similar to: using newdata in survfit with categorical variable

Hi R helpers, If I have a dataset looks like: ID record 1 20 . 30 . 25 2 26 . 15 3 21 4..................... And I want it becomes ID record 1 20 1 30 1 25 2 26 2 15 3 21 4..................... That is, I have to duplicate IDs for those with multiple records. I am wondering it is possible to be

Hello R-Helpers, I was looking into how to test whether the beta coefficient from a regression would be the same for two different groups contained in the dataset for the regression. When I put that question into google, AI returned a very nice looking answer (and a couple of variations on it). library(car) data <- data.frame(income = c(30, 45, 50, 25, 60, 55), education =

Hi there, > Value=c(709,679,699,657,594,677,592,538,476,508,505,539) > Lard=rep(c("Fresh","Rancid"),each=6) > Gender=rep(c("Male","Male","Male","Female","Female","Female"),2) > Food=data.frame(Value,Lard,Gender) > Food Value Lard Gender 1 709 Fresh Male 2 679 Fresh Male 3 699 Fresh

Dear all, I am confused with the behaviour of survfit with newdata option. I am using the latest version R-2-15-0. In the simple example below I am building a coxph model on 90 patients and trying to predict 10 patients. Unfortunately the survival curve at the end is for 90 patients. Could somebody please from the survival package confirm that this behaviour is as expected or not - because I

Sent from my iPhone > On Jan 19, 2025, at 1:57?PM, David Winsemius <dwinsemius at comcast.net> wrote: > > ?I don?t understand why you don?t include the full text of the error. > > ? > David > Sent from my iPhone > >> On Jan 19, 2025, at 10:00?AM, Sparks, John via R-help <r-help at r-project.org> wrote: >> >> ?Hello R-Helpers, >>

Hello Helpers, I have some problems with fitting the model for my data... -->my Literatur says (crawley testbook)= Non-normality of errors-->I get a banana shape Q-Q plot with opening of banana downwards Structure of data: origin wt pes gender 1 wild 5.35 147.0 male 2 wild 5.90 148.0 male 3 wild 6.00 156.0 male 4 wild 7.50 157.0 male 5 wild 5.90

I don?t understand why you don?t include the full text of the error. ? David Sent from my iPhone > On Jan 19, 2025, at 10:00?AM, Sparks, John via R-help <r-help at r-project.org> wrote: > > ?Hello R-Helpers, > > I was looking into how to test whether the beta coefficient from a regression would be the same for two different groups contained in the dataset for the

Dear R-helpers, To get curves for a pseudo cohort other than the one centered at the mean of the covariates, I have been trying to use the newdata argument to survfit with no success. Here is my model statement, the newdata and the ensuing error. What am I doing wrong? > summary(fit) Call: coxph(formula = Surv(Start, Stop, Event, type = "counting") ~ Week + LagAOO + Prior.f +

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the

Hi, I am doing a project on the simulation of glucose metabolism based on a pharmacokinetic modeling in which we have 4 differential equations. I did this in R by using the odesolve package. It works very well, but I have two questions: Here is the odemodel function _________________________________________________ Ogtt.Odemodel <- function(t, y, p) { absx <- c(-60, -45, -30,

I looked for examples of count data that might interest the students and found this project about dropout rates in Los Angeles High Schools. It is discussed in the UCLA stats help pages for the Stata users: http://www.ats.ucla.edu/stat/stata/library/count.htm and See: http://www.ats.ucla.edu/stat/stata/library/longutil.htm To replicate those results, I used R's excellent foreign package to

Hi, I'm sure this is simple, but I haven't been able to find this in TFM, say I have some data in R like this (pasted here: http://pastebin.com/raw.php?i=sjS9Zkup): > head(df) gender age smokes disease Y 1 female 65 ever control 0.18 2 female 77 never control 0.12 3 male 40 state1 0.11 4 female 67 ever control 0.20 5 male 63 ever state1 0.16

Hi there I'm trying to fit a logistic regression model to data that looks very similar to the data in the sample below. I don't understand why I'm getting this error; none of the data are proportional and the weights are numeric values. Should I be concerned about the warning about non-integer successes in my binomial glm? If I should be, how do I go about addressing it? I'm

Hello at all, working with a dataset I try to optimize a non-linear function with constraint. test<-read.csv2("C:/Users/Herb/Desktop/Opti/NORM.csv") fkt<- function(x){ a<-c(0) s<-c(0) #Minimizing square error for(j in 1:107){ s<-(test[j,2] - (x[1] * test[j,3]) - (x[2] * test[j,4]) - (x[3]*test[j,5]) - (x[4]*test[j,6]) - (x[5]*test[j,7]))^2 a<- a+s} a<-as.double(a)

Hi: I am using a subset of the below dataset to predict PRED_SUIT for the whole dataset but I am having trouble with 'newdata'. The model was created with 153 records and want to predict for 208 records. wolf2 <- structure(list(gridcell = c(367L, 444L, 533L, 587L, 598L, 609L, 620L, 629L, 641L, 651L, 662L, 674L, 684L, 695L, 738L, 748L, 804L, 805L, 872L, 919L, 929L, 938L, 950L, 958L,

Hello, I have a question about glm: if i have a binary covariate (unit=1,0) the reference group would be 0? (prediction for unit=1) example: dat1<-data.frame(y,unit,x1,x2) log_u <- glm(y~.,family=binomial,data=dat1) summary(log_u) Estimate Std. Error z value Pr(>|z|) (Intercept) -0.54247 0.24658 -2.200 0.0278 * unit1 -0.13052 0.22861 -0.571 0.5680 aps

Hi I have been trying so many different things to get my Inverse Mills Ratio going for a Two stage Heckman Model, I have tried the following so far (the commands are listed below till teh point where I get an error), I get an error in the last sentence (marked in bold below), if this were successful then I could have used the IMR as a control in my OLS (which would be the OLS for the outcome

It can't be done with the current code. In a nutshell, you are trying to use a feature that I never got around to coding. It's been on my "to do" list, but may never make it to the top. Terry

Dear community, I've a tree which included at first 23 variables. Then I've pruned this tree, and there are only 8 variables involved. I'd like to predict and only introduce in newdata the values of these 8 variables involved. However, as the tree was built with the 23, it asked me for 15 values, even if it doesn't need them. Is there a way to introduce only this 8 values?

Dear All, I am attempting to make predictions based on a survreg() model with some censoring and a frailty term, as below: predict works fine on the original data, but not if I specify newdata. # a model with groups as fixed effect model1 <- survreg(Surv(y,cens)~ x1 + x2 + groups, dist = "gaussian") # and with groups as a random effect fr <- frailty(groups,