similar to: [LLVMdev] 2008-01-25-ByValReadNone.c Failure

Good Morning. Below I like statement like j<-grep(".r\\b",colnames(mydata),value=TRUE); j with the \\b option which I read long time ago which Ive found useful. Are there more or these options, other than ? grep? Thanks. dstat is just my own descriptive routine. > x ?[1] "age"????????? "sleep"??????? "primary"????? "middle" ?[5]

?s 02:10 de 02/08/2024, Steven Yen escreveu: > Good Morning. Below I like statement like > > j<-grep(".r\\b",colnames(mydata),value=TRUE); j > > with the \\b option which I read long time ago which Ive found useful. > > Are there more or these options, other than ? grep? Thanks. > > dstat is just my own descriptive routine. > > > x > ?[1]

Hi: I am using a subset of the below dataset to predict PRED_SUIT for the whole dataset but I am having trouble with 'newdata'. The model was created with 153 records and want to predict for 208 records. wolf2 <- structure(list(gridcell = c(367L, 444L, 533L, 587L, 598L, 609L, 620L, 629L, 641L, 651L, 662L, 674L, 684L, 695L, 738L, 748L, 804L, 805L, 872L, 919L, 929L, 938L, 950L, 958L,

Hi, running Asterisk 1.6.2.0 and have started to see in messages: [Jan 14 05:43:43] NOTICE[29231] chan_sip.c: Peer '100' is now Lagged. (4007ms / 3000ms) [Jan 14 05:43:53] NOTICE[29231] chan_sip.c: Peer '100' is now Reachable. (9ms / 3000ms) [Jan 14 05:44:02] NOTICE[29231] chan_sip.c: Peer '100' is now Lagged. (5008ms / 3000ms) [Jan 14 05:44:12] NOTICE[29231] chan_sip.c:

Hi Bill, > As you can see in it, there are "readonly" attributes on the functions. this test uses a huge array in order to be sure that it would be passed 'byval' and not as thousands of individual integer parameters. On your machine it is passed as thousands of individual integer parameters! That is the bug. Ciao, Duncan.

On Feb 19, 2008, at 1:15 AM, Duncan Sands wrote: > Hi Bill, > >> As you can see in it, there are "readonly" attributes on the >> functions. > > this test uses a huge array in order to be sure that it would be > passed > 'byval' and not as thousands of individual integer parameters. On > your > machine it is passed as thousands of

Hi, In my analysis of impacts of insecticide-treated bednets on malaria, I look at the relationship between malaria incidence and mosquito behaviors. The condensed data set is copied here. Ordinary regression (lm) shows that Incidence was negatively related to Mortality. This makes sense because the latter reflected the strength of killing mosquitoes by insecticide-treated nets. Since the

Sadly you failed to set your email program to send plain text and the data is corrupted at my end. I also think you need to reduce the size of the data set... the intent here is to increase your understanding, not debug your particular analysis. I will say that I am having a very challenging time understanding what you are trying to accomplish though. What are the equations that you think need

OK, Let's try this again! Here is the reproducible script; it is long because I had to copy the panel dataset here. My question is related to systemfit; I don't know how to get the result for the entire panel. #Reproducible script Empdata<- read.csv("/Users/ngwinuiazenui/Documents/UPLOADemp.csv") View(Empdata) install.packages("systemfit")

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Hi, I am trying to make a multi-class classification tree by using rpart. I used MASS package'd data: fgl to test and it works well. However, when I used my small-sampled data as below, the program seems to take forever. I am not sure if it is due to slowness or there is something wrong with my codes or data manipulation. Please be advised ! The data is described as the output from str()

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Dear R People: I have the following situation. I have observations that are 128 samples per second, which is fine. I want to fit them with ARIMA models, also fine. My question is, please: when I do my forecasting, do I need to do anything special to the "n.ahead" parm, please? Here is the initial setup: > xx <- ts(rnorm(128),start=0,freq=128) > str(xx) Time-Series

Suharto, If you're interested in performance with subscripting, you might want to look at pqR (pqR-project.org). It has some substantial performance improvements for subscripting over R Core versions. This is especially true for the current development version of pqR (probably leading to a new release in about a month). You can look at a somewhat-stable snapshot of recent pqR development

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

Dear All, I have two factors: GROUP and PATIENT, where PATIENT is nested within GROUP. >levels(example$GROUP) [1] "0" "1" "2" "3" "4" > levels(example$PATIENT) [1] "1" "2" "3" There are three observations at each combination of these factors. However, there are no observations for PATIENT = 3 and GROUP

Hi Renato, you are right! There is 'avx' support: fpu vme de pse tsc msr pae mce cx8 apic mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good xtopology nonstop_tsc aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 cx16 xtpr pdcm dca sse4_1 sse4_2 x2apic popcnt aes xsave

I looked more into this. For the previously sent IR the vector width of 256 bit is found mistakenly (and reproducibly) on this hardware: model name : Intel(R) Xeon(R) CPU E5-2650 0 @ 2.00GHz For the same IR the loop vectorizer finds the correct vector width (128 bit) on: model name : Intel(R) Xeon(R) CPU E5630 @ 2.53GHz model name : Intel(R) Core(TM) i7 CPU M 640 @

Hi Frank, I'm not an Intel expert, but it seems that your Xeon E5 supports AVX, which does have 256-bit vectors. The other two only supports SSE instructions, which are only 128-bit long. cheers, --renato On 10 November 2013 06:05, Frank Winter <fwinter at jlab.org> wrote: > I looked more into this. For the previously sent IR the vector width of > 256 bit is found mistakenly

Dear all, I'm quite new in R and especially with linear mixed effects models and I'm not completely sure to read the lmer table in the right way. for example: head(march.f) fam subjID Cond Code reg total first second log.total log.second cat 3 f 30 an fDan1 3 1.2304688 0.6679688 0.56250000 0.20739519 0.44628710 f