similar to: Wikis etc.

I am trying to model data in which subjects are followed through time to determine if they fall, or do not fall. Some of the subjects fall once, some fall several times. Follow-up time varies from subject to subject. I know how to model time to the first fall (e.g. Cox Proportional Hazards, Kaplan-Meir analyses, etc.) but I am not sure how I can model the data if I include the data for those

I am using read.csv to read a CSV file (produced by saving an Excel file as a CSV file). The columns containing dates are being read as factors. Because of this, I can not compute follow-up time, i.e. Followup<-postDate-preDate. I would appreciate any suggestion that would help me read the dates as dates and thus allow me to calculate follow-up time. Thanks John John Sorkin M.D., Ph.D. Chief,

Timothy, I believe you are mistaken. Fisher's exact test give the correct answer even in the face of small expected values for the cell counts. Pearson's Chi-square approximates Fisher's exact test and can give the wrong answer when expected cell counts are low. Chi-square was developed because it is computationally "simple". Fisher's exact test, particularly with tables

I need arguments pro-S-PLUS and against SAS for a meeting I will have next week. S-Plus is (90 - 99)% compatible with R, so using S-Plus will make things much easier for everyone. But I can't use this argument. What other arguments could I use? Alberto Monteiro

Widows XP R 2.3.1 I have been trying to make a data structure that will contain both the coefficients from a linear regression along with column and row titles AND the call, i.e. myreg<-lm(y~x+y+z) whatIwant<-cbind(c(summary(myreg)$call,"",""),summary(myreg)$coefficients) Neither the statement above, nor any one of twenty variations I have tried work. I would appreciate

(1) I know that \n when used in cat, e.g. cat("\n") produces a line feed (i.e. skips to the next line). Is there any escape sequence that will go to the top of the next page? (2) I know that control L will clear the console. Is there an equivalent function or other means that can be used in R code to clear the console? Thanks, John John David Sorkin M.D., Ph.D. Chief, Biostatistics

An apparatus exists whereby a collection of balls is displaced to the top of a stack by suction. A top level (Level 1) each ball is shifted 1 unit to the left or 1 unit to the right at random with equal probability. The ball then drops down to level 2. At Level 2, each ball is again shifted 1 unit to the left or 1 unit to the right at random. The process continues for 15 levels and the balls are

R 2.1.1 Win 2k Would someone suggest a method (or methods) that can be used to determine ntile cutpoints of a vector, i.e. to determine values that can be used to divide a vector into thirds such as 0-33 centile, 34-66 centile, 67-100 centile. If for example I had a vector: 1,2,3,4,5,6,7,8,9 and wanted to divide the vector into thirds I would have cut-points of 3, and 6. Thanks, John John

I want to place four plots on a page, and I would like to have all four plots share a common title. I have tried the following code, but the title is centered over the fourth graph and not centered across all four plots. Does anyone have any suggestions? R 2.1.1 windows xp oldpar<-par(mfcol =c(1,4),ask=TRUE) plot(p,varp) plot(p,SEp) plot(p,CVp) plot(p,ppval) title(paste("P and 95%CI

??I would like to apply a function, fract, to the columns of a dataframe. I tried the following apply(data5NonEventEpochs,2,fract) but, no surprise it did not work as apply works on matrices not data frames. How can I apply a fuction to the columns of a data frame? (I can't covert data5NonEventsEpochs to a matrix as it contains character data). Thank you, John John David Sorkin M.D., Ph.D.

I am looking for a package that will allow me to choose R (a real number) that minimizes the correlation of y and x^R, i.e. find R such that corr(y,x^R) is minimized. Any suggestions for packages I might look at would be helpful. Thanks, John John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology Baltimore VA

I have written an R function that produces multiple graphs. I use par(ask=TRUE) to allow for the inspection of each graph before the next graph is drawn. I am looking for a way to recall all graphs drawn in an R session, and a method that can be used to print all the graphs at one time. I know that I could simply print each graph after I inspect the graph, but this gets tiresome if one's

R 2.2 on windows XP I have a dataset with multiple columns. Some of the columns represent independent variables, some represent dependent variables. I would like to run the same analyses on a fixed set of independent variables, changing only the dependent variable, e.g. y1-y2=x1+x2+x3 y3-y4=x1+x2+x3 y5-y6=x1+x2+x3, etc. I know I can write a function to perform the analyses, however in order to

I am having trouble selecting rows of a dataframe that will be included in a regression. I am trying to select those rows for which the variable Meno equals PRE. I have used the code below: difffitPre<-lm(data[,"diff"]~data[,"Age"]+data[,"Race"],data=data[data[,"Meno"]=="PRE",]) summary(difffitPre) The output from the summary indicates that

I am trying to drop rows of a dataframe based on values of the column PID, but my strategy is not working. I hope someoen can tell me what I am doing incorrectly. # Values of PID column > jdata[,"PID"] [1] 16608 16613 16355 16378 16371 16280 16211 16169 16025 11595 15883 15682 15617 15615 15212 14862 16539 [18] 12063 16755 16720 16400 16257 16209 16200 16144 11598 13594 15419 15589

I have a batch of data in each line of data contains three values, calcium score, age, and sex. I would like to predict calcium scores as a function of age and sex, i.e. calcium=f(age,sex). Unfortunately the calcium scorers have a very "ugly distribution". There are multiple zeros, and multiple values between 300 and 600. There are no values between zero and 300. Needless to say, the

Is there an R function that can be used to calculate a sample size for a linear regression: i.e. Given a linear regression: y=f(x,z) i.e. fit1<-lm(y~x+z) is there a function which can be passed coef(z), SE(z), and other parameters to determine a sample size based on z, SEz and other information from the regression? Thanks, John John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics

I think I am doing something wrong when I try to bootstrap R square obtained from lm. My code is included below. No matter how many times I run the simulation, I always get exactly the same result, the bias and std.error are always zero. I would think that these values should be non-zero. I would appreciate any suggestions as to what I am doing wrong, or perhaps what I fail to understand. R 2.1.0

windows XP R 2.10 As pointed out by Prof. Venables and Ripley (MASS 4th edition, p275), the results obtained from lme using method="ML" and method="REML" are often different, especially for small datasets. Is there any way to determine which method is preferable for a given set of data? Thanks, john John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics

Fellow Rers, Please forgive me if I have posted this to the wrong R list serve. Over the course of the years that I have used R and participated in this list server, I have noted a large number of questions and answers that direct people to specific packages. The multitude of packages is one of the great strengths of R. Unfortunately there is no (or at least I am not aware of) any single source