search for: sens

Hi All, I have a very simple problem. I have several files in a same directory. I would like to send for an object only the files that finish in ".sens.". I execute the command below, files <- dir(pattern="*.sens") but it includes all of the files that have "sens", independent of they be in the end or in the middle of the name of the file. How could I solve this? I sought in the html_help but I didn't find similar...

Recently, we made a switch from 1.9.18 to 2.0.3. Under 1.9.18, we had local master turned off to prevent a browser election war. However, under some network configurations, (where the system was alone on a Workgroup), the system would, quite appropriately, not appear in the network neighborhood. So for 2.0.3 we changed the config file to allow election participation (local master = yes).

Dear List,   I am new to ROC analysis and the package ROCR. I want to compute the confidence intervals of sensitivity and specificity for a given cutoff value. I have used the following to calculate sensitivity and specificity:   data(ROCR.simple) pred <- prediction(ROCR.simple$predictions, ROCR.simple$labels)   se.sp <- function (cutoff, performance) {     sens <- performance(pred,"sens&quot...

Dear R fellows, Assume I define a <- expression(fn+tp) sen <- expression(tp/a) Now I'd like to know, which variables are necessary for calculating sen all.vars(sen) This results in a vector c(tp,a). But I'd like all.vars to evaluate the sen-object down to the ground level, which would result in a vector c(tp,fn) (because a was defined as fn+tp). In other words, I'd like

In an earlier post, a person wanted to divide each of the rows of rawdata by the row vector sens so he did below but didn't like it and asked if there was a better solution. rawdata <- data.frame(rbind(c(1,2,2), c(4,5,6))) sens <- c(2,4,6) temp <- t(rawdata)/sens temp <- t(temp) print(temp) Gabor sent three other solutions and I understood 2 of them but not the...

Hello, I am trying to decide which package to use to calculate the non-parametric Sen's Slope for identifying trends in rainfall data (determine the slope between all pairs of points and take the median of those slopes). I have found three packages that output Sen's: "zyp", "wq" and "fume". The outputs of "zyp.sen()" and "mannKen()" from

...-2.5000000 2.333333 > ci_90 <- confint.zyp(slope, level=0.90) > ci_90 0.05 0.95 Intercept 0.1230428 7.876957 x -2.0000000 2.000000 # zyp.yuepilon # confidence interval corresponds to nominal 0.95 interval in confint.zyp (output 0.025 0.975) > xy_senslope <- zyp.yuepilon (y, conf.intervals=TRUE) > xy_senslope lbound trend trendp ubound tau sig nruns autocor valid_frac -2.50000000 0.66666667 4.00000000 2.33333333 0.80000001 0.08641075 1.00000000 -0.22400000 1.00000000 linear i...

...objective = as.double(objective), const.count = as.integer(const.count), constraints = as.double(constraints), int.count = as.integer(int.count), int.vec = as.integer(int.vec), objval = as.double(objval), solution = as.double(solution), presolve = as.integer(presolve), compute.sens = as.integer(compute.sens), sens.coef.from = as.double(sens.coef.from), sens.coef.to = as.double(sens.coef.to), duals = as.double(duals), duals.from = as.double(duals.from), duals.to = as.double(duals.to), status = as.integer(status), PACKAGE = &quot;lpSolve&quot;) 2: lp(&a...

Hello... I tried to compile an R addon package, R/qtl on a Solaris machine and I get this error. I know that R/qtl complies correctly on other platforms (Windows, Mac, Linuxes, etc), so I am wondering what the problem might be. Any ideas? Thanks, Saunak ----------------------------------- /export/home/sen/tmp <howrahbridge> <pts/5> R INSTALL

Hello... I just installed R version 1.4.0 on my laptop running Redhat Linux 7.1. I downloaded the RPM from CRAN and when I try to use a function I have used on other Redhat systems, I get the following error message. -------- > library(qtl) > gastritis <- read.cross(format="csv",dir="/home/sen/qtl/data/gastritis", +

...he same plot.Actually each line is a ROC plot The code I'm using for plotting one line is as follows: logit.roc.plot <- function(r, title="ROC curve") { old.par <- par(no.readonly = TRUE); on.exit(par(old.par)) par(xaxs="i", yaxs="i") plot(1 - r$spec, r$sens, xlim=c(0, 1), ylim=c(0,1), type="l", xlab="(1 - specificity): false positive rate", ylab="sensitivity: true positive rate", col="grey82", lwd=2); points(1 - r$spec, r$sens,pch=20, cex=1.5, col="grey82") abline(0, 1, lty=2); segments(1-r$spec,...

