similar to: another aov results interpretation question

Hi, This question is related to the bwplot issue I reported yesterday. I have a 3 factors (2x3x2) dataset that I collapsed into a 2 factors dataset (3x2 = sizexModality). For size==small, I have 2 observations per subject (Snr), for the other sizes only 1. I reckoned that aov (and underneath, lm) might handle this as it should, since the subjects are idendified, when I do > aov(

R provides a few ways of handling missing values, a.o. in the context of an anova (aov); 2 types of exclusion, and failure. In some situations, I personally like to have missing values replaced by the mean (or the median) for the given combination of factors. A routine that does that is something like the code included below. It works, but is (of course) rather slow. It would be much quicker

Hello world, I'm trying to do an anova on data in data.set, dependent variable is a column named "dep.var", grouping variable is in a column called "indep.var", and is.factor(indep.var) is TRUE... why can't I just do aov(dep.var ~ indep.var, data = data.set)? What have I done to deserve this?! What gives? Am I missing something totlly obvious? R-base-1.0.0-1,

Hi, how to estimate a the following model in R: y(t)=beta0+beta1*x1(t)+beta2*x2(t)+...+beta5*x5(t)+beta6*y(t-1)+beta7*y(t-2)+beta8*y(t-3) 1) using "lm" : dates <- as.Date(data.df[,1]) selection<-which(dates>=as.Date("1986-1-1") & dates<=as.Date("2007-12-31")) dep <- ts(data.df[selection,c("dep")]) indep.ret1

Hi useRs, I'm experiencing something quite weird with glm() and weights, and maybe someone can explain what I'm doing wrong. I have a dataset where each row represents a single case, and I run glm(...,family="binomial") and get my coefficients. However, some of my cases have the exact same values for predictor variables, so I should be able to aggregate up my data frame and

Hello, Is there a better way to use paste such as: a = paste(colnames(list.indep)[1],colnames(list.indep)[2],colnames(list.indep)[3],colnames(list.indep)[4],colnames(list.indep)[5],sep="+") > a [1] "aa+dummy1+dummy2+bb+cc" I tried a = paste(colnames(list.indep)[1:5],sep="+") > a [1] "aa" "dummy1" "dummy2"

Hello Jean-Marc, **Resending.. not sure why subject got removed earlier** Below are the results that show test_unit_dft passes, but test_unit_mdct fails (only for nfft=480, 960, 1920) Note: Tested on BeagleboneBlack(Cortex-A8) fixed point on branch [1] ./test_unit_dft nfft=32 inverse=0,snr = 88.394372 nfft=32 inverse=1,snr = 93.896470 nfft=128 inverse=0,snr = 89.185895 nfft=128 inverse=1,snr =

Hi, Can you apply this change to the MDCT test and run it again. See if more (all) sizes pass. Given the results, I strongly suspect an overflow. Jean-Marc On 08/05/15 01:21 PM, Viswanath Puttagunta wrote: > Hello Jean-Marc, > > Below are the results that show test_unit_dft passes, but > test_unit_mdct fails (only for nfft=480, 960, 1920) > Note: Tested on

Hello R community, I have a question about the logistic regression function. Specifically, when the predictor variable has not just 0's and 1's, but also fractional values (between zero and one). I get a warning when I use the "glm(formula = ... , family = binomial(link = "logit"))" which says: "In eval(expr, envir, enclos) : non-integer #successes in a binomial

Hello all, I use arima to fit the model with fit <- arima(y, order = c(1,0,1), xreg = list.indep, include.mean = TRUE) and would like to use predict() to forecast: chn.forecast <- rep(0,times=num.record) chn.forecast[1] <- y[1] for (j in 2:num.record){ indep <- c(aa=chn.forecast[j-1], list.indep[j,2:num.indep]) # this is the newxreg in the

Hello Jean-Marc, Yep, that was it.. with your patch, test_unit_mdct passes for all nfft. So, what you do you suggest the next step here is? Regards, Vish On 8 May 2015 at 12:30, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi, > > Can you apply this change to the MDCT test and run it again. See if more > (all) sizes pass. Given the results, I strongly suspect an

Hello Jean-Marc, Below are the results that show test_unit_dft passes, but test_unit_mdct fails (only for nfft=480, 960, 1920) Note: Tested on BeagleboneBlack(Cortex-A8) fixed point on branch [1] ./test_unit_dft nfft=32 inverse=0,snr = 88.394372 nfft=32 inverse=1,snr = 93.896470 nfft=128 inverse=0,snr = 89.185895 nfft=128 inverse=1,snr = 93.537021 nfft=256 inverse=0,snr = 88.353151 nfft=256

On Wed, Feb 5, 2014 at 11:05 AM, Marcello Caramma (mcaramma) <mcaramma at cisco.com> wrote: > Hi, > > Apologies if this is a known issue, but running make on revision e3187444692195957eb66989622c7b1ad8448b06 fails one of the tests when using fixed point configuration (floating point is ok) on my linux x86. > Note that libopus1.1, as extracted from the tar ball, is OK. > >

Hello R users, I am new to R and am wondering if anyone can help me out with the following issue: I wrote a function to build ts models using different inputs, but when R displays the call for a model, I cannot tell which variables it is using because it shows the arguments instead of the real variables passed to the function. (e.g Call: lm(formula = dyn(dep ~ lag(dep, -1) + indep)) --->

Hi, Apologies if this is a known issue, but running make on revision e3187444692195957eb66989622c7b1ad8448b06 fails one of the tests when using fixed point configuration (floating point is ok) on my linux x86. Note that libopus1.1, as extracted from the tar ball, is OK. Specifically, the tests that fail are in celt/tests/test_unit_mdct: nfft=32 inverse=0,snr = 85.341197 nfft=32 inverse=1,snr =

Dear R community, I am a newbie to R and I am using lme() to analyzed a two way repeated measures ANCOVA on some data I have gathered. In producing some graphs based on the fixed effects I noticed that I get vary different results depending on how I order my levels in my factor statement (see code below). Now, I have read that different models treat data from the factor class

Hello, I'm building packages for Slackware and I've just tried to upgrade Slackware 13.37's CELT package to version 0.11.3, which apparently was released last year, but I've omitted it because it was not announced on the site. Anyway, now that I try build with the following configuration in my SlackBuild script: CFLAGS=$SLKCFLAGS -O2 \ CXXFLAGS=$SLKCFLAGS -O2 \

Dear R-users, An updated version of the energy package, energy 1.1-0, is now available on CRAN. This version has merged the dcov package (previously available from my personal web page) into energy. New functions include: dcov (distance covariance) dcor (distance correlation) DCOR (four statistics) dcov.test (distance covariance test of multivariate independence) indep.test (choice of

Dear R-users, An updated version of the energy package, energy 1.1-0, is now available on CRAN. This version has merged the dcov package (previously available from my personal web page) into energy. New functions include: dcov (distance covariance) dcor (distance correlation) DCOR (four statistics) dcov.test (distance covariance test of multivariate independence) indep.test (choice of

Actually, the SNR calculation in testenc has been broken for a while. The reason for taking the last frame was that the codec would introduce a one-frame latency. I changed that to half a frame (10 ms) a while ago and never updated testenc. Jean-Marc Le lun 01/09/2003 à 13:12, Mike Dunn a écrit : > Hi all, > > I'm new to the group. I'm looking at the speex code with an