similar to: proportional test on epicalc library vs. Jerrold H. Zar.

Dear R-friends, ? I am using the epicalc package and the manual by V. Chongsuvivatwong "Analysis of epidemiological data using R and Epicalc" to get the hang of some basic epidemiological analyses.??? ? After running all the analyses of chapter 7, one is supposed to wrap it up by saving the data writing: ? ? > save(.data, file = "Chapter7.Rdata") ? ...?after writing the

Dear R-help, I am resending as I believe I screwed up the e-mail address to R-help earlier. Sorry for my lack of attention to detail, and for any inconvenience. I have also sent the question to the package maintainer, as suggested in the posting guide. Regards, Cliff ---------- Forwarded message ---------- From: Clifford Long <gnolffilc at gmail.com> Date: Sun, Jul 26, 2009 at 8:46

Hi community, Seems I am too stupid: I try to calculate with power.t.test() an example from the statistics textbook [Jerrold H. Zar, Biostatistical Analysis, 2nd edition, Prentice Hall, 1984, Chapter 8, Exercise 8.4, (page 120)]: ------- A sample of size 18 has a mean of 13.55cm and a variance of 6.4512cm^2. (a) Calculate the 95% confidence interval for the population mean. (b) How large a

Dear R users: Suppose I have two different response variables y1, y2 that I regress separately on the different explanatory variables, x1 and x2 respectively. I need to compare points on two regression lines. These are the x and y values for each lines. x1<-c(0.5,1.0,2.5,5.0,10.0) y1<-c(204,407,1195,27404313) x2<-c(2.5,5.0,10.0,25.0) y2<-c(440,713,1520,2634) Suppose we need to

Hi everyone, I'm studying the manual name: Analysis of Epidemiological Data Using R and Epicalc, maked by: Virasakdi Chongsuvivatwong and Edward McNeil. And I can't find the data base that they use in some examples, this are the names: Chapter7.Rdata,Chapter8.Rdata,Chapter9.Rdata Somebody can tell me how can I have this files? Thk! Jos? O__ ---- Jos? Bustos M. c/ /'_ ---

Dear R-help list, I'm attempting to use the ROC routine from the epicalc package after performing a logistic regression analysis. My code is included after the sessionInfo() result. The datafile (GasketMelt1.csv) is attached. I updated both R and the epicalc packages and tried again before sending this request. sessionInfo result: R version 2.9.1 (2009-06-26) i386-pc-mingw32 locale:

Hello list, I am trying to avoid "Rifying" some of my SAS code to generate ROC plots, and the logistic.display() and lroc() functions in the epicalc package do what I want. However, I must generate my logistic model with bigglm because I have 1) limited hardware, 2) ~2.5 million rows, and 4 categorical and 2 continuous independent variables. When I attempt to invoke epicalc's

Hello all, Here goes one of my first functions. I want to make a nonparametric multiple sample comparison with unequal sample sizes (see Zar?s Biostatistical Analysis, 3rd. Ed., pg. 201 Example 10.11, pg. 288 Example 11.10). In the real world, I want to compare samples of fish length captured with different fishing gears. After using the Kruskal-Wallis test I want to check the differences

I am using nginx with unicorn as a reverse proxy. Has been fantastic, but needed to look through some code for first time. Using ruby 1.9.3p194 with rails 3.2.9, development mode with ssl enabled, I start unicorn with -d ...and it does not get served up by nginx. Any ideas, or more information needed? Thanks! Jet Below is my unicorn.rb for developent: # config/unicorn.rb env =

I am trying to do a Nonparametric Tukey-type multiple comparison post-hoc test to determine which groups are significantly different. I have read the dialogue on this topic from the R-help, and am still not clear why no statistical packages include this test as an option? Is it not an appropriate test to conduct on non-normally distributed data? Is the only option to calculate it by hand

Hi. I searched the list and didn't found nothing similar to this. I simplified my example like below: #I need calculate correlation (for example) between 2 columns classified by a third one at a data.frame, like below: #number of rows nr = 10 #the third column is to enforce that I need correlation on two variables only dataf =

Hi, I'm newbie in R package, but i'm extremely gratefull for these list! is there possible to make kruskal.test summarize the sum of ranks?? e.g ##### z<-c(1.2,3.4,0,0,0,0,0) ranks(z) # like in octave package ans= 6 7 3 3 3 3 3 sum(ranks(z)) ans= 28 ### then apply multiple comparisions for many other data SE<-sqrt(n*(n*k)*(n*k+1)/12) # according Zar,JH. Bioestatistical

I need to specify error terms properly in a mixed-effects anova model. I know you can add error terms in aov using Error and can specify random factors in lme but I am not sure how these get treated. When making the calculations for fixed and random factors, are the correct error terms used and how can you get aov or lme to use different error terms for fixed and random effects? I'm

This is only partly an R question, but this seems a good place to ask. Like many, if not most, statistics programs, R has no post hoc tests for Kruskal Wallis or Friedman, and as far as I can make out they are not available in any of the R packages. I would be grateful if someone could point me in the right direction if I am wrong. As I said this is not uncommon. Parametric Anova procedures

How I make the below matrix | a^0 0 0 ... 0 | | a^1 a^0 0 ... 0 | | a^2 a^1 a^0 ... 0 | | . | | . | | . | | a^n a^(n-1) a^(n-2) ... a^0 | with no loops, where "a" is a constant? ======================================================================== C?zar de Freitas Depto. de Estat?stica - UFPE Recife - Pernambuco - Brasil

Hi, Does anyone know if the D'agostino test is available with R ? Alex

Hi, I use the epicalc package which provides the function aggregate.numeric. Unfortunately aggregate.numeric produces warnings when aggregate is used by functions not under my control on a numeric value. If I don't load epicalc, aggregate.default is used instead by these functions and does not produce any warning. However I need epicalc. So to get around this, what I would do is firstly

Dear R users, Suppose I have two different response variables y1, y2 that I regress separately on the same explanatory variable, x; sample sizes are n1=n2. Is it legitimate to compare the regression slopes (equal variances assumed) by using lm(y~x*FACTOR), where FACTOR gets "y1" if y1 is the response, and "y2" if y2 is the response? The problem I see here is that the

Buenas noches Javier y José, Estoy en contra de usar attach(), asi que propongo la siguiente alternativa con with(): # paquete require(epicalc) # los argumentos en ... pasan de epicalc:::cc # ver ?cc para mas informacion foo <- function(var1, var2, var3, ...){ or1 <- cc(var1, var2, ...) or2 <- cc(var1, var3, ...) list(or1 = or1, or2 = or2) } # datos x <-

Hi, all. I began to migrate my R codes from Windows to Linux and surprised me with an old question. I simplified the problem and made a little test to compare times at same computer and the Linux time is worse (not so little) than Windows time: 28 vs 53 seconds. I make an example (below) to facilitate all to see the difference. I also build from source (it's my first time) a version of R to