similar to: can we include nonparametric component for survival regression?

Dear all, May I ask is there any functions in R to evaluate the fitness of "coxph" and "survreg" in survival analysis, please? For example, the results from Cox regression and Parametric survival analysis are shown below. Which method is prefered and how to see that / how to compare the methods? 1. coxph(formula = y ~ pspline(x1, df = 2))

Hello, coxph does not use any information that are in the dataset between event times (or "death times") , since computation only occurs at event times. For instance, removing observations when there is no event at that time in the whole dataset does not change the results: > set.seed(1) > data <- as.data.frame(cbind(start=c(1:5,1:5,1:4),stop=c(2:6,2:6,2:5),status=c(rep(

Hi, I was reading a paper published in JCO "Prediction of risk of distant recurrence using 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a TransATAC study" (ICO 2010 28: 1829). The author uses a method to estimate the 9-year risk of distant recurrence as a function of continuous recurrence

Dear Sir, I would like to ask whether there is any tool in R, developed for the function of non parametric Anova, where the non parametric analysis is able to compute the p-value for interaction as well (similar to Anova)? If not, would sir like to suggest any of the other statistical software around which would have this tool? Thank you. LYLing. ?SAVE PAPER! SAVE THE WORLD! - Please do not

Does anybody know if there is such a function to estimate the distribution for interval censored data? survfit doesn't work for this type of data as I tried various references. [[alternative HTML version deleted]]

Why do I get different sign of the coefficients of covariates when I run the semi-parametric proportional hazard model (coxph) compared to the parametric proportional hazard model (survreg)? Anyone with experience in extracting information form survreg to make predictions are free to contact me. Cheers, Ullrika [[alternative HTML version deleted]]

Hi experts, http://r.789695.n4.nabble.com/file/n4034318/Parametric_survival_analysis_2nd-order_efffect.JPG Parametric_survival_analysis_2nd-order_efffect.JPG As we know a normal survival regression is the equation (1) Well, I'ld like to modify it to be 2nd-order interaction model as shown in equation(2) Question: Assume a and z is two covariates. x = dummy variable (1 or 0) z = factors

Dear all, I have a bivariate dataset from a preliminary study. I want to do two things: (1) estimate the probability density of this bivariate distribution using some nonparametric method (kernel, spline etc); (2) sample a big dataset from this bivariate distribution for a simulation study. Is there any good method or package I can use in R for my work? I don?t want parametric models like

Dear Rusers, As we all know , there are many methods to do multiple comparison in the parametric statistical analysis, But i can't find some in nonparametric statistical analysis. Could anybody give some suggestions? [[alternative HTML version deleted]]

Hi Evan, Evan Cheng wrote: > The patch looks great. But I do have one comment: > > +let usesCustomDAGSchedInserter = 1 in { > + let Uses = [CR0] in { > + let Uses = [R0] in > + def ATOMIC_LOAD_ADD_I32 : Pseudo< > > The "let Uses = [R0]" is not needed. The pseudo instruction will be > expanded like this later: > > + BuildMI(BB,

Look for createVirtualRegister. These are examples in PPCISelLowering.cpp. Evan On Jul 8, 2008, at 8:24 AM, Gary Benson wrote: > Hi Evan, > > Evan Cheng wrote: >> The patch looks great. But I do have one comment: >> >> +let usesCustomDAGSchedInserter = 1 in { >> + let Uses = [CR0] in { >> + let Uses = [R0] in >> + def ATOMIC_LOAD_ADD_I32 :

Dear all, Does R or S-plus or any of their packages provide Non-parametric "Run test" (which tests whether a sequence of numbers might be random or not)? If yes, i'd like a numerical illustration of this test. Any response / help / comment / suggestion will be greatly appreciated. Thanks in advance. ------------------------------- Mohammad Ehsanul Karim <wildscop at

My interpretation of the relation between 1-way ANOVA and Wilcoxon's test (wilcox.test() in R) is the following. 1-way ANOVA is to test if two or multiple distributions are the same, assuming all the distributions are normal and have equal variances. Wilcoxon's test is to test two distributions are the same without assuming what their distributions are. In this sense, I'm wondering

Well fellow R users, I throw myself on your mercy. Help me, the unworthy, satisfy my employer, the ungrateful. My feeble ramblings follow... I've searched R-Help, the R Website and done a GOOGLE without success for a one way ANOVA procedure to analyse data that are both non-normal in nature and which exhibit unequal variances and unequal sample sizes across the 4 treatment levels. My

Hi Gary, The patch looks great. But I do have one comment: +let usesCustomDAGSchedInserter = 1 in { + let Uses = [CR0] in { + let Uses = [R0] in + def ATOMIC_LOAD_ADD_I32 : Pseudo< The "let Uses = [R0]" is not needed. The pseudo instruction will be expanded like this later: + BuildMI(BB, TII->get(is64bit ? PPC::LDARX : PPC::LWARX), dest) +

help Hello, I?m a statistic student in Austria and I have to do a survival analysis in R by using psplines as regressor. My problem is that I sometimes (I think it depends on the choose of the parameters) get a error message, but I do not know what it means. After that I tried the procedure with an example dataset R is providing. Although using the cancer dataset I also get this message. Input:

I just finished coding up a change to how code generation builds machine instructions. The change is in include/llvm/CodeGen/MachineInstrBuilder.h, where when you want to add a register, you have to specify a long list of booleans indicating if it's defined, implicit, killed, dead, or early clobbered. I don't know about you, but it was hard for me to read the source and understand what was

On 13/05/2009, at 02.46, Bill Wendling wrote: > Instead of all of the booleans, you pass in a flag that has bits set > to indicate what state the register is in: > > namespace RegState { > enum { > Define = 0x1, > Implicit = 0x2, > Kill = 0x4, > Dead = 0x8, > EarlyClobber = 0x10, > ImplicitDefine = Implicit |

I've been looking for ways to calculate a large number (100) of non-crossing Nonparametric quantile regressions on large populations (1000+). Can the quantreg package in R ensure the non-crossing property? If not, do you know any alternative? Thank you, Paulbegc -- View this message in context:

Evan Cheng wrote: > You need to insert new basic blocks and update CFG to accomplish this. > There is a hackish way to do this right now. Add a pseudo instruction > to represent this operation and mark it usesCustomDAGSchedInserter. > This means the intrinsic is mapped to a single (pseudo) node. But it > is then expanded into instructions that can span multiple basic >