search for: tstats

Hi, R newb here. I've coded a function that inputs N dimensional array(s) [or class=numeric if it's dim=1] of coefficients and tstats, where dim(coef_matrix)=dim(tstat_matrix), it will then output a same dimension matrix of coefficients pasted to tstats in brackets pasted to significance stars. If I go straight to the code INSIDE the function, it works 100% as it's supposed to. The output matrix has the correct dimensions. H...

Hello! I am running a very simple mini Monte-Carlo below using the function tstatistic (right below this sentence): tstatistic = function(x,y){ m=length(x) n=length(y) sp=sqrt( ((m-1)*sd(x)^2 + (n-1)*sd(y)^2)/(m+n-2) ) t=(mean(x)-mean(y))/(sp*sqrt(1/m+1/n)) return(t) } alpha=.1; m=10; n=10 # sets alpha, m, n - for run 1 N=10000 # sets the number of simulations n.reject=0 # counter of num.

Here's one possibility, assuming muhat and sigmahat are estimtes of mu and sigma from N iid draws of N(mu, sigma^2): tStat <- abs(x - muhat) / sigmahat pValue <- pt(tStat, df=N, lower=TRUE) I'm not quite sure what df tStat should have (exercise for math stat), but given fairly large N, that should make little difference. Andy > From: Ross Clement > > Hi. I have a

Hi, people. I was a bit intrigued by the message quoted below. Indeed, if pt() is given a matrix, it returns a matrix. Should this feature be documented? ?pt speaks about "a vector of quantiles", and says nothing about the type of what it returns. The same might presumably apply to other distribution-related functions. ----- Forwarded message from Martyn Plummer <plummer at

...struct rtnl_link_stats64 *tot) { struct virtnet_info *vi = netdev_priv(dev); - int cpu; + int i; unsigned int start; - for_each_possible_cpu(cpu) { - struct virtnet_stats *stats = per_cpu_ptr(vi->stats, cpu); + for (i = 0; i < vi->max_queue_pairs; i++) { + struct virtnet_tx_stats *tstats = &vi->sq[i].stats; + struct virtnet_rx_stats *rstats = &vi->rq[i].stats; u64 tpackets, tbytes, rpackets, rbytes; do { - start = u64_stats_fetch_begin_bh(&stats->tx_syncp); - tpackets = stats->tx_packets; - tbytes = stats->tx_bytes; - } while (u64_stats_...

...struct rtnl_link_stats64 *tot) { struct virtnet_info *vi = netdev_priv(dev); - int cpu; + int i; unsigned int start; - for_each_possible_cpu(cpu) { - struct virtnet_stats *stats = per_cpu_ptr(vi->stats, cpu); + for (i = 0; i < vi->max_queue_pairs; i++) { + struct virtnet_tx_stats *tstats = &vi->sq[i].stats; + struct virtnet_rx_stats *rstats = &vi->rq[i].stats; u64 tpackets, tbytes, rpackets, rbytes; do { - start = u64_stats_fetch_begin_bh(&stats->tx_syncp); - tpackets = stats->tx_packets; - tbytes = stats->tx_bytes; - } while (u64_stats_...

...1,2,3,4,5,6,7,8,9,16,17,18,19,20,... noise,49,47,48,48,50,47,48,47,46,50,48,54,49,47,49,... signal,99,0,100,0,0,100,0,100,100,0,100,101,100,0,0,... pctrcv,5,0,5,0,0,5,0,5,11,0,5,5,5,0,0,... I can transpose the data file (e.g., using an awk script), and then read it using read.csv("tstats"), $ transpose /tmp/stats > /tmp/tstats $ cat /tmp/tstats freq,noise,signal,pctrcv 0,49,99,5 1,47,0,0 2,48,100,5 ... but would prefer to import the line-oriented files directly. I've drawn a blank after perusing help, documentation, google searches, etc.....

