similar to: Looking for simpler solution to probabilistic question

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:

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

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, 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 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 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

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)

Greeting R helpers J I am not familiar with R but I have to use it to analyze data set that I have (30,000 20,000) I want to change the structure of the dataset and I am wondering how that might be possible in R A main data looks like this: some entities are empty Age No. Age No. Age No. Center1 5 2 8 7

Hi ! This is Madhavi from Mumbai, India. Incidently this is my first post. I am working on Credit Scoring Model and using R, I have run the logistic regression. I have received following Output. I have two questions (a) What is the significance of "family = binomial(link = logit)". Why do I have to mention Binomial? Is it because my dependent variable assumes only two values 0 and 1?

Hello, I want to create a new variable which includes 4 age categories in this way: if (age>=12 && age<32) age1==1 if (age>=32 && age<52) age1==2 if (age>=52 && age<72) age1==3 if (age>=72 && age<100) age1==4 but I get the results only for the first observation. how can I apply this condition to all observations? Thanks in advance,

I present you with a function that solves a problem that has bugged me for many years. I think the problem may be general enough to at least consider adding this function, or a revamped version of it, to the 'stats' package, with the other multinomial functions reside. I'm using R to export data to text files, which are input data for an external model written in C++. Parts of the

Dear R-Users, How can I put a pre-defined regression model into to an object of class lm in order to use the predict.lm function. A simplified example: I would normally run a regression analysis on a dataset, > germany<-lm(RENT~AGE1, in.mi01) > summary(germany) Call: lm(formula = RENT ~ AGE1, data = in.mi01) Residuals: Min 1Q Median 3Q Max

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

Consider an analysis of covariance involving age and cohort. The goal is to assess whether the influence of cohort depends upon the age. The simplest case involves data as follows value Age Cohort x1 ????? 1?????? 3 x2?????? 1?????? 4 x3?????? 1?????? 5 x4 ????? 2 ????? 3 x5 ????? 2 ????? 4 x6 ????? 2 ????? 5 etc. Age is a factor. The numeric response variable is value and Cohort is a

Hi there, I have a rather large data set and perform the following cox model: test1 <- list(tstart,tstop,death1,chemo1,radio1,horm1) out1<-coxph( Surv(tstart,tstop, death1) ~ chemo1+chemo1:log(tstop+1)+horm1+horm1:log(tstop+1)+age1+grade1+grade1:log(tstop+1)+positive1+positive1:log(tstop+1)+size1+size1:log(tstop+1), test1) out1 Up to here everything works fine (with each covariate

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:

R-helpers; I used Proc Model in SAS to fit the following model to data: proc model data = dbsmv; a = a1*F**2; b = b1*F + b2*T + b3*F*T; tph2 = tph1 *((1 - exp(-a*age2)) / (1 - exp(-a*age)))**-b; fit tph2; and yielded the following estimated parameters after iterations: a1 = -0.15943, a2 = -1.8177, b1 = -0.01911, b2

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