similar to: Identification of Outliners and Extraction of Samples

Hi! I'm trying to implement an outlier test once/row in a large dataframe. Ideally, I'd do this then add the Pvalue results and the number flagged as an outlier as two new separate columns to the dataframe. Grubbs outlier test requires a vector and I'm confused how to make each row of my dataframe a vector, followed by doing a Grubbs test for each row containing the vector of numbers

How can I extract the samples (S1-S5) containing a TRUE value in their row? Solution should apply to a much bigger data frame. > a Samples A B C D . . . 1 S1 FALSE FALSE FALSE FALSE 2 S2 FALSE FALSE NA TRUE 3 S3 FALSE FALSE FALSE FALSE 4 S4 FALSE TRUE FALSE FALSE 5 S5 FALSE FALSE FALSE FALSE . . . Thank you guys, I appreciate your help a lot! [[alternative HTML version deleted]]

Good Evening, I recently purchased an EPYC quantum UPS. I installed NUT on the home assistant by connecting the UPS via USB and tried all the various drivers in the list, the only one that seems to work is: usbhid-ups with this log: s6-rc: info: service s6rc-oneshot-runner: starting s6-rc: info: service s6rc-oneshot-runner successfully started s6-rc: info: service base-addon-banner: starting

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 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(

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

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

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,

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

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

Dear all, I did post this more or less identical mail in a follow up to another question I posted, but under another heading. I try again, but now under the correct header. upon running this code (from the Hmisc library-latex function) I believe the call to summary.formula is allright and produces wonderful tables, but the latex command results in a correct formatted table but where all the

Hi Berend, I see you are one of the contributors to the rbecnhmark package. I am sorry that I am bothering you again. I have tried to run your code (slightly tweaked) involving the benchmark function, and I am getting the following error message. What am I doing wrong? Error in benchmark(d1 <- s1(df), d2 <- s2(df), d3 <- s3(df), d4 <- s4(df), : could not find function

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

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

Today I attempted to upgrade to S10_U2 and migrate some mirrored UFS SVM partitions to ZFS. I used Live Upgrade to migrate from U1 to U2 and that went without a hitch on my SunBlade 2000. And the initial conversion of one side of the UFS mirrors to a ZFS pool and subsequent data migration went fine. However, when I attempted to attach the second side mirrors as a mirror of the ZFS pool, all

Hi, ABCRegister.td : def SGPR32 : RegisterClass<"ABC", [i32], 16, (add S0, S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 )>; def SFGPR32 : RegisterClass<"ABC", [f32], 16, (add S0, S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 )>; ===== Instruction selection ends: ... t8: i32 = ADDrr t37, t32

If you add bricks to existing volume one host could be down in each three host group, If you recreate the volume with one brick on each host, then two random hosts can be tolerated. Assume s1,s2,s3 are current servers and you add s4,s5,s6 and extend volume. If any two servers in each group goes down you loose data. If you chose random two host the probability you loose data will be %20 in this

On Dec 5, 2009, at 4:02 AM, Tianwei wrote: > Hi, all, > We are working on a static analysis phase on LLVM's IR, we want to do a backforward phase through the use-def chain, I knew that LLVM > had a built-in SSA form which provide the use-def chain for virtual register variables, however, I want to know if you also provide some kinds of use-def chain for memory operations? for

Hi, all, We are working on a static analysis phase on LLVM's IR, we want to do a backforward phase through the use-def chain, I knew that LLVM had a built-in SSA form which provide the use-def chain for virtual register variables, however, I want to know if you also provide some kinds of use-def chain for memory operations? for example, I have the following source code int foo(int *q, int

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