similar to: help with handling replicates before reshaping data

From: Christoph Bumiller <christoph.bumiller at speed.at> Reviewed-by: Ilia Mirkin <imirkin at alum.mit.edu> Cc: "10.2" <mesa-stable at lists.freedesktop.org> --- src/gallium/drivers/nouveau/codegen/nv50_ir_from_tgsi.cpp | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/gallium/drivers/nouveau/codegen/nv50_ir_from_tgsi.cpp

On Wed, May 21, 2014 at 2:51 PM, Emil Velikov <emil.l.velikov at gmail.com> wrote: > On 21/05/14 00:39, Ilia Mirkin wrote: >> From: Christoph Bumiller <christoph.bumiller at speed.at> >> >> Reviewed-by: Ilia Mirkin <imirkin at alum.mit.edu> >> Cc: "10.2" <mesa-stable at lists.freedesktop.org> >> --- >>

On Wed, May 21, 2014 at 3:01 PM, Emil Velikov <emil.l.velikov at gmail.com> wrote: > On 21/05/14 19:53, Ilia Mirkin wrote: >> On Wed, May 21, 2014 at 2:51 PM, Emil Velikov <emil.l.velikov at gmail.com> wrote: >>> On 21/05/14 00:39, Ilia Mirkin wrote: >>>> From: Christoph Bumiller <christoph.bumiller at speed.at> >>>> >>>>

> -----Original Message----- > From: Douglas Bates [mailto:bates at stat.wisc.edu] > Sent: Thursday, January 24, 2002 8:55 AM > To: Uwe Ligges > Cc: Brad Buchsbaum; r-help at stat.math.ethz.ch > Subject: Re: [R] Re: coding factor replicates > > Douglas Bates <bates at cs.wisc.edu> writes: > > > Uwe Ligges <ligges at statistik.uni-dortmund.de>

I want to use boot.ci to generate confidence intervals over the bootstrapped mean(s) of a group of observations (i.e. I have 10 observations and I want to know how confident I can be on the value for the mean). I don't know (or want to know) the details of bootstrapping - I just have the simplistic idea of taking samples, measuring a statistic on the sample, and getting some confidence in the

Dear R-helper, I want to plot the following-like data: x y 1 1 1 1 1 2 1 3 1 3 1 4 ...... In the plot that produced, I don't want to show the usual circles or points. Instead, I want to show the number of replicates at that point. e.g. at the position of (1,1), there are 2 obsevations, so a number '2' will be displayed in the plot. Is my narrative clear? Is there a way to make the

Hello All. I would like to repeat a multi-screen, multivariate time series plot across multiple units. Consider the following data: <r-help@stat.math.ethz.ch> UNIT TRIAL Y1 Y2 Y3 Y4 Y5 Y6 1 109 1 NA NA NA 13718 3655 15240 2 109 2 15000 6000 30000 13057 6198 21962 3 109 3 10000 10000 20000 8356 12929 27992 4 109

Dear R-users, I'm encoutering some problems with the qcc()-function. When there are more than 25 replicates per "sample" the estimate for the standard deviation becomes NA, implying that nothing is plotted (error message: "Error in plot.window(...) : need finite 'ylim' values") On the forum I found a way to work around the error message, but this does not

How do I retrieve the replicates estimates from a crosstab done using svyby? Here is an example from the help page for svyby in the package: > data(api) > dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) > rclus1<-as.svrepdesign(dclus1) > > a <- svyby(~api99, ~stype, rclus1, svymean, return.replicates=TRUE) > a$replicates NULL But, compare to > b

Hi, I have a question about filterMicroRna in AgiMicroRna package function for filtering probes in Agilent microRNA dataset. >ddPROC = filterMicroRna(ddNORM.micro, dd, control = TRUE, IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 75, limNEG = 25, makePLOT = TRUE, target.micro, verbose = TRUE) If in a dataset there are two or more sample replicates and in the step of

Hi Group, I have a data frame below. Within this data frame there are samples (columns) that are measured more than once. Samples are indicated by "idx". So "id1" is present in columns 1, 3, and 5. Not every id is repeated. I would like to create a new data frame so that the repeated ids are averaged. For example, in the new data frame, columns 1, 3, and 5 of the original

Dear R-help, I am trying to have a generic way to assess the replicates in a character vector. Say that I have the following vector: x <- c('A','B','A','C','C','B') I would like to obtain: replicates <- c(1,1,2,1,2,2) each number beeing the time we see the corresponding value in x. Any clever and generic way to obtain that? Eric --

Hello, I'm wondering what's the best way to analyse an unbalanced design with a low number of replicates. I'm not a statistician, and I'm looking for some direction for this problem. I've a 2 factor design: Factor batch with 3 levels, and factor dose within each batch with 5 levels. Dose level 1 in batch one is replicated 4 times, level 3 is replicated only 2 times. all

Dear all, I wonder if anyone can help me with specifying a right model for my analysis. I am a beginner to lme methods. I was unfortunately not able to find a solution to my problem on my own. Data structure: I have sampled monthly 6 basins during two hydrological cycles, and I have taken several (2 to 4) samples (“replicate”) for each basin and month. I’m trying to relate Shannon diversity

On 21/05/14 00:39, Ilia Mirkin wrote: > From: Christoph Bumiller <christoph.bumiller at speed.at> > > Reviewed-by: Ilia Mirkin <imirkin at alum.mit.edu> > Cc: "10.2" <mesa-stable at lists.freedesktop.org> > --- > src/gallium/drivers/nouveau/codegen/nv50_ir_from_tgsi.cpp | 6 +++++- > 1 file changed, 5 insertions(+), 1 deletion(-) > > diff

I have a dataframe with several different treatment variables, and would like to calculate the mean and standard deviation of the replicates for each day and treatment variable. It seems like it should be easy, but I've only managed to do it for one treatment at a time using subset and tapply. Here is an example dataset: > `exampledata` <- structure(list(day = c(1L, 1L, 1L, 1L, 1L,

How about this. Its not as 'swish' as Doug's response, but it might be a little simpler to think about and adapt... # show the test vector > tmp [1] A B C B C A C B A A B Levels: A B C > # create a named vector to store the number of occurences of each level > index <- numeric(length=length(levels(tmp))) > names(index) <- levels(tmp) > index A B C [1] 0 0

Hi, I am just wondering if there is an easy way to count in a numeric vector how many numbers don't have replicates. For example, a=c(1,1,2,2,3,4,5), how can I know there are three numbers (3, 4 and 5) without replicates? Thank you! Jun =====

On 21/05/14 19:53, Ilia Mirkin wrote: > On Wed, May 21, 2014 at 2:51 PM, Emil Velikov <emil.l.velikov at gmail.com> wrote: >> On 21/05/14 00:39, Ilia Mirkin wrote: >>> From: Christoph Bumiller <christoph.bumiller at speed.at> >>> >>> Reviewed-by: Ilia Mirkin <imirkin at alum.mit.edu> >>> Cc: "10.2" <mesa-stable at

Hi, I have sort of a newbie question. I've seriously put a lot of effort into how to handle simple replicates in a repeated ANOVA design, but haven't had much luck. I really liked reading "Notes on the use of R for psychology experiments and questionnaires", by Jonathan Baron and Yuelin Li ( http://www.psych.upenn.edu/~baron/rpsych/rpsych.html ) but still didn't run across