similar to: Sum of Probabilities in a matrix...

Hello everyone, I have a 2 x 5 matrix: say 0.2 0.3 1 -1 3 0.2. 0.4 5 0.5 -1 I want to replace all the values greater than or equal to 1 with 1 and those less than or equal to 0 with 0. So I should end up with a mtrix looking like: 0.2 0.3 1 0 1 0.2. 0.4 1 0.5 0 Any ideas how to do this? -- Thanks, Jim. [[alternative HTML version deleted]]

Hello everyone,           I have a basic question regarding logical operators. > x<-seq(-1,1,by=0.02) > x   [1] -1.00 -0.98 -0.96 -0.94 -0.92 -0.90 -0.88 -0.86 -0.84 -0.82 -0.80 -0.78  [13] -0.76 -0.74 -0.72 -0.70 -0.68 -0.66 -0.64 -0.62 -0.60 -0.58 -0.56 -0.54  [25] -0.52 -0.50 -0.48 -0.46 -0.44 -0.42 -0.40 -0.38 -0.36 -0.34 -0.32 -0.30  [37] -0.28 -0.26 -0.24 -0.22 -0.20 -0.18 -0.16

Hi all, I have the following data: rep1_treat rep2_treat rep1_control rep2_control 2 3 4 5 100 20 98 54 0 1 2 3 23 32 27

Hello, all... I've been playing with the TTR package and quantmod, and I'm loading the Chicago Board of Exchange put/call ratio data via a simple read.csv call... CBOEtotal<-read.csv(file=" http://www.cboe.com/publish/ScheduledTask/MktData/datahouse/totalpc.csv ",skip=1) this gives me a data frame with columns.... > names(CBOEtotal) [1] "Trade_date"

Dear List, This is just a programming problem which i cannot seem to figure out. I am trying to get a set of power from a test (say, kolmogorov smirnov) out of a distribution (say, G-K distribution) as follows. I am trying to reduce to pain of writing the whole set of data points (p# below) using "for" loop. However, I seem to have some problem in it as the output "M" does not

Dear list, I have observed a weird behaviour from R --- apologies if I am missing something obvious! df3f826f28 df3f826f28 Say you type in R: >c.preec <- 10074 >c.gd <- 2200 >p1 <- .2 >c.neo <- p1*9451 + (1-p1)*3883 >n.preec <- 3710 >n.gd <- 2650 >n.neo <- 2120 >n.pcos <- 53000 >unit.met <- 94 >cost.met <- 94*n.pcos >effect <-

Full_Name: Anne CORI Version: 2.6.1 OS: windows Submission from: (NULL) (134.157.220.19) let A be a mtrix with n lines and p columns, p>36 ; matplot(A) will only draw the plot of the first 36 columns of A. This is due to a bug in the definition of pch in the function : pch <- c(paste(c(1:9, 0)), letters)[1:k] should be replaced by pch <- c(paste(c(1:9, 0)), letters) I hope this

Hi all, I have a vector xm say: xm = c(1,2,3,4,5,5,5,6,6) I want to return a vector with the corresponding probabilities based on the amount of times the numbers occurred. For example, I should get the following vector for xm: prob.xm = c(1/9, 1/9, 1/9, 1/9, 3/9, 3/9, 3/9, 2/9, 2/9) Any help greatly appreciated. -- Thanks, Jim. [[alternative HTML version deleted]]

Hi all, I'm handling massive data.frames and matrices in R (30000 x 400). In the 1st column, say, I have 0s and 1s indicating rows that matter; other columns have probability values. One simple task I would like to do would be to get the column mean values for signaled rows (the ones with 1) As a very fresh "programmer" I have build a simple function in R which should not be very

