similar to: TRUE FALSE issue

Using R, I would like to find out which Samples (S1, S2, S3, S4, S5) fulfill the following criteria:contain minimally one value (x, y or z) bigger than 4. Any ideas? Thanks, Alex. > data Sample x y z 1 S1 -0.3 5.3 2.5 2 S2 0.4 0.2 -1.2 3 S3 1.2 -0.6 3.2 4 S4 4.3 0.7 5.7 5 S5 2.4 4.3 2.3

Hello everybody, I need to know which samples (S1-S6) contain a value that is bigger than the median + five standard deviations of the column he is in. This is just an example. Command should be applied to a data frame wich is a lot bigger (over 100 columns). Any solutions? Thank you very much for your help!!! > s Samples A B C E 1 S1 1 2 3 7 2

Hi Jean-Marc, Thanks a lot for reviewing this huge assembly function! silk_warped_autocorrelation_FIX_c()'s kernel part is for( n = 0; n < length; n++ ) { tmp1_QS = silk_LSHIFT32( (opus_int32)input[ n ], QS ); /* Loop over allpass sections */ for( i = 0; i < order; i++ ) { /* Output of allpass section */ tmp2_QS = silk_SMLAWB(

This is a great idea. But the order (psEncC->shapingLPCOrder) can be configured to 12, 14, 16, 20 and 24 according to complexity parameter. It's hard to get a universal function to handle all these orders efficiently. Any suggestions? Thanks, Linfeng On Mon, Feb 6, 2017 at 12:40 PM, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi Linfeng, > > On 06/02/17 02:51 PM,

Hi Jean-Marc, Thanks for your suggestions. Will get back to you once we have some updates. Linfeng On Mon, Feb 6, 2017 at 5:47 PM, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi Linfeng, > > On 06/02/17 07:18 PM, Linfeng Zhang wrote: > > This is a great idea. But the order (psEncC->shapingLPCOrder) can be > > configured to 12, 14, 16, 20 and 24 according to

Sorry to bother you kind folks again with my questions. I am trying to learn as much as I can about all this, and I will admit that I don't have the proper background, but I hope that someone can at least point me in the correct direction. I have created a test matrix for what I want to do: s1 s2 s3 s4 s5 s1 10 5 0 8 7 s2 5 10 0 0 5 s3 0 0 10 0 0 s4 8 0 0 10 0 s5 7

I attached a new patch with small cleanup (disassembly is identical as the last patch). We have done the same internal testing as usual. Also, attached 2 failed temporary versions which try to reduce code size (just for code review reference purpose). The new patch of silk_warped_autocorrelation_FIX_neon() has a code size of 3,228 bytes (with gcc). smaller_slower.c has a code size of 2,304

Hi, I'm a new R user, and struggeling with the basics (...sigh) I would like to combine 2 vectors : d1: "S1" "S2" "S3" "S4" 1 3 5 6 and d2: "S1" "S3" "S5" 3 3 2 to give a result: "S1" "S2" "S3" "S4" "S5" 1

Thank Jean-Marc! The speedup percentages are all relative to the entire encoder. Comparing to master, this optimization patch speeds up fixed-point SILK encoder on NEON as following: Complexity 5: 6.1% Complexity 6: 5.8% Complexity 8: 5.5% Complexity 10: 4.0% when testing on an Acer Chromebook, ARMv7 Processor rev 3 (v7l), CPU max MHz: 2116.5 Thanks, Linfeng On Wed, Apr 5, 2017 at 11:02 AM,

In my undertstanding of the problem, the model lme1 <- lme(resp~fact1*fact2, random=~1|subj) should be ok, providing that variances are homogenous both between & within subjects. The function will sort out which factors & interactions are to be compared within subjects, & which between subjects. The problem with df's arises (for lme() in nlme, but not in lme4), when

Hi all, i had a replica 2 gluster 3.12 between S1 and S2 (1 brick per node) geo-replicated to S5 where both S1 and S2 were visible in the geo-replication status and S2 "active" while S1 "passive". I had to replace S1 with S3, so I did an "add-brick replica 3 S3" and then "remove-brick replica 2 S1". Now I have again a replica 2 gluster between S3 and S2

The following dummy data frame has factor Q (with 2 levels) nesting factor P (with levels p1 and p2 nested under q1, and p3 and p4 nested under q2), but both crossing the random variate s, which has 8 levels. The dependent measure is dv. > # The data frame: > testnest dv s P Q 1 1 s1 p1 q1 2 2 s2 p1 q1 3 1 s3 p1 q1 4 2 s4 p1 q1 5 1 s5 p1 q1 6 3 s6 p1 q1 7 3 s7

Hi, Attached is a patch with arm neon optimizations for silk_warped_autocorrelation_FIX(). Please review. Thanks, Felicia -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.xiph.org/pipermail/opus/attachments/20170131/9a912bb4/attachment-0001.html> -------------- next part -------------- A non-text attachment was scrubbed... Name:

Hello R-friends, i have a question to the "text"-function. a little test-dataset for better understanding: -the dataset was imported with read.table(....,header=TRUE) s1-s10 are the samplenames var1 var2 var3 s1 1 1 2 s2 2 3 1 s3 2 2 3 s4 5 4 3 s5 4 2 3 s6 6 3 2 s7 8 5 4 s8 7 2 1 s9 9 3 2

Hi, I need to subset different levels of vector in a dataset to create a new dataframe that contains only these. These observations are not numerical, so I can't use the subset() function (at least this is the response I get from R). Suppose the dataframe looks like this:   ParticipID    ERP   Electrode 1         s1  0.0370       FP1 2         s2 35.0654       FP2 3         s3

Dear R users: I have some difficulties analizing data with mixed effects NLME and the last version of R. More concretely, I have a repeated measures design with a single group and 2 experimental factors (say A and B) and my interest is to compare additive and nonadditive models. suj rv A B 1 s1 4 a1 b1 2 s1 5 a1 b2 3 s1 7 a1 b3 4 s1 1 a2

Hello, me again. I have a data frame that looks like this (actual dput output at bottom): > head(tencor) date lot wf.id s1 s2 s3 s4 s5 1 08.05.2012 W0X3H0 9 1238 1263 1244 1200 1183 2 08.05.2012 W0X3H0 10 1367 1396 1371 1325 1311 3 08.05.2012 W0X3H0 11 1383 1417 1393 1346 1328 I'd like to add a column to this that gives, for each row, the averages of the

Dear ExpeRts, Suppose I have a typical psychological experiment that is a within-subjects design with multiple crossed variables and a continuous response variable. Subjects are considered a random effect. So I could model > aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2)) However, this only holds for orthogonal designs with equal numbers of observation and no missing values.

Hi, When S3 is added to master volume from new node, the following cmd should be run to generate and distribute ssh keys 1. Generate ssh keys from new node #gluster system:: execute gsec_create 2. Push those ssh keys of new node to slave #gluster vol geo-rep <mastervol> <slavehost>::<slavevol> create push-pem force 3. Stop and start geo-rep But note that

Hi guys: I have just installed gluster on a single instance, and the command: gluster volume create gv0 replica 2 server.n1:/export/brick1 server.n1:/export/brick2 returns with: "Failed to perform brick order check... do you want to continue ..? y/N"? What is the meaning of this error message, and why does brick order matter? -- Jay Vyas http://jayunit100.blogspot.com