similar to: f-test

Hello, I have a R-script, R1, that contains some variables and some R-functions. I want to include/require this script into another R-script, R2, so that I can access the the variables and functions that are inside script R1 in script R2. Is this possible? Script R1 could for example contain information about database connections, paths etc. that would be used in several R scripts. I do not

I performed an aov() analysis and got the following results: Df Sum Sq Mean Sq F value Pr(>F) block 1 0.0040 0.0040 0.3282 0.5672 Residuals 269 3.2766 0.0122 Can anyone tell me how to extract the F value column and Pr(>F) column from the summary output of aov analysis? Many thanks in advance, Liqing Eco. Evol. Biol. UCIrvine

Dear R-users, My question is how to get right F tests for random effects in random effect models (I hope this question has not been answered too many times yet - I didn't find an answer in rhelp archives). My data are in mca2 (enc.) : names(mca2) [1] "Lignee" "Pollinisateur" "Rendement" dim(mca2) [1] 100 3 replications(Rendement ~ Lignee *

I read somewhere that t.test is equivalent to a hypothesis testing for one way ANOVA. But I'm wondering how they are equivalent. In the following code, the p-value by t.test() is not the same from the value in the last command. Could somebody let me know where I am wrong? > set.seed(0) > N1=10 > N2=10 > x=rnorm(N1) > y=rnorm(N2) > t.test(x,y) Welch Two Sample t-test data:

Dear R-users, Sorry for reposting. I put it in another way : I want to test random effects in this random effect model : Rendement ~ Pollinisateur (random) + Lignee (random) + Pollinisateur:Lignee (random) Of course : summary(aov(Rendement ~ Pollinisateur * Lignee, data = mca2)) gives wrong tests for random effects. But : summary(aov1 <- aov(Rendement ~ Error(Pollinisateur * Lignee), data =

Dear list, I have a problem with nested functions and I don't manage to get it solved. I know I should be looking in environments, and I have tried a lot, but it keeps on erroring. An easy version of the problem is as follows: innerfunction<-function() { print(paste(a, " from inner function")) print(paste(b, " from inner function")) setwd(wd) }

Hello all I'm trying to learn how to fit a nested model in R. I found a toy example on internet where a dataset that have?3 areas and 4 sites within these areas. When I use Minitab to fit a nested model to this data, this is the ANOVA table that I got: Nested ANOVA: y versus areas, sites Analysis of Variance for y Source DF SS MS F P areas 2 4.5000 2.2500

Any votes for/against? ----- Forwarded message from Rik Faith <faith@alephnull.com> ----- Date: Wed, 28 Nov 2001 12:55:29 -0500 From: Rik Faith <faith@alephnull.com> To: mbp@samba.org Subject: rsync patch X-Mailer: VM 6.96; XEmacs 21.1; Linux 2.4.16 (light) Here is a patch that adds rate information (e.g., kB/s) to the --progress display. I just noticed that 2.4.7pre4 is coming

<ad> .q.u.e.l.c.h is an audio player dedicated to Ogg Vorbis. This is a bugfix release. RPMs available for i386, i486, i586, i686 and Athlon. Source available as tar.gz, tar.bz2 and src.rpm. http://www.geoid.clara.net/rik/squelch.html Tested on Linux, may work elsewhere. Requires only Vorbis, libao and Qt (compiled with thread support.) </ad> Have fun ;) Rik --- >8 ----

Hi R users! I have the following problem: how appropriate is my aov model under the violation of anova assumptions? Example: a<-c(1,1,1,1,1,1,1,1,1,1,2,2,2,3,3,3,3,3,3,3) b<-c(101,1010,200,300,400, 202, 121, 234, 55,555,66,76,88,34,239, 30, 40, 50,50,60) z<-data.frame(a, b) fligner.test(z$b, factor(z$a)) aov(z$b~factor(z$a))->ll TukeyHSD(ll) Now from the aov i found that my model

Hi, On 2018-03-07 16:35, Ondrej Valousek wrote: > Why do you need to replace your existing solution? > If you don't need to scale out due to the capacity reasons, the async > NFS server will always outperform GlusterFS The current solution is 8 years old and is reaching its end of life. The reason we are also looking into gluster is that we like that it uses standard components

I've made squelch-1.0beta5 and uploaded it - you can find it linked from http://www.geoid.clara.net/rik/squelch.html This version has a config dialog. It works out which output drivers libao has available and gives you a choice. You can also tell it where your 'audio dir' is - though that works a bit strangely at the moment - when you change it, all the files you have in your playlist

Hi Rik, Nice clarity and detail in the description. Thanks! inline... On Wed, Mar 7, 2018 at 8:29 PM, Rik Theys <Rik.Theys at esat.kuleuven.be> wrote: > Hi, > > We are looking into replacing our current storage solution and are > evaluating gluster for this purpose. Our current solution uses a SAN > with two servers attached that serve samba and NFS 4. Clients connect to

Hi, i'm looking for an R function to fit a one-way ANOVA with one factor containing 10 levels. The factor levels have different numbers of observations (varying between 20 to 40). For most of the dependent variables i'm testing there are unequal variances among the factor levels. I see the function oneway.test: oneway.test(variable ~ factor, data=dataset) which by default does not

Dear all, I am studying a dataset using the aov() function. The independant variable 'cds' is a factor() with 8 levels and here is the result in studying the dependant variable 'rta' with aov() : > summary(aov(rta ~ cds)) Df Sum Sq Mean Sq F value Pr(>F) cds 7 0.34713 0.04959 2.3807 0.02777 Residuals 92 1.91635 0.02083 The dependant variable

I'm attempting to get a Linux box to authenticate users from NT. I think it's possible by writing a PAM module (not hard) and using part of Samba's source to verify the password on the NT server. I'm getting pretty bogged down trying to work out how to do this - it seems like it should be simple - just take the password and username and ask the NT server if it's correct. If

Hello, I'm new to R and have been following many guides including the two-way anova (http://www.personality-project.org/r/r.anova.html). Using that walkthrough including the supplied data I do str(data.ex2) and receive the appropriate types of data as follows: > str(data.ex2) 'data.frame': 16 obs. of 4 variables: $ Observation: int 1 2 3 4 5 6 7 8 9 10 ... $ Gender :

Greetings to all, This is my model: aov.fit<-aov(Y~A+B+C+D+E+A:C+A:E) In summary(aov.fit) all F values are comptuted by eg MS(A)/MS(Residuals). This is not correct (or what I want), except for F(B) and F(A:E). I suppose P values are not correct either. Is it possible with aov to define the way F computations will be done? I 'd like them to be like this: F(A)=MS(A)/MS(E),

While digging through the time code, I found something very strange in do_settime: x = (secs * 1000000000ULL) + (u64)nsecs - system_time_base; y = do_div(x, 1000000000); spin_lock(&wc_lock); wc_sec = _wc_sec = (u32)x; wc_nsec = _wc_nsec = (u32)y; spin_unlock(&wc_lock); The value "x" appears to be the number of nanoseconds, while the value

Bert, thanks for the reply but I feel that my question is less about statistics and more about R interface. Specifically, because the output of R seems different than other programs (systat, for example, gives a between and a within table instead of a three level one). I am familiar with the connection between mixed models and repeated measures,and how mixed models are essentially replacing the