similar to: Censboot Warning and Error Messages

Dear mailing list, I tried to run the example for the conditional bootstap written in the help file of censboot. I got the following result: STRATIFIED CONDITIONAL BOOTSTRAP FOR CENSORED DATA Call: censboot(data = aml, statistic = aml.fun, R = 499, F.surv = aml.s1, G.surv = aml.s2, strata = aml$group, sim = "cond") Bootstrap Statistics : original bias std. error t1*

Dear all, I run the example of "censboot" contained in "boot" package. But, I can't find the confidence interval of the resulted "censboot" object. Any idea ? > aml.fun <- function(data) { + surv <- survfit(Surv(time, cens)~group, data=data) + out <- NULL + st <- 1 + for (s in 1:length(surv$strata)) { +

Dear R Help list, My question is regarding extracting the standard error or Z-score from a cph or coxph call. My Cox model is: - modz=cph(Surv(TSURV,STATUS)~RAGE+DAGE+REG_WTIME_M+CLD_ISCH+POLY_VS, data=kidneyT,method="breslow", x=T, y=T) I've used names(modz) but can't see anything that will let me extract the Z scores for each coefficient or the standard errors in the same

Hi, The following code, from Angelo Canty article on line "Resampling Methods in R: the boot Package, 2002", works fine for Angelo Canty using R 2.6.0 on Windows XP. It also works for me using R 1.2.1 and S-PLUS 2000 on Windows XP after installing the S-PLUS bootstrap library, with slight differences in my outputs. > library(boot) > library(survival) >

Hi, The following code, from Angelo Canty article on line "Resampling Methods in R: the boot Package, 2002", works fine for Angelo Canty using R 2.6.0 on Windows XP. It also works for me using R 1.2.1 and S-PLUS 2000 on Windows XP after installing the S-PLUS bootstrap library, with slight differences in my outputs. > library(boot) > library(survival)

Hi Arie, Given the heuristic, in all of my examples with a missing two-factor interaction the three-factor interaction should be coded with dummy variables. In reality, it is encoded by dummy variables only when the numeric:numeric interaction is missing, and by contrasts for the other two. The heuristic does not specify separate behavior for numeric vs categorical factors (When the author of

Hi, The following code, from Angelo Canty article on line "Resampling Methods in R: the boot Package, 2002", works fine for Angelo Canty using R 2.6.0 on Windows XP. It also works for me using R 1.2.1 and S-PLUS 2000 on Windows XP after installing the S-PLUS bootstrap library, with slight differences in my outputs. > library(boot) >

I think it is not a bug. It is a general property of interactions. This property is best observed if all variables are factors (qualitative). For example, you have three variables (factors). You ask for as many interactions as possible, except an interaction term between two particular variables. When this interaction is not a constant, it is different for different values of the remaining

Hi, The following code, from Angelo Canty article on line "Resampling Methods in R: the boot Package, 2002", works fine for Angelo Canty using R 2.6.0 on Windows XP. It also works for me using R 1.2.1 and S-PLUS 2000 on Windows XP after installing the S-PLUS bootstrap library, with slight differences in my outputs. > library(boot) > library(survival) >

Hello Tyler, You write that you understand what I am saying. However, I am now at loss about what exactly is the problem with the behavior of R. Here is a script which reproduces your experiments with three variables (excluding the full model): m=expand.grid(X1=c(1,-1),X2=c(1,-1),X3=c("A","B","C")) model.matrix(~(X1+X2+X3)^3-X1:X3,data=m)

Hi, I recently ran into an inconsistency in the way model.matrix.default handles factor encoding for higher level interactions with categorical variables when the full hierarchy of effects is not present. Depending on which lower level interactions are specified, the factor encoding changes for a higher level interaction. Consider the following minimal reproducible example: -------------- >

Dear R-help folks: Here is my platform: > version platform sparc-sun-solaris2.7 arch sparc os solaris2.7 system sparc, solaris2.7 status major 1 minor 5.0 year 2002 month 04 day 29 language R I have a

Hi Arie, Thank you for your further research into the issue. Regarding Stata: On the other hand, JMP gives model matrices that use the main effects contrasts in computing the higher order interactions, without the dummy variable encoding. I verified this both by analyzing the linear model given in my first example and noting that JMP has one more degree of freedom than R for the same model, as

Hi all, I found that the two different versions of "survival" packages, namely 2.36-5 vs. 2.36-8 or later, give different results for coxph function. Please see below and the data is attached. The second one was done on Linux, but Windows gave the same results. Could you please let me know which one I should trust? Thanks, ...Tao #####============================ R2.13.0,

Hi, I want to fit multiple cox models using the coxph() function. To do this, I use a for-loop and save the relevant results in a separate matrix. In the example below, only two models are fitted (my actual matrix has many more columns), one gives a warning message, while the other does not. Right now, I see all the warning message(s) after the for-loop is completed but have no idea which model

Hi Arie, The book out of which this behavior is based does not use factor (in this section) to refer to categorical factor. I will again point to this sentence, from page 40, in the same section and referring to the behavior under question, that shows F_j is not limited to categorical factors: "Numeric variables appear in the computations as themselves, uncoded. Therefore, the rule does not

Hi Arie, I understand what you're saying. The following excerpt out of the book shows that F_j does not refer exclusively to categorical factors: "...the rule does not do anything special for them, and it remains valid, in a trivial sense, whenever any of the F_j is numeric rather than categorical." Since F_j refers to both categorical and numeric variables, the behavior of

This is a simple R program that I have been trying to run. I keep running into the "singular matrix" error. I end up with no sensible results. Can anyone suggest any changes or a way around this? I am a total rookie when working with R. Thanks, Rasika > library(survival) Loading required package: splines > args(coxph) function (formula, data, weights, subset, na.action, init,

Hello Tyler, I rephrase my previous mail, as follows: In your example, T_i = X1:X2:X3. Let F_j = X3. (The numerical variables X1 and X2 are not encoded at all.) Then T_{i(j)} = X1:X2, which in the example is dropped from the model. Hence the X3 in T_i must be encoded by dummy variables, as indeed it is. Arie On Thu, Nov 2, 2017 at 4:11 PM, Tyler <tylermw at gmail.com> wrote: > Hi

Hello Tyler, I want to bring to your attention the following document: "What happens if you omit the main effect in a regression model with an interaction?" (https://stats.idre.ucla.edu/stata/faq/what-happens-if-you-omit-the-main-effect-in-a-regression-model-with-an-interaction). This gives a useful review of the problem. Your example is Case 2: a continuous and a categorical regressor.