similar to: [Bug 8183] New: rsync 3.0.8 can't modify read-only directories created on the same run

Dear All user, Hello, I'm a student and I have some trouble with the experimental (columns-experiments) design of my project. I use a randomized block design with 4 treatments including a control. For each treatment, I use 3 replicates and 3 blocks. The treatments are: -T1 = COD (300 mg/Lit) COD=chemical oxygen demand -T2 = COD (200 mg/Lit) -T3 = COD (100 mg/Lit) -T4 = COD (0 mg/Lit) as

Hi, I've noticed that LLVM tends to generate suboptimal code and spill an excessive amount of registers in large functions, such as in those that are automatically generated by FFTW. LLVM generates good code for a function that computes an 8-point complex FFT, but from 16-point upwards, icc or gcc generates much better code. Here is an example of a sequence of instructions from a 32-point

Database structure as follows: t1 -> t2 -> t3 -> t4 select * from t1; +----+-------+-------+ | id | t2_id | t3_id | +----+-------+-------+ | 1 | 1 | null | | 2 | null | 1 | | 3 | null | 2 | +----+-------+-------+ select * from t2; +----+-------+ | id | title | +----+-------+ | 1 | "hat" | +----+-------+ select * from t3; +----+-------+ | id | t4_id |

The code below is intended to analyse a textbook example of a balanced incomplete block design: # # Data taken from pp. 219-230 in # Cox, D.R. (1958) Planning of Experiments. John Wiley and Son, Inc. New York. 308 pp. # day <- factor(rep(1:10, each = 3)) T <-

Hello, I've been redirected from cfe-dev, as code optimizations in clang are done in llvm layer. I'm investigating how optimized code clang generates, and have come across such an example: I have two procedures: void exec0(const int *X, const int *Y, int *res, const int N) { int t1[N],t2[N],t3[N],t4[N],t5[N],t6[N]; for(int i = 0; i < N; i++) { t1[i] = X[i]+Y[i];

# read.table is NOT part of the data.table package #library(data.table) DFM <- read.table( text= 'obs start end 1 2/1/2015 1/1/2017 2 4/11/2010 1/1/2011 3 1/4/2006 5/3/2007 4 10/1/2007 1/1/2008 5 6/1/2011 1/1/2012 6 10/5/2004 12/1/2004 ',header = TRUE, stringsAsFactors = FALSE) # cleaner way to compute D DFM$start <- as.Date( DFM$start, format="%m/%d/%Y" ) DFM$end

Hello. I'd like to compute the associate vector of distances between leaves in a binary non-rooted tree. The definition of a distance between two leaves in a binary non-rooted tree is the number of edges in the path joining the two leaves. I've tried the ape package but I'm unable to find this vector. For example, using rtree(5,rooted=F) I've obtained the following tree: $edge

(I'm sending to r-bugs because rpart is one of the recommended packages and is always installed. I'm also sending it directly to Dr. Ripley, as the maintainer.) rpart working as a classifier does not work (produces no splits) when the class indicator has no instances of one of the factor levels, as long as the factor level is not the final level. I have at least a partial fix, which I

Ii is difficult to provide useful help, because you have failed to read and follow the posting guide. In particular: 1. Plain text, not HTML. 2. Use dput() or provide code to create your example. Text printouts such as that which you gave require some work to wrangle into into an example that we can test. Specifically: 3. Have you gone through any R tutorials?-- it sure doesn't look like

Since the number of choices is small (6), how about this? Starting with Jeff's initial DFM: DFM <- structure(list(obs = 1:6, start = structure(c(16467, 14710, 13152, 13787, 15126, 12696), class = "Date"), end = structure(c(17167, 14975, 13636, 13879, 15340, 12753), class = "Date"), D = c(700, 265, 484, 92, 214, 57), bin = structure(c(6L, 3L, 5L, 1L, 3L, 1L), .Label

