similar to: Fractional Polynomials in Competing Risks setting

Dear all, I was wondering if anyone can help? I am an R user but recently I have resorted to SAS to calculate the probability of the event (and the associated confidence interval) for the Cox model with combinations of risk factors. For example, suppose I have a Cox model with two binary variables, one for gender and one for treatment, I wish to calculate the probability of survival for the

Hi: I have a few clarification questions about the elements returned by the coxph function used in conjuction with a frailty term. I create the following group variable: group <- NULL group[id<50] <- 1 group[id>=50 & id<100] <- 2 group[id>=100 & id<150] <- 3 group[id>=150 & id<200] <- 4 group[id>=200 & id<250] <- 5 group[id>=250

Dear All, I am attempting to use forward and/or backward selection to determine the best model for the variables I have. Unfortunately, because I am dealing with patients and every patient is receiving treatment I need to force the variable for treatment into the model. Is there a way to do this using R? (Additionally, the model is stratified by randomisation period). I know that SAS can be

-- begin included message ----- I am looking at a continuous variable, age. I am looking at time to 12-month remission and can calculate the HR and 95% confidence interval are follows: coxfita = coxph(Surv(rem.Remtime,rem.Rcens)~nearma$all.age,data=nearma) exp(coxfita$coefficients) exp(confint(coxfita)) However, because I am looking at age as a continuous variable I cannot draw a Kaplan-Meier

Hi All, This sounds a relatively simple query, and I hope it is! I am looking at a continuous variable, age. I am looking at time to 12-month remission and can calculate the HR and 95% confidence interval are follows: coxfita = coxph(Surv(rem.Remtime,rem.Rcens)~nearma$all.age,data=nearma) exp(coxfita$coefficients) exp(confint(coxfita)) However, because I am looking at age as a continuous

Dear R-Help, I am using the 'mfp' package. It produces three plots (as I am using the Cox model) simultaneously which can be viewed together using the following code: fit <- mfp(Surv(rem.Remtime,rem.Rcens)~fp(age)+strata(rpa),family=cox,data=nearma,select=0.05,verbose=TRUE) par(mfrow=c(2,2)) plot(fit) They can be viewed separately but the return key must be pressed after each graph

Dear All, Sorry to bother you again. I have a model: coxfita=coxph(Surv(rem.Remtime/365,rem.Rcens)~all.sex,data=nearma) and I'm trying to do a plot of Schoenfeld residuals using the code: plot(cox.zph(coxfita)) abline(h=0,lty=3) The error message I get is: Error in plot.window(...) : need finite 'ylim' values In addition: Warning messages: 1: In sqrt(x$var[i, i] * seval) : NaNs

Dear R-help, I'm very sorry to ask 2 questions in a week. I am using the package 'crr' and it does exactly what I need it to when I use the dataset a. However, when I use dataset b I get the following error message: Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : system is computationally singular: reciprocal condition number =

Hi all, I'm very confused! I've been using the same code for many weeks without any bother for various covariates. I'm now looking at another covaraite and whenever I run the code you can see below I get an error message: "Error in rep(0, nrow(data)) : invalid 'times' argument" This code works: # remove 'missing' cases from data # snearma <-

Dear list, In some places (for example, http://en.wikipedia.org/wiki/Support_vector_machine) , the polynomail kernel in SVM is written as (u'*v + 1)^d, while in the document of svm() in e1071 package, the polynomial kernel is written as (gamma*u'*v + coef0)^d. I am a little confused here: When doing parameter optimization (grid search or so) for polynomial kernel, does it need to tune

Now that Solaris 10 11/06 is available, I wanted to post the complete list of ZFS features and bug fixes that were included in that release. I''m also including the necessary patches for anyone wanting to get all the ZFS features and fixes via patches (NOTE: later patch revision may already be available): Solaris 10 Update 3 (11/06) Patches sparc Patches * 118833-36 SunOS 5.10:

Good Evening, I would like to work with multivariate polynomials (x and y variables). I know that there is a package called multipol but I am not sure that supports my needs. I use a function (in reality legendre.polynomials) which creates me the polynomials I want. For example the following returns > legendre.polynomials(2)[[2]] x (first order polynomial) I would like to calculate the

Hi Folks! Im using the function poly to generate orthogonal polynomials, but Id like to see the actual polynomials so that I could convert it to a polynomial in my original variable. Is that possible and if so how do I do it? /E

This very likely falls in the category of an unexpected result due to user ignorance. I generated the following data: time <- 0:10 set.seed(4302007) y <- 268 + -9*time + .4*(time^2) + rnorm(11, 0, .1) I then fit models using both orthogonal and raw polynomials: fit1 <- lm(y ~ poly(time, 2)) fit2 <- lm(y ~ poly(time, degree=2, raw=TRUE)) > predict(fit1, data.frame(time =

Hi I can do this: formula = as.factor(outcome) ~ . in glm and other model building functions. I think there is a way to get the product of the determinants (that is d1 * d2, d1 * d3, etc) and also another way to get all the polynomials (that is like poly(d1,2) would produce for a single determinant). Can anyone tell me how you write them? Stephen [[alternative HTML version deleted]]

Dear All, I have found in the poly help this sentence: The orthogonal polynomial is summarized by the coefficients, which can be used to evaluate it via the three-term recursion given in Kennedy & Gentle (1980, pp. 343–4), and used in the predict part of the code. My question: which type of orthogonal polynomials are used by this function? Hrmite, legendre.. TIA Giovanni [[alternative HTML

Dear list, This is quite a specific question requiring the package orthopolynom. This package provides a nice implementation of the Legendre polynomials, however I need the associated Legendre polynomial which can be readily expressed in terms of the mth order derivative of the corresponding Legendre polynomial. (For the curious, I'm trying to calculate spherical harmonics [*]).

Dear R-users, I am trying to modify the mfp() function in the mfp package to model Weibull survival using fractional polynomials approach. However, I keep getting into trouble when mfp.fit and other "hidden" functions can't be found. I did find some of them in Splus but it's getting nowhere. I wonder if any of you can give me some tips on how to modify it or any experience

Fractional polynomials ("FPs") are an automatic way of fitting non-linear, parametric effects. The R-package mfp implements a frequentist inference approach for FP models. Recently, we have proposed a Bayesian inference approach for normal FP models, which is based on the quasi-default hyper-/g/ prior for the regression coefficients [1]. This approach is implemented in the new

Fractional polynomials ("FPs") are an automatic way of fitting non-linear, parametric effects. The R-package mfp implements a frequentist inference approach for FP models. Recently, we have proposed a Bayesian inference approach for normal FP models, which is based on the quasi-default hyper-/g/ prior for the regression coefficients [1]. This approach is implemented in the new