similar to: Need help on this problem!

Hi, Sorry to bother those who are not interested in the question. I am using R to run a multiphase mixed-effects model. I simulated a data set by using the following model. And now I want to use R to estimate the parameters and compare the results with the true values. The equations of the models are as below: gf[ij]=b0[i]+b1[i]*age[ij]+b2[i]*max(0,(age[ij]-tau[i]))+e[ij] b0[i]=b00+e[i0]

Dear all, I need to read in 4 sets of tab delimited data in a loop. The 4 data sets are called "simu1.dat", "simu2.dat" and so on. I know what I need on the righthand side of the read.table expression but I can't the left hand side of it to work (see the line in bold below). Can you kindly help? Many thanks. simu1 <- matrix(0,30,3) simu2 <- matrix(0,30,3) simu3

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

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

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

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

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

Hi, I am using Splus to run a multiphase mixed-effects model. The quations of the models are as below: gf[ij]=b0[i]+b1[i]*age[ij]+b2[i]*max(0,(age[ij]-tau[i]))^2+e[ij] b0[i]=b00+e[i0] b1[i]=b10+e[i1] b2[i]=b20+e[i2] tau[i]=tau+e[i3] i: 1,2,...,100 subjects j: 1,2,...,6 occasions The main scripts of Splus is: simu1<-groupedData(gf~age|id) simu.nlme<-nlme(gf~(b0 + b1 * age + b2 *

To be fair I don't really understand what the error is telling me so I don't know how to correct it. I'm trying to reshape my data so that I do repeated measures ANOVA on it. The warning message: "Error in d[, timevar] <- times[1L] : subscript out of bounds" The program: library(QRMlib) library(Hmisc) ss<-5 mu<- c(0,2,3,12) mu2<- c(0,2,10,20)

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:

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

Dear R-Help, I am Deniz. I am currently trying to replicate a semiparametric sample selection paper and I am working on Klein and Spady estimator. I am using the npindex() and npindexbw() functions. The problem is, I need results for single bandwidth and when I set bandwidth computation to "FALSE" mode, R is not optimizing anything. Here is the code I am using:

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?

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

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))

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 ! 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?