similar to: Error when using the cat function

Hi R Users, I am going to run a multiple linear regression with around 57 independent variables. Each time I run the model with just 11 variables, the results are reasonable. With increasing the number of independent variables more than 11, the coefficients will get ?NA? in the output. Is there any limitation for the number of independent variables in multiple linear regressions in R? I attached

On Sat, Dec 17, 2016 at 6:39 PM, Mehdi Amini <mehdi.amini at apple.com> wrote: > >> On Dec 17, 2016, at 1:35 PM, Davide Italiano via llvm-dev <llvm-dev at lists.llvm.org> wrote: >> >> First of all, sorry for the long mail. >> Inspired by the excellent analysis Rui did for lld, I decided to do >> the same for llvm. >> I'm personally very

> On Dec 17, 2016, at 1:35 PM, Davide Italiano via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > First of all, sorry for the long mail. > Inspired by the excellent analysis Rui did for lld, I decided to do > the same for llvm. > I'm personally very interested in build-time for LTO configuration, > with particular attention to the time spent in the optimizer. >

#is there a way to get NA in the table of descriptive statistics instead of the function stopping Thank you in advance #data x.f <- structure(list(Site = structure(c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("BC", "HC", "RM119", "RM148", "RM179", "RM185",

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

First of all, sorry for the long mail. Inspired by the excellent analysis Rui did for lld, I decided to do the same for llvm. I'm personally very interested in build-time for LTO configuration, with particular attention to the time spent in the optimizer. Rafael did something similar back in March, so this can be considered as an update. This tries to include a more accurate high-level

> On Dec 17, 2016, at 1:35 PM, Davide Italiano via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > First of all, sorry for the long mail. > Inspired by the excellent analysis Rui did for lld, I decided to do > the same for llvm. > I'm personally very interested in build-time for LTO configuration, > with particular attention to the time spent in the optimizer. >

Hi all! I tried to perform Shapiro-Wilk test for my sample of 243 values. > Us [1] -10.4 -13.1 -12.2 38.1 -18.8 -13.3 -11.7 29.3 49.7 6.8 12.7 16.3 [13] 5.8 -0.7 -29.4 4.1 38.8 -1.4 8.8 15.6 32.9 -5.3 19.1 35.8 [25] 4.0 -1.5 0.6 -4.2 -10.0 -4.0 1.1 48.9 -21.0 -5.3 5.8 -10.8 [37] 21.9 8.2 -3.2 -3.9 -2.3 12.6 -4.7 -8.0 11.8 27.4 -9.5 -20.8 [49]

Dear R Users, Could you please help me on the following issue? I have a real large yearly data set. For each year I have 365 flow values. Some of the flow values are not known and that’s why you will see NA written in those slots. I wanted to know, is there a way that I can estimate those values? I tried approx command but it seems least helpful for the kind of issue I am up against.

Greetings, I am trying the get the standard errors of multiple comparisons using Tukey's HSD. These are not reported by the function TukeyHSD. When I apply the following code to the data, which I store as PROLE4.TXT, several unexpected things happen. First, the function TukeyHSD works for all the comparisons but the function simint doesn't. Second, after the application of na.omit

Dear list, I have a table that I would like to convert to latex for inclusion into a Sweave file. > round(ftable(prop.table(xtabs(~agemF + votcat + Type , data=work),margin=2))*100,1) Type Voiced Voiceless unaspirated Voiceless aspirated agemF votcat 18 - 24 Prevoiced 2.6 8.7

Dear R-help, First I apologize if my question is quite simple. I need add some of data in the first place my dataset, how can I do that. I have tried with rbind, but I did not succes. 0.1 3.6 0.4 0.9 rose 4.1 4.0 1.2 1.2 rose 4.4 3.2 1.9 0.5 rose 4.6 1.1 1.1 0.2 rose For example,

Dear arun and all R users, I will first of all try to simply define my issue.. I have data in the following format Year Discharge dd/mm/yyyy x .. … … … There are some NA values in the discharge which I would like to predict by using “predict command”. I cant figure out the way to write the coding for that. Could you please help me on that??? I have also ,written

Hello zfs-discuss, One of a disk started to behave strangely. Apr 11 16:07:42 thumper-9.srv sata: [ID 801593 kern.notice] NOTICE: /pci at 1,0/pci1022,7458 at 3/pci11ab,11ab at 1: Apr 11 16:07:42 thumper-9.srv port 6: device reset Apr 11 16:07:42 thumper-9.srv scsi: [ID 107833 kern.warning] WARNING: /pci at 1,0/pci1022,7458 at 3/pci11ab,11ab at 1/disk at 6,0 (sd27): Apr 11 16:07:42 thumper-9.srv

On 12/17/2016 01:35 PM, Davide Italiano via llvm-dev wrote: > First of all, sorry for the long mail. > Inspired by the excellent analysis Rui did for lld, I decided to do > the same for llvm. > I'm personally very interested in build-time for LTO configuration, > with particular attention to the time spent in the optimizer. > Rafael did something similar back in March, so

On Sat, Dec 17, 2016 at 1:35 PM, Davide Italiano via llvm-dev < llvm-dev at lists.llvm.org> wrote: > First of all, sorry for the long mail. > Inspired by the excellent analysis Rui did for lld, I decided to do > the same for llvm. > I'm personally very interested in build-time for LTO configuration, > with particular attention to the time spent in the optimizer. >

Hello All, I am new to R and am having a problem. I have the following 29 x 5 array called temp with a mode "list". Sincere Exciting Competent Sophisticated Rugged 3.5 2.5 3.8 3.5 3.0 2.0 2.5 3.5 2.5 2.3 2.8 3.0 2.3 1.5 1.8 2.0 4.0 2.8 1.5 4.5 2.5 1.5 1.8 1.3 1.3

Dear r-helpers, I can run the examples on the mcsamp help page. For example: **************************************** > M1 <- lmer (y1 ~ x + (1|group)) > (M1.sim <- mcsamp (M1)) fit using lmer, 3 chains, each with 1000 iterations (first 500 discarded) n.sims = 1500 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff beta.(Intercept)

Hi: This is a new version of trying to enable tx interrupts for virtio-net. We used to try to avoid tx interrupts and orphan packets before transmission for virtio-net. This breaks socket accounting and can lead serveral other side effects e.g: - Several other functions which depends on socket accounting can not work correctly (e.g TCP Small Queue) - No tx completion which make BQL or

Hi: This is a new version of trying to enable tx interrupts for virtio-net. We used to try to avoid tx interrupts and orphan packets before transmission for virtio-net. This breaks socket accounting and can lead serveral other side effects e.g: - Several other functions which depends on socket accounting can not work correctly (e.g TCP Small Queue) - No tx completion which make BQL or