similar to: Two apparent bugs in aov(y~ *** -1 + Error(***)), with suggested (PR#6510)

I believe you are right, but can you please explain why anyone would want to fit this model? It differs only in the coding from aov(y ~ a + b + Error(c), data=test.df) and merely lumps together the top two strata. There is a much simpler fix: in the line if(intercept) nmstrata <- c("(Intercept)", nmstrata) remove the condition (and drop the empty stratum later if you

In a previous message I was not clear enough in my querry. I have the following program: tst<- function() { x <- c(32.7,32.3,31.5,32.1,29.7,29.1,35.7,35.9,33.1, 36.0,34.2,31.2,31.8,28.0,29.2,38.2,37.8,31.9, 32.5,31.1,29.7) g <- rep(1:7,rep(3,7)) s <- rep(1:3,7) cat(" Only x and g \n") aov1(x,g) cat("\n\n Now x, g and s \n") aov1(x,g,s=s) }

predict.lm(..., type="terms") gives wrong standard errors. Below, I have provided what I believe are the necessary fixes. However, there are subtleties, and the code needs careful checking. Some of the looping is surely not necessary, but it is surely best to begin with the minimum necessary changes. My tests, including checks against S-PLUS, have extended to fitting spline curves. I

>From e980153 Tue Apr 25 14:42:27 2000 To: r-help@stat.math.ethz.ch Subject: Wrong SEs in predict.lm(..., type="terms") For what it is worth, I am using RW-1.0.0 under Windows 98. I submitted this earlier to r-help. There is one change below to my proposed corrected code: predict.lm(..., type="terms") gives wrong standard errors. Below, I have provided what I believe are

Would someone be so kind as to explain in English what the ANOVA code (anova.lm) is doing? I am having a hard time reconciling what the text books have as a brute force regression and the formula algorithm in 'R'. Specifically I see: p <- object$rank if (p > 0L) { p1 <- 1L:p comp <- object$effects[p1] asgn <-

On Sat, 12 Aug 2006, takahashi kohske wrote: > Dear list members: > > I'd like to one-way repeated measured anova by using mlm. > I'm using R-2.3.1 and my code is: > > dat<-matrix( c(9,7,8,8,12,11,8,13, 6,5,6,3,6,7,10,9, > 10,13,8,13,12,14,14,16, 9,11,13,14,16,12,15,14), > ncol=4, dimname=list(s=1:8, c=1:4)) >

Full_Name: Yves Rosseel Version: 2.2.1 OS: i686-pc-linux-gnu Submission from: (NULL) (157.193.116.152) Dear developers, For the single-model case, the anova.mlm() function does not seem to handle multi-parameter predictors (eg factors) correctly. A toy example illustrates the problem: Y <- cbind(rnorm(100),rnorm(100),rnorm(100)) A <- factor(rep(c(1,2,3,4), each=25)) fit <- lm(Y ~ A)

On 14/08/16 12:20, Anand Buddhdev wrote: Hi folks, I've discovered something. See below: > The packet rate is also not that high. From the sending side, this is > what I have: > > # tcpreplay -i qtx:p1p1 5min.pcap If I send packets without qtx, like this: tcpreplay -i p1p1 5min.pcap then tcpdump on the receiving box has no problem, and keeps up happily with the queries, and

>>>>> Patrick Perry <pperry at stern.nyu.edu> >>>>> on Wed, 27 May 2015 23:19:09 -0400 writes: {@PP, you forgot this part:} >>>>> peter dalgaard <pdalgd at gmail.com> >>>>> on Thu, 21 May 2015 14:36:03 +0200 writes: >> I suspect that what we really need is >> >> fitI <- lm(x ~

On 6/23/05, RenE J.V. Bertin <rjvbertin at gmail.com> wrote: > Hello, > > I was just having a look at the aov function source code, and see that when the model used does not have an Error term, Helmert contrasts are imposed: > > if (is.null(indError)) { > ... > } > else { > opcons <- options("contrasts") >

Hey, I have stacked a couple of garchFit objects in a list with names $fit1, $fit2, ..., $fiti assigning objects names using a loop, i.e. after running the loop modelStack = list($fit1, $fit2,...,$fiti). Thus the following apply; a = modelStack$fit2, then a is the second garchFit object of formal class 'fGarch' with 11 slots, @call, @formula... etc. I then want to extract information in

On 21 May 2015, at 12:49 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: >>>>>> peter dalgaard <pdalgd at gmail.com> >>>>>> on Thu, 21 May 2015 11:03:05 +0200 writes: > >> On 21 May 2015, at 10:35 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: > >>>> >>>> I noticed that

Prof Brian Ripley <ripley@stats.ox.ac.uk> writes: > I believe you are right, but can you please explain why anyone would want= =20 > to fit this model? When analysing data from mixture designs, the variables add up to a constant, and it is often preferrable to fit models without intercept term. We often have experiments where a mixture design (typically raw materials) is combined

Hi, I have a problem with aov(). I used it many times, but now I have new data, tried to use it in the same way. I get I strange Error message that i can't understand (see below). I guess it is caused by incompatible data types. The dataframe also looks ok to me. I use: spk

I found a bug in one of the fourteen speed patches I posted, namely in patch-vec-subset. I've fixed this (I now see one does need to duplicate index vectors sometimes, though one can avoid it most of the time). I also split this patch in two, since it really has two different and independent parts. The patch-vec-subset patch now has only some straightforward (locally-checkable) speedups for

Hi folks, I've got a Dell R320 running CentOS 7, and a 10G NIC. I'm running a DNS server on it, for testing. As part of my testing, I'm attempting to capture all the DNS queries arriving on the server, using tcpdump. However, tcpdump's performance is abysmal, and it loses lots of the packets. Here's the output when I stop the capture: # tcpdump -nn -i p1p1 -w

Hi guys. I downloaded the xiph-qt-win32-0.1.5.zip file from your website. I unzipped and placed the .qtx file in C:\Program Files\Quicktime\QTCompenents. It doesn't install anything. There's no .exe file for it, and even if there shouldn't be, iTunes won't play .FLAC files or even .ogg files. Am I missing something here? I can't see an installation process. Is it possible to

Hi, I'm interested in doing a repeated measures anova using aov. The procedure is nicely described in section 6.7.1, pp. 24-27 of Baron and Li's "Notes on the use of R for psychology experiments and questionnaires," and I've reproduced their example exactly. My own problem is almost identical to theirs: rawdat<-c(1.6530074e+001, 1.2124254e+001, 1.0040371e+001,

Finally something is happening with the Ogg Vorbis QuickTime plugin. Today a post on the projects forum enlightened the future for Ogg Vorbis on the mac platform. “publius” wrote this: “Some profiling of oggvorbis.qtx indicated that the lion's share of CPU time was spent inside, of all places, QuickTime itself - but being called from BeginMediaEdits. The BeginMediaEdits/EndMediaEdits

(R-1.3.0 on linux, alpha and intel; also tested on R-1.1.1 on irix.) Below is a program that creates some random data (n, x, and y), creates a factor out of x and y and then creates a factor z out of their interaction (corresponding, with the default nf = 2, to quadrants, which is how I came upon this). It then runs an analysis of variance. f.test.problem <- function(n = 100, nf = 2){ t1