Hi all, I have a data set with time interval and depending on the interval I want to create 5 more variables . Sample data below obs, Start, End 1,2/1/2015, 1/1/2017 2,4/11/2010, 1/1/2011 3,1/4/2006, 5/3/2007 4,10/1/2007, 1/1/2008 5,6/1/2011, 1/1/2012 6,10/15/2004,12/1/2004 First, I want get interval between the start date and end dates (End-start). obs, Start , end, datediff

Thank you all for the useful suggestion. I did some of my homework. library(data.table) DFM <- read.table(header=TRUE, text='obs start end 1 2/1/2015 1/1/2017 2 4/11/2010 1/1/2011 3 1/4/2006 5/3/2007 4 10/1/2007 1/1/2008 5 6/1/2011 1/1/2012 6 10/5/2004 12/1/2004',stringsAsFactors = FALSE) DFM DFM$D =as.numeric(difftime(as.Date(DFM$end,format="%m/%d/%Y"),

I have big .csv file. I would like to filter that file into a new table. For example, I have .csv file as below: f1 f2 f3 f4 f5 f6 f7 f9 f10 f11 t1 1 0 1 0 1 0 0 0 0 1 t2 1 0 0 0 0 1 1 1 1 1 t3 0 0 0 0 0 0 0 0 0 0 t4 1 0 0 0 1 0 0 0 0 0 t5 0 0 0 0 0 0 0 0 0 0 t6 0 0 0 0 0 0

What??s happening with this following code: require(sem) Celpe.Mod.RAM <- matrix(c( # path parametro Inicio "Produ????o -> T1", "gamma.11", NA, "Produ????o -> T2", "gamma.12", NA,

Apart from the fact that the statement "such that t1+t2+t3+t4=2970 (as it must)" is not correct, the LP can be implemented as follows: library(lpSolve) LHS <- rbind( c(0,0,0,0, 1, 0, 0,0), c(1,0,0,0,-1, 1, 0,0), c(0,1,0,0, 0,-1, 1,0), c(0,0,1,0, 0, 0,-1,1), cbind(-diag(4),diag(4)), c(0,0,0,0,0,1,0,0), c(0,0,0,0,0,0,1,0), c(0,0,0,0,0,0,0,1) ) RHS <-

Thank you Jeff and All, Within a given time period (say 700 days, from the start day), I am expecting measurements taken at each time interval;. In this case "0" means measurement taken, "1" not taken (stopped or opted out and " -1" don't consider that time period for that individual. This will be compared with the actual measurements taken (Observed-

Change the assembly code to use only relative references of symbols for the kernel to be PIE compatible. Position Independent Executable (PIE) support will allow to extended the KASLR randomization range below the -2G memory limit. Signed-off-by: Thomas Garnier <thgarnie at google.com> --- arch/x86/crypto/aes-x86_64-asm_64.S | 45 ++++++++----- arch/x86/crypto/aesni-intel_asm.S

I need to remove certain interactions and keep only the one between the second level of the factor and the continuous var t2 bin4 <- glm(resp2~ t*t2+c5.vrm,data=dfa,family="quasibinomial") > summary(bin4) Call: glm(formula = resp2 ~ t * t2 + c5.vrm, family = "quasibinomial", data = dfa) Deviance Residuals: Min 1Q Median 3Q Max -6.5464

Hi: I am looking for some help in making two boxplots next to each other. I have a data like this: N1 T1 N2 T2 N3 T3 N4 T4 ... Nn Tn 7 8.2 4 5 8 10 4 5 ..... 10 11 I want to have box plot for all Normal samples (N1,N2,N3,N4,,,,Nn) and another box plot for all tumors (T1,T2,T3,T4,...Tn). I have data in a numeric class. If data is represented as N1

Hi all, I hope that there might be some statistician out there to help me for a possible explanation for the following simple question. Y1~ lm(y~ t1 + t2 + t3 + t4 + t5,data=temp) # oridnary linear model library(gam) Y2~ gam(y~ lo(t1) +lo(t2) +lo(t3) +lo(t4) +lo(t5),data=temp) # additive model In the first model t1, t2 and t3 found to be significant,. However, in the second model (using