similar to: Friedman test for replicated blocked data

Dear all, I am conducting a full factorial analysis. I have one factor consisting in algorithms, which I consider my treatments, and another factor made of the problems I want to solve. For each problem I obtain a response variable which is stochastic. I replicate the measure of this response value 10 times. When I apply ANOVA the assumptions do not hold, hence I must rely on non parametric

Dear all, I have a problem installing a package required by Hmisc on MacOS 10.3.5 with R 2.00. g77 -fno-common -g -O2 -c avas.f -o avas.o g77 -fno-common -g -O2 -c rlsmo.f -o rlsmo.o gcc -bundle -flat_namespace -undefined suppress -L/usr/local/lib -o acepack.so ace.o avas.o rlsmo.o -L/usr/local/lib -L/usr/local/lib/gcc/powerpc-apple-darwin6.8/3.4.2

Hallo, is there any extension of the pairwise Wilcoxon test to a dependent samples layout with replicates (or, in other terms, a one-way layout with blocking and replicates)? The Wilcoxon method with matched pairs works for the case of dependent samples with one observation per block, while the Mann-Whitney test works for independent samples, thus one single block and replicated observations. Is

Dear list, I would like to plot the empirical cumulative distribution of the time needed by a treatment to attain a certain goal. A number of experiments is run with a strict time limit. In some experiments the goal is attained before the time limit, in other experiments time expires before the goal is attained. The situation is very similar to survivial analysis with censored data. I tryed

Dear R-users, I would need to do a Friedman test with replicated samples, and Friedman.test(y...) currently works only for unreplicated designs. Is there a script or a function available? Thanks, Alessandro -- --

Hi, pchisq -> distribution function dchisq -> density function pval is the area under the curve, to calculte it you use distribution function which is the integral of density function. See: http://www.itl.nist.gov/div898/handbook/eda/section3/eda362.htm http://mathworld.wolfram.com/DistributionFunction.html f(x) density function F(x) distribution function =Pr(X<x)= integral(f(x))

Dear R helpers: Thanks for the previous reply. I am using Friedman racing test. According the the book "Pratical Nonprametric Statistic" by WJ Conover, after computing the statistics, he suggested to use chi-squared or F distribution to accept or reject null hypothesis. After looking into the source code, I found that R uses chi-sqaured distribution as below: PVAL <-

I am trying to use the friedman.test() on a data frame, d, but I am receiving the following error message: > d AW HS IAC WA 1 6 8 3 5 2 2 2 3 6 3 7 7 8 3 4 8 5 4 5 .... 20 2 5 2 7 21 7 7 6 7 22 7 8 6 8 23 6 8 4 5 24 5 7 5 2 > friedman.test(d) Error in any(is.na(groups)) : argument "groups" is missing, with no default I think I

Some attribute x from 17 individuals was recorded repeatedly on 6 time points using a Likert scale with 7 distractors. Which statistical test(s) can I apply to check whether the changes along the 6 time points were significant? set.seed( 123 ) x <- matrix( sample( 1:7, 17*6, repl=T ), nrow = 17, byrow = TRUE, dimnames = list(1:17, paste( 'T', 1:6, sep='' )) ) I found

Does rsync -aAXv /dir1 /dir2, where dir2 is a mounted FAT formatted SD card, and then: rsync -aAXv /dir2 /dir1, to restore the files in dir1 from the FAT formatted SD card, restore the ACL and owner/permissions of the files and directories in dir1? Thanks, John -- John Conover, conover at rahul.net, http://www.johncon.com/

Hi all, I have been trying to reproduce an analysis from Douglas Montgomery?s book on design and analysis of experiments. Table 6.10 of example 6.2 on page 246, gives a table as follows: > NPK <- expand.grid(A=mp,B=mp,C=mp,D=mp) > Rate <- c(45,71,48,65,68,60,80,65,43,100,45,104,75,86,70,96) > filtration <- cbind(NPK,Rate) > filtration A B C D Rate 1 - - - - 45 2

Dear all, I am trying to produce survfit plots in a trellis environment and I would like the plots to be logarithmic. I am trying this: print(Ecdf(~time | size*type, groups=alg,data=B,subscripts=TRUE, panel=function(x,groups,subscripts) { t <- survfit(Surv(time[subscripts],event[subscripts])~groups[subscripts],data=B)

I'm taking the residual-regression approach to semiparametric estimation (Robinson 1988, Econometrica), and basically using SemiPar simply as a convenient means of doing multivariate nonparamteric additive models. The final bit of code is here: finalfit <- spm(res~f(V3,basis="trunc.poly")+f(V5,basis="trunc.poly")+f(V6,basis="trunc.poly")) summary(finalfit)

Dear List, I am looking for a function `Pseudo standard error' (PSE), which is available in S+ DOX (design of experiemnt) module - Is there a similar function available in R? Reference for PSE function is in the paper: 'Quick and easy analysis of unreplicated factorials' by Russell V. Lenth, Technometrics, 1989, 31, 4, 469-473. Thanks. -Nitin

------- =_aaaaaaaaaa0 Content-Type: text/plain; charset="us-ascii" Content-ID: <14008.870179829.1@erehwon.bmc.com> See attached. Red Hat Linux package mh-6.8.3-13.i386.rpm installs the inc and msgchk programs as follows: -rwsr-sr-x- root mail 72628 Oct 17 16:57 /usr/bin/mh/inc -rwsr-xr-x- root root 52536 Oct 17 16:57 /usr/bin/mh/msgchk Hal -------

Hello, I'm having some trouble getting Opus to work in an ARM64 iOS application and I'm wondering if anyone here has any guidance. Maybe I am missing something simple. Specifically, the problem is that Opus running on ARM64 outputs different data than other architectures -- ARMv7, ARMv7s, i386, and x86_64. On all other platforms except ARM64, encoding a 1280 byte slice of audio data

Hi Guys, Here's a simple test case that causes a segfault with the perl bindings patched to enable spelling correction: use strict; use warnings; use Search::Xapian; my $db = Search::Xapian::WritableDatabase->new("test.db", Search::Xapian::DB_CREATE_OR_OPEN); if (!defined($db)) { die("Failed to open xapian_database: $!"); } my $indexer =

Hi, I'm working with the ks.test() function and I have also implemented the test using Conover as the reference. My D value matches that produced by R. However to calculate the p value I am using the code described in Numerical Recipes in C++ (2nd Ed.) pg 631. The p value produced by the NRC code is generally larger than that produced by R by a factor of 10. Currently I am not in a position

Hi, I'm using the ks.test() on two vectors. I looked up the reference and also coded up a version of the two sample Smirnov test. My question is that how can I decide from the output of R that the two vectors x & y come from the same distribution? Am I correct in assuming that smaller D values indicate that they come from the same distribution? In addition how can I use the p value that

Dear all, is there a way to produce survfit plots in a trellis environment? I am trying this: print(Ecdf(~time | size*type, groups=alg,data=B,subscripts=TRUE, panel=function(x,groups,subscripts) { t <- survfit(Surv(time[subscripts],event[subscripts])~groups[subscripts],data=B,conf.type="none")