similar to: about bootstrapping

I am writing a resampling program for multiple regression using lm(). I resample the data 10,000 times, each time extracting the regression coefficients. At present I extract the individual regression coefficients using brg = lm(Newdv~Teach + Exam + Knowledge + Grade + Enroll) bcoef[i,] = brg$coef This works fine. But now I want to extract the t tests on these coefficients. I cannot

Hello, We are a group of PhD students working in the field of toxicology. Several of us have small data sets with N=10-15. Our research is mainly about the association between an exposure and an effect, so preferrably we would like to use linear regression models. However, most of the time our data do not fulfill the model assumptions for linear models ( no normality of y-varible achieved even

Hi, I am new to R (I have most of my experience in SAS and SPSS). I was wondering if anyone has used both Resampling Stats and R, and could comment on strengths/relationships. Also, I have no clue on how to do the various examples from the book "Resampling: The New Statistics" in R. Can anyone give me some possible starting points? Or websites/books? Thanks, Brandon

hello, the following questions will without doubt reveal some fundamental ignorance, but hopefully you can still help me out. I'd like to bootstrap a coefficient gained on the basis of the coefficients in a logistic regression model (the mean differences in the predicted probabilities between two groups, where each predict() operation uses as the newdata-argument a dataframe of equal size as

Hi, Please apologize if my questions sounds somewhat 'stupid' to the trained and experienced statisticians of you. Also I am not sure if I used all terms correctly, if not then corrections are welcome. I have asked myself the following question regarding bootstrapping in regression: Say for whatever reason one does not want to take the p-values for regression coefficients from the

Dear Mr. Therneau, Mr. Fox, or to whoever, who has some time... I don't find a solution to use the "survfit" function (package: survival) for a defined pattern of covariates with a Cox-Model including a time dependent interaction term. Somehow the definition of my "newdata" argument seems to be erroneous. I already googled the problem, found many persons having the

I have data like so: time datum 30 12 60 24 90 37 120 41 150 8 In addition to standard deviation, I want to measure the average of the differences in data for each time interval, i.e. average of 24-12, 37-24, 41-37, 8-41. Is there a statistical term for this task? Which package should I use please? rhelp at conference.jabber.org

Hallo Could anybody please help. I have a simple linear regression model with 5 predictors. I want to use "bootstrap residuals" to make inferences regarding beta(2)hat. After fitting the model y=b0+b1+b2+b3+b4+b5 I tried the following: mod <- lm(y ~ x1+x2+x3+x4+x5) res <- resid(mod) pred <- predict(mod) Now, I have tried boot(res, lm(res+pred ~ x1+x2+x3+x4+x5)$coef[3],

I am new to R and would like to get some practice analyzing multilevel data. I wonder if anyone can point me to a sample data set and command lines that I might replicate for a sample session. I would then compare my output with HLM output. Any help is appreciated. ------ Harold C. Doran Director of Research and Evaluation New American Schools 675 N. Washington Street, Suite 220 Alexandria,

Dear All: I have some questions about the output of coxph. Below is the input and output: ---------------------------------------- > coxph(formula = Surv(futime, fustat) ~ age + rx + ecog.ps, data = + ovarian, x = TRUE) Call: coxph(formula = Surv(futime, fustat) ~ age + rx + ecog.ps, data = ovarian, x = TRUE) coef exp(coef) se(coef) z p age 0.147 1.158

Hi all, I have created a new edit mode file for the editor JEdit (http://www.jedit.org) based on the file from Zed Shaw. The difference between his and my file is that mine can be used as a spell checker. It does not highlight all functions but provides syntax highlighting for the base package (keyword types 1 and 2) and 26 additional packages (boot, class, cluster, ctest, eda, foreign, grid,

Hi - I have a dataframe of (x,y) values. I'd like to fit an exponential curve to the data for further statistical analysis (pretty much the same functionality provided by Excel's LOGEST worksheet array function). Can someone point me to the (set of) functions/ package that is best suited to provide this functionality? Admittedly, I am a novice in the use of R statistical functions,

Hello folks, I am wondering how to do survival analysis with time-related IVs in R. For example, > > If we have time-related variables, such as the Overall Condition of 1990, 1991 etc., how can we include these variables in coxph model? > > > > If we can not use coxph model, do we need to rearrange the dataset to make it something like: > > ID time age

Dear Friends, I'm trying to learn to how to get Bootstrapped standard errors for estimated coefficients from a regression. For instance suppose I have the following model logitmodel <- glm (y~X1+X2+X3, family=binomial(link="logit")) beta <- logitmodel$coef can somebody please guide me on how to use the package boot to obtain bootstrapped SE's for the associated betas.

Hi! I came across R just a few days ago since I was looking for a toolbox for cox-regression. I?ve read "Cox Proportional-Hazards Regression for Survival Data Appendix to An R and S-PLUS Companion to Applied Regression" from John Fox. As described therein plotting survival-functions works well (plot(survfit(model))). But I?d like to do some manipulation with the survival-functions

Hi all, I need to estimate S.E. of a certain indicator. The function to compute the value of indicator contains two arguments. Can anybody tell me how to do it? Example: We have data: a <- c(1:10) b <- c(11:20) data <- data.frame(a, b) Function to compute value of the indicator: indicator <- function(X, Y) sum(X)/(sum(Y)*2) Next I need to do the

Dear List: I have a data set with over 7000 students with about 4 observations over time per student. I want to examine the within-group fits of a random sample of this group as it takes forever to compute and draw all 7000 regressions. Here is the code I have used so far. >group<-groupedData(math~year|childid, data=scores) >group.list<-lmList(group)

The PDF file on the web, which is an appendix on nonlinear regression associated with the CAR book, is very nice. When I ran through the code presented there, I found something odd. The code does a certain model in 3 ways: Vanilla NLS (using numerical differentation), Analytical derivatives (where the user supplies the derivatives) and analytical derivatives (using automatic differentiation). The

If you redefine your NAs as below to be detected as some arbitrary large number, then the code should work through. Any 5's left in your dataset can be replaced just as easily by NAs again. Not elegant, but effective. site <- c("s1", "s1", "s1", "s2","s2", "s2") pref <- c(1, 2, 3, 1, 2, 3) R1 <- c(NA, NA, 1, NA,NA,NA) R2

Greetings to all, First off, I want to thank you all for answering any nagging questions I've had over the past few days. I've been in the process of putting together A Quick and (Very) Dirty Intro to Doing Your Statistics in R (which I have posted to http://didemnid.ucdavis.edu/rtutorial.html ) in order to teach an R workshop for the graduate students in my department. This is a