search for: age2

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 between the ##### Any...

...na, but haven't quite figured out how to do this. Thank you in advance, -jason ##BEGIN TEST CODE ########################################################### testme<-function(...){ dotlist<-list(...) dotlist } nm<-"age" testme(age=NA) testme(nm=NA) testme(age=NA,age2=NA) testlist<-list("age","age2") for (i in 1:length(testlist)){ print(testme(testlist[i])) } ##END TEST CODE ############################################################# ##BEGIN RESULTS ############################################################# > testme<-f...

...392813 10 2 4 41.353627 74.89508 14.8 -0.331536 5.293478 -5.392813 11 2 5 56.353627 73.40505 14.8 -0.331536 5.293478 -5.392813 12 2 6 64.353627 72.61037 14.8 -0.331536 5.293478 -5.392813 Firstly, I used lme to fit a mixed effects model with quadratic curve. > simu1$age2<-simu1$age^2 > mm.lme.2<-lme(gf~age+age2,random=~age+age2|id,data=simu1) It works well: > summary(mm.lme.2) Linear mixed-effects model fit by REML Data: simu1 AIC BIC logLik 5222.225 5267.193 -2601.113 Random effects: Formula: ~age + age2 | id Structure: Gener...

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: data1<-reshape(data,varying=list(c("y1","y2","y3"),...

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 fine. However, when I replaced the time dummy variable: semi.sub.in.mi1.boxtid...

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: polr(formula = trade1 ~ age2 + education2 + personal2 + economy2 + partisan2 + employment2 + union2 + home2 + market2 + race2 + inco...

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 = - 0.01494, b3 = 0.036427 with SEE = 4.7492 and MSE = 0.0111 (427 d.f). The model fits well to the data. Trying to duplicate the model in...

...space restored] > invisible(options(echo = TRUE)) > library(nlme) Loading required package: nls Loading required package: lattice > fm1<-nlme(model=plfp ~ (exp(-1+bm + bmxd + + wm + wmxd + + bf + bfxd + + wf + wfxd + + yrsed + age + age2)) / (1+exp(-1+bm + bmrd + + wm + wmxd + + bf + bfxd + + wf + wfxd + + yrsed + age + age2)), + fixed=list(bm ~ 1, bmxd ~ 1, + wm ~ 1, wmxd ~ 1, + bf ~ 1, bfxd ~ 1, + wf ~ 1, wfxd ~ 1, +...

...=3:4) l=list(x1,x2) l [[1]] Name Age 1 A 1 2 B 2 [[2]] Name Age 1 C 3 2 D 4 I would like to unlist l to create a dataframe with 2 columns and 4 rows but keeping the format of Name (character) and Age (numeric). Now when I unlist l, I obtain : unlist(l) Name1 Name2 Age1 Age2 Name1 Name2 Age1 Age2 1 2 1 2 1 2 3 4 Is there a way to at least obtain something like A 1 B 2 C 3 D 4 as result from the unlist?? Thanks a lot for your replies Naira -- View this message in context: http://www.nabble.com/unlist---dataframes-tp20358993p2035899...

...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 <-glm(mydata$vzv~mydata$age.c+mydata$age2+mydata$sex1, family=Gamma(link=log)) x.betahat1<-fit1s$fitted.values res1<-fit1s$residuals b<-1000 for (i in 1:b){ b.i <- sample(index, size=n, replace=T) res.star1=res1[b.i] bst1=x.betahat1+res.star1 mydata1 <-data.frame(age,age2,sex,bst1) ########Modeling...

...oded by density). Any ideas? I'm hoping to see what the effect of density is on growth, and there are 4 replicates for each density. The initial and final sizes are staggered, 1/2 were measured at age 7.3 and 11, and the other 1/2 were measured at age 8.5 and 12. here's a sample pool age age2 density body_length body_length2 1 1 7.4 11.3 25 0.887 1.322550 2 2 7.4 11.3 100 0.921 1.152000 3 3 7.4 11.3 250 0.896 1.136300 4 4 7.4 11.3 75 0.723 0.955950 5 5 7.4 11.3 500 0.755 1.113300 6 6 7.4 11.3...

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 the combination of the two age parameters, age1+age2, at some age X, without needing...

Hi, i'am with Wine 1.3.15 and i had install Dragon Age 2 succesful, but the game didn't want start, Wine crash. I make some search and i found that, the folder "actvation.exe" tell :Runtime Error! Program: C:Program Files/Core/activation.exe R6034 An application has made an attempt to load the C runtime library incorrectly. I want to know if i can do something with this?

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]]

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 cell so arbitrarily, cell [2,2] should be value in cell [1,1] * exp(0.3), and cell [...

...trate 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)) for (x in 1:length(new.variable)) {f<-((((age1[x]-7)*(weight[x]-mw))+((age2[x]-7)*(weight2[x]-mw))+((age3[x]-7)* (weight3[x]-mw)))/(((age1[x]-7)^2)+((age2[x]-7)^2)+((age3[x]-7)^2))); new.variable[x]<-f} #then bind it into the existing old data frame data.frame2<-cbind(data.frame1,newvariable) rm(dat.frame1) attach(data.frame2) #everything o.k. so far but now the pro...

...g 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 tried: > demog <- cbind(age1, age2, age3) which allows me to run a regression, but this is not practical because when I subset the original data set and run a regression, this new matrix demog doesn't get subsett...

...(rather theoretical) programming problem for which I have found a solution, but I feel it is a rather poor one. I wonder if there's some other (more clever) solution, using (maybe?) vectorization or subscripting. A toy example would be: rel1 rel2 rel3 age0 age1 age2 age3 sex0 sex1 sex2 sex3 1 3 NA 25 23 2 NA 1 2 1 NA 4 1 3 35 67 34 10 2 2 1 2 1 4 4 39 40...

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 Formula: qd ~ edu + jiankang + peixun + hunyin + cadcj + age + age2 + sex + dangyuan + Comp.1...

...d 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: rp_ksbw<-npindexbw(bws=c(1, -0.1, -0.08, 0.06, -0.14, -0.02, 0.3), formula=(rp$participation ~ (AGE + rp$CHILD + rp$YCHILD + rp$HW + rp$EDU + AGE2)), bandwidth.compute=FALSE, optim.method="Nelder-Mead", method="kleinspady") and it is giving me exactly the same values I specified. Here is the result I got: Single Index Model Regression data (2339 observations, 6 variable(s)): AGE rp$CHILD rp$YCHILD rp$HW rp$EDU AG...