similar to: bootstrap means by strata

Is there a way of extracting mean distance or dissimilarity for a given strata from a 'dist' or 'dissimilarity' object, e.g. extract mean distances for each species in Anderson's iris data? data(iris) iris.dist<-dist(iris[,1:4]) then what? Mikkel Grum, PhD Genetic Diversity Scientist International Plant Genetic Resources Institute (IPGRI) Sub-Saharan Africa Group ***

Hello All. I must be missing the really obvious here: mm <- function(d, i) median(d[i]) b1 <- boot(gravity$g, mm, R = 1000) b1 b2 <- boot(gravity$g, mm, R = 1000, strata = gravity$series) b2 Both b1 and b2 seem to have done (almost) the same thing, but it looks like the strata argument in b2 has been ignored. However, str(b1) vs str(b2) does show that the strata have been noted

Just browsing the documentation, and searching the list came up short... I have some unbalance data and was wondering if, in a "0" v "1" classification forest, if these options might yield better predictions when the proportion of one class is low (less than 10% in a sample of 2,000 observations). Not sure how to specify these terms... from the docs, we have: classwt: Priors

Hello, I was hoping that someone could answer a few questions for me (the background is given below): 1) Can the coxph accept an interaction between a covariate and a frailty term 2) If so, is it possible to a) test the model in which the covariate and the frailty appear as main terms using the penalized likelihood (for gaussian/t frailties) b)augment model 1) by stratifying on the variable that

Hi all, I am aware that crr lacks the "friendly" command structure of functions such as cph. All is clear to me about including covariates until I want to include a stratification term in the competing risk framework (no nice strat command). I am still a bit of a novice in R - I am looking for an example to help me with this, but can't seem to find one. Any advice appreciated (no

[code]>require("survival") > coxph(Surv(futime,fustat)~age + strata(rx),ovarian) Call: coxph(formula = Surv(futime, fustat) ~ age + strata(rx), data = ovarian) coef exp(coef) se(coef) z p age 0.137 1.15 0.0474 2.9 0.0038 Likelihood ratio test=12.7 on 1 df, p=0.000368 n= 26, number of events= 12 > coxph(Surv(futime,fustat)~age, ovarian, subset=rx==1)

Dear All, I got a warning message "X matrix deemed to be singular" in Cox model with a time dependent coefficient. In my analysis, the variable "SEX" is a categorical variable which violate the PH assumption in Cox. I first used the survSplit() function to break the data set into different time intervals, and then fit the model. The procedures can be described as follows:

> -----Original Message----- > From: Brian Roy [mailto:mister.roy@gmail.com] > I do this with a Toshiba Strata DK280. The model before the CTX. You > shouldn't have any problem doing this with the CTX either. Assuming > you don't have SMDI integration with your voicemail (you didn't state > that you did), you just need the phones to send trailing digits, and > you

Hi everyone, Been reading up on Asterisk, and very interested in learning more. I've googled and read the archives and haven't found anything definitive on support for this phone system. We have a fairly large investment in the system itself and the phones, but would love to get away from the voicemail system it forces on us. Can anyone provide any feedback on using this system with

I've been using strata(sampling) and found that if the dataframe to be sampled ("data") consists of repeated measures that are not sorted in order the run will fail on a given stratum. Ordering the dataframe prior to using strata() eliminates this problem. Thanks, Eric -- Eric Vander Wal Ph.D. Candidate University of Saskatchewan, Department of Biology, 112 Science Place,

How can I get the log odds associated with the levels in strata() within a clogit() model? I'm running R-1.9.0 on a Linux platform. I am using clogit() to run a Rasch model in Item Response Theory in psychometrics. Symbolically, the model is: logit(p_{j,k}) = \log({\Pr(p_{j,k}) \over \Pr(1-p_{j,k})}) = \theta_j - \alpha_k, That is, the log odds of answering an test item correctly is

Hello, I am new to R, and am having trouble running a stratified bootstrap. My data set consists of 38 study sites in which I recorded the number of bird pairs. Each site has been classified as burned or mowed and for each of these two strata I would like to determine the precision of an overall density estimate. I can run an unstratified bootstrap without problem, but when I specify the strata I

Dear list members, I am working with a Cox ph model for the duration of unemployment. The event of interest in my analysis is getting employed. I have various background variables explaining this event: age, sex, education etc. I have multiple unemployment spells per person. I use a model with person-specific frailty terms in order to take into account the correlation of spells by the same

Sorry for the repost, but I've really been looking, and can't find any syntax direction on this issue... Just browsing the documentation, and searching the list came up short... I have some unbalanced data and was wondering if, in a "0" v "1" classification forest, some combo of these options might yield better predictions when the proportion of one class is low (less

Dear experts, when I call the step function for a coxph model with n covariates and a dicotomous variable included as strata, the first term removed by step is always the strata variable. This is not what I want and then I do a manual step updating the model minus the least significant covariate and testing with anova, until I have minimized the model. Is there a package were this can be done? or

hie l would like to create about ten strata l have tried using sample(c(1,2),1,replace=TRUE) but all l get is a vector of 1 and 2 if i change it to sample(c(1,10),1,replace=TRUE) it gives me a vector of 1 and 10 how do l go about it --------------------------------- [[alternative HTML version deleted]]

Hello, I'm trying to sample in Random Forests by a factor, but it is a regression problem and I can't figure out how to do this (I can only see how to sample by strata in classification). Thanks Jesse Lasky

My data.frame table consist of 3 variables (x,y and z) where each variable has 1000 units. I need to create 5 equal size strata according to one of the variable (let's say x) whereas units of x variable with a higher value have higher probability to be selected in a strata with a higher number (max strata number is 5). I've been trying different things so far and since I am fairly new to

Hello: So i've fit a hazard function to a set of data using kmfit<-survfit(Surv(int, event)~factor(cohort)) this factor variable, "cohort" has four levels so naturally the strata variable has 4 values. I can use this data to estimate the hazard rate haz<-n.event/n.risk and calculate the cumulative hazard function by H<--log(haz) Now, I would like to plot this

Hi everyone, I'm doing some coxph() analyses with a large and complex dataset. The data was collected in different centers, so I am using strata(centers) to stratify the analysis. My main issue is, not all centers collected all the variables, so for a model such as: coxph(Surv(days, cancer) ~ varA + sex + strata(centers), data) I might have 1 or more centers that have NA for varA (in