I am trying to assign values to a vector (pvalue). Similar code works in C but not in R. What am I doing wrong? r <- pvalue <- 0 for(i in (1:(k-1))){ for(j in (i+1):k){ r <- r+1 tstat <- (means[i]-means[j])/rms pvalue[r] <- 2*(1-pt(abs(tstat),df)) } } Thank you. Peter B. -- Peter B. Mandeville mandevip at deimos.tc.uaslp.mx Jefe del

Dear All, I am doing some exercise in statistics textbook on comparison of two experimental means. Is it possible to use t.test() do t-test when I have only two means, sample size, two standard deviations ? (no raw data). Thanks. Pramote

...,list(g=times$g, n=times$n, method=times$method) ,mean, na.rm=TRUE ) ,by=c('g','n','method') ) # transpose to wide mm <- melt(stats, id=c('g','n','method')) tstats <- dcast(mm, g + n ~ variable+method) tstats$speedup.elapsed1 <- tstats$meanElapsed_unvectorized / tstats$meanElapsed_1CoreVectorized tstats$speedup.elapsed3 <- tstats$meanElapsed_1CoreVectorized / tstats$meanElapsed_SMPVectorized speedupVectorizing <- dcast(tstats[c('g...

As a very new user of R, and a user of S with very limited experience, I thought I would start humbly with a simple t.test. The response to the command sequence sample <- scan() 4 5 6 7 8 9 t.test(sample,mu=20) rather surprised me, because of the idiosyncratic choice of a confidence interval for the mean of the population from which the sample was drawn. I asked my colleague Bob Henery

Hello All, I was wondering how I can get the overall Pearson chi^2 test of model fit with its df and p value in the LTM package for the 2PL models. Thanks, -- Davood Tofighi Department of Psychology Arizona State University [[alternative HTML version deleted]]

Hola, quiero hacer una regresión lineal lm(y ~ x * grupo, data =datos) y: numérica, x: numérica, grupo: factor con dos niveles (1 y 2) pero no calcula el coeficiente de x:grupo2 a cuenta de una singularidad Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) -1.283e+06 2.276e+04 -56.359 < 2e-16 *** x

I'd like to learn the process of revising R functions & packages and then submitting proposed patches to the R Core team. Would someone be willing to mentor me through one example? For starters, consider an example. I'd like to revise the t.test function to return the stderr value to the user. We only need to change the "rval" in the third-from-the end line of

I need to perform 10000 T tests #I have two populations with different means Popc1<-rnorm(100000,10,2) Popc2<-rnorm(100000,8,2) #I created two sets of samples - each set has 10000 samples, and I made a matrix of 20 rows and 10000 columns to fit the data sampc1<-matrix(,20,10000) for(j in 1:10000){sampc1[1:20,j]<-sample(Popc1,20)} sampc2<-matrix(,20,10000) for(j in

RE t.test in R I objected a day or two ago to the behaviour of the one-sample t.test in R where it is easy to generate a "confidence interval" for the mean of the population which does not contain the sample mean, in the case where the null hypothesis is rejected. It now appears that the same behaviour is latent in the code for the two-sample version of this test. The relevant lines

Dear all, For certain reasons I have to compile the source code of my package on Windows XP using Microsoft Visual Studio 9.0, thus I had to create a "Makefile.win". Now I have a question regarding compiler options /MT vs /MD for Makefile.win. The following partial output shows that when building my package on Windows XP using "R CMD build --binary xps" with compiler

I am trying to pass an equation as an arguement to a function, which seems pretty straightforward given lazy evaluation. I constructed the following little test to make sure I sort of knew what I was doing: go <- function(X,eq) { C <<- X*2; d <<- eq} a _ rnorm(100) go(10,eq=a[.5*length(a)]+C+1) > C [1] 20 > d [1] 21.10551 > a[.5*length(a)] [1] 0.1055086 Everything

Number of subjects = 25 Mean of Sample = 77 Standard Deviation (s) = 12 sem = 2.4 df = 24 The claim is that population mean is less than 80 * > 80 So our H0 (null hupotheis) is * > 80 > qt(.95,24) [1] 1.710882 > qt(0.05, 24) [1] -1.710882 tstat = -1.25 on t24 falls between 1.711 (.95,24) and *1.711 (.005,24) How Could I sketch t curve for the above data where my * would be at the

Hi! I'm doing Monte Carlo analyses of the distribution of the t-statistics of the parameters of models evaluated with the lm( ) function. Is there an easy way to recover the t-statistics (similarly to using coef to recover the coefficients)? Thanks, joseph --------------------------------- [[alternative HTML version deleted]]