Greetings.? My wife is teaching an introductory stat class at UC Davis.? The class emphasizes the use of simulations, rather than mathematics, to get insight into statistics, and R is the mandated tool.?? A student in the class recently inquired about different approaches to sampling from a binomial distribution.? I've appended some code that exhibits the idea, the gist of which is that using

dear R users, I have fit the lm() on a mtrix of responses. i.e M1 = lm(cbind(R1,R2)~ X+Y+0). When i use summary(M1), it shows details for R1 and R2 separately. Now i want to use stepAIC on these models. But when i use stepAIC(M1) an error message comes saying that dropterm.mlm is not implemented. What is the way out to use stepAIC in such cases. regards,

Hi there, Here is a minimum working example: ---------------------------------------------------------------- lower = 0 upper = 1 n_bins = 50 interval = (upper - lower) / n_bins bins = vector(mode="numeric", length=n_bins) breaks = seq(from=lower + interval, to=upper, by=interval) for(idx in breaks) { bins[idx / interval] = idx } print(bins)

Duncan, Here are the complete benchmarks rerun against gcc 4.5.4 built with... Using built-in specs. COLLECT_GCC=gfortran-fsf-4.5 COLLECT_LTO_WRAPPER=/sw/lib/gcc4.5/libexec/gcc/x86_64-apple-darwin11.0.0/4.5.4/lto-wrapper Target: x86_64-apple-darwin11.0.0 Configured with: ../gcc-4.5.4/configure --prefix=/sw --prefix=/sw/lib/gcc4.5 --mandir=/sw/share/man --infodir=/sw/lib/gcc4.5/info

I have the following data which I tried to draw the probability density plot. Here is the code I have: x <- read.table("mydat.txt"); d <- rep(x$V2,times=x$V3); hist(d,probability=T, xlab="FlowSignal"); But why the y-axis range from 0 to 6, instead of 0 to 1? What's the correct way to plot it? #id flowsignal frequency 1 0.67 1 1 0.70 1 1

On Thu, Jun 09, 2011 at 03:44:40PM +0200, Duncan Sands wrote: > Hi Jack, thanks for doing this. > >> Below are the tabulated compile times and executable sizes. >> >> A) gcc 4.5.4svn using -msse3 -ffast-math -O3 -fno-tree-vectorize >> B) gcc 4.5.4svn/dragonegg using -msse3 -ffast-math -O3 -fno-tree-vectorize -fplugin-arg-dragonegg-enable-gcc-optzns >> C)

Maybe this is dumb, but I seem to be having problems reading a file saved in R v. 0.99 into R 0.90.1 (in a different machine). I did >save(test1, file="test1.RData") then I tried to read that data file in R 0.90.1: >load("test1.RData") Error in load("test1.RData") : restore file corrupted -- no data loaded More info: - both machines are running Linux;

Dear all, I have looked for an answer for a couple of days, but can't come with any solution. I have a set of functions, say: > t0 <- function(x) {1} > t1 <- function(x) {x} > t2 <- function(x) {x^2} > t3 <- function(x) {x^3} I would like to find a way to add up the previous 4 functions and obtain a new function: > rrr <- function(x) {1+x+x^2+x^3} without,

Hi all, If I run LDA on the same data (2-class classification) with default(no priors specified in the lda function) vs. "prior=c(0.5, 0.5)", the results are different. The (0.5, 0.5) priors give better 1-classify-to-1 rate, and the proportional priors(default, no priors specified in the lda function) give better 0-classify-to-0 rate, for both training and testing data sets. However,

Deal R Users, I'm trying to find out how can I compute probabilities on a Bayesian Network using R. The Bayesian Network I modelled is shown at http://www.dsr.inpe.br/~mello/1727/BNgrapmodel.png and I'd like to know how can I proceed (commands on R) to answer questions like: (1) what is the probability of S=T given C=T, L=T, R=F, H=F and D=F; or (2) what is the probability of S=T

Hello, I'm writing from the otherside of the world from where my systems are, so details are coming in slow. We have a 6TB OCFS2 volume across 20 or so nodes all running OEL5.4 running ocfs2-1.4.4. The system has worked fairly well for the last 6-8 months. Something has happened over the last few weeks which has driven write performance nearly to a halt. I'm not sure how to proceed, and