlist<-list(...)" inside the function and have dotlist become: $age [1] NA I'm modifying existing code and need to minimize changing that code so it's easiest to conform how I call the existing function. My sample code fragment, results, and R.version

Dear list I'm using the reshape command and want to reshape a wide data set to a long one e.g. I have the variables y1,y2,y3,age1,age2,age3,sex,ethnic I want my new long data set to consist of the variables y (which has been created from y1,y2,y3), age (which has been created from age1,age2,age3), sex and ethnic I have tried to use the command:

Dear R-Users, I used the box.tidwell () function of the car Package. When I used the following formula: semi.sub.in.mi1.boxtidwell_h<-box.tidwell(RENT_LG ~ I(age+1)+I(age2+1)+X06A + I(X08B+1) + I(X22+1) + I(X24+1) + X31A, ~B_YEAR + C_X01 + C_X14 + C_X19 + C_X29A +C_X21 + C_X23 + D_X12 + D_X17 + D_X18 + D_X25 + D_X27 + D_X30 + D_X32 + D_X35, data = semi.sub.in.mi1) everything is

I'm trying to run an ordered logistic regression model. I've run the following code, but the output does not provide the p-values. Is there some command to include the p-values in the output. reg2 <- polr(trade1 ~ age2 + education2 + personal2 + economy2 + partisan2 + employment2 + union2 + home2 + market2 + race2 + income2) summary(reg2) Re-fitting to get Hessian# Call:

Below you will find the output from a failed multi-level model run. I am trying to estimate the following model: Pr(PLFP=1)= logistic regression -> B1_j * bm + B2_j * wm + B3_j bf + B4_j wf + B5 yrsed+ B6 age+ B7 age^2+e_ij B1_j = G01 + G11 bmxd + d1 B2_j = G02 + G12 wmxd + d2 B3_j = G03 + G13 bfxd + d3 B4_j = G04 + G14 wfxd + d4 d1-d4 freely correlated Note that there is no

Greetings to you all, I am performing a semi parametric bootstrap in R on a Gamma Distributed data and a Binomial distributed data. The main challenge am facing is the fact that the residual variance depends on the mean (if I am correct). I strongly feel that the script below may be wrong due to mean-variance relationship #####R code####### fit1s

Hi, Is there a simple command for computing the standard error of a combination of parameter estimates in a GLM? For example: riskdata$age1 <- riskdata$age riskdata$age2 <- ifelse(riskdata$age<67,0,riskdata$age-67) model <- glm(death~age1+age2+ldl, data=riskdata,family=binomial(link=logit)) And we want to find the standard error of the partial linear predictor for

Hello everyone, I'm trying to plot the initial and final size of some tadpoles and look at their growth rates (slope of line). They are divided into 6 densities, and so far I have plotted the initial and final sizes (these are means for a pool), and color coded for the 6 densities, but I want a line to connect the initial and final value for each pool (also color coded by density). Any ideas?

Dear all, I would like to know whether it is possible to unlist elements and keep the original format of the data. To make it more clear, let me give an exemple: I have a list l of dataframes that I created with apply but which looks like this: x1=data.frame(Name=LETTERS[1:2],Age=1:2) x2=data.frame(Name=LETTERS[3:4],Age=3:4) l=list(x1,x2) l [[1]] Name Age 1 A 1 2 B 2 [[2]] Name

Hi all, I am running regressions with many covariates, most of which remain the same each time (control variables). Instead of writing 30 demographic variables every regression, is there a way I could call them all at once using a variable called, perhaps "demog"? I have tried: > demog <- list(age1, age2, age3) but I get an error when I try to call a list in a regression. I also

Hi, I am an R newbie and one thing I am having trouble with binding variables that I have created within one data frame into a new data frame when using na.omit(). To illustrate this problem I will give the example I am working on and the approah I have been using:- data.frame1<-filepath.... attach(data.frame1) #create a new variable using a function new.variable<-rep(1,length(weight3))

Hi all, I am hoping someone can help me out with this: If I have dataframe of years and ages and the first column and first row are filled with leading values: Df<- age1 age2 age3 Yr1 1 0.4 0.16 Yr2 1.5 0 0 Yr3 0.9 0 0 Yr4 1 0 0 Yr5 1.2 0 0 Yr6 1.4 0 0 Yr7 0.8 0 0 Yr8 0.6 0 0 Yr9 1.1 0 0 Now the rest of the cells need to be filled according to the previous year and age

I use lmer to fit a mixed effect model.It give some warnings.what does this warnings mean? and what should I do? > (fm2.mlm <- lmer(qd ~ edu + jiankang + peixun +hunyin + cadcj + age + age2 + sex + dangyuan + Comp.1 + Comp.2+trust.cz1 +(trust.cz1|commid), data = individual,na.action = "na.exclude",family="quasibinomial")) Generalized linear mixed model fit using PQL

Hi all, The last e-mails about beginners gave me the courage to post a question; from a beginner's perspective, there are a lot of questions that I'm tempted to ask. But I'm trying to find the answers either in the documentation, either in the about 15 free books I have, either in the help archives (I often found many similar questions posted in the past). Being an (still actual)

Hi, I am using a function 'polygenic' in the package called 'GenABEL'. -------------------------------------------------------------------------------- > h2.gs <- polygenic(b.dbp~age+age2+age3+sex+b.bmi, kin=gs.gkin, data=gs) ------------------------------------------------------------------------------- ## and I got the following error message: Error in svd(X): infinite or

Hi everyone, I have posted a similar question to this list, but I don't get a reply. I really want to solve this problem, so I post it again... I am trying to use R to fit some mixed-effects models for a nested data. The data is a simulated data with 111 subjects. Each subject has 6 waves' data. Below are the first two subjects' data : > simu1[1:12,] Grouped Data: gf ~ age |

I had a post since 1 week and i want to know why i cant play to Dragon Age2 I'am with Playonlinux 1.3.15, the game i purchast it to Wallmart. When i see the game on Playonlinux i go for search it, i installed it i cant play because Playonlinux tell me to bypass the anti-hacking. I said WHAT'S THAT?. It's the first time i see it for a l?gal game, can you help me please or explain this

'make check-all' for current R has been showing this error in the middle for a few months now - any thought on fixing this? I think cmprsk should be either included in the recommended bundle, or the survival vignette to not depend on it. Having 'make check-all' showing glaring ERROR's for a few months seems to defeat the purpose of doing any checking at all via 'make

Hi, I am just wondering if there is a test available for testing if a linear fit of an independent variable in a Cox regression is enough? Thanks for any suggestions. John Zhang ____________________________________________________________________________________ [[elided Yahoo spam]]