Hello, I am importing some raw voltage multichannel measurements into an R data frame. I need to scale each column with the respective sensitivity for that channel. I figured how to do it, but I am curious if there isn't a more elegant way. Now I start with something like this: rawdata <- data.frame(rbind(c(1,2,3), c(4,5,6))) sens <- c(2,4,6) and I do this: data <- as.data.frame(t(t(rawdata)/sens)) Any suggestions? C...

Dear Mr Peter Dalgaard and Mr Dieter Menne, I sincerely thank you for helping me out with my problem. The thing is taht I already have calculated SENS = Gg / (Gg + Bg) = 89.97% and SPEC = Bb / (Bb + Gb) = 74.38%. Now I have values of SENS and SPEC, which are absolute in nature. My question was how do I interpret these absolue values. How does these values help me to find out wheher my model is good. With regards Ms Maithili Shiva ____________...

..., int.count = as.integer(int.count), int.vec = as.integer(int.vec), bin.count = as.integer(bin.count), binary.vec = as.integer(binary.vec), num.bin.solns = as.integer(num.bin.solns), objval = as.double(objval), solution = as.double(solution), presolve = as.integer(presolve), compute.sens = as.integer(compute.sens), sens.coef.from = as.double(sens.coef.from), sens.coef.to = as.double(sens.coef.to), duals = as.double(duals), duals.from = as.double(duals.from), duals.to = as.double(duals.to), scale = as.integer(scale), use.dense = as.integer(use.dense), dense.col = as....

Hi R-masters I read the article: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. In this paper i proposed a bivariate mixed model and use SAS proc mixed to adjust the estimates. I thinks use R to make the same and try with this code: base<-read.csv("base.csv") adj<-.5 attach(...

Hi, I'm trying to set up a prediction software, now i testing the performance of my method, so i need to calculate a ROC curve, specially auc, cut-off, sens and spec, i just looking at ROCH package, but it's a mass for me, i'm not a math guy and I'm getting lost Could any of you recommend me an easy-to-use package to do this task? i just have a list of positive/negative samples and his score on my program. can I compute a roc curve with t...

...code to continue with the "for loop" after the lmer model results in an error. Here is two attemps of my code, the input is a 3D array file, but really any function would do.... metatrialstry<-function(mydata){ a<-matrix(data=NA, nrow=dim(mydata)[3], ncol=5) #colnames(a)=c("sens", "spec", "corr", "sens_se", "spec_se", "counter")#colnames(a)=c("sens", "spec", "corr", "sens_se", "spec_se") k=1 for(ii in 1:dim(mydata)[3]){ tmp<-mydata[,,ii] tmp1<-as.data.fram...

...in the same plot.Actually each line is a ROC plot The code I'm using for plotting one line is as follows: logit.roc.plot <- function(r, title="ROC curve") { old.par <- par(no.readonly = TRUE); on.exit(par(old.par)) par(xaxs="i", yaxs="i") plot(1 - r$spec, r$sens, xlim=c(0, 1), ylim=c(0,1), type="l", xlab="(1 - specificity): false positive rate", ylab="sensitivity: true positive rate", col="grey82", lwd=2); points(1 - r$spec, r$sens,pch=20, cex=1.5, col="grey82") abline(0, 1, lty=2); segments(1-r$spec, 1-r$s...

No, As it worked fine before. I can't see the #if that goes with +#elif defined(__WIN32__) as you removed -#if TARGET_OS_MAC. Please go over your #if / #endif blocks and trail the #endif with a comment. I am willing to bet there is a problem there. Thanks for your time, Edward. 2009/9/23 Shantonu Sen <ssen at apple.com>: > Sounds like your system compiler doesn't support

Dear all, I would like to share a paper I co-authored with Prof. Alper Sen. This paper describes an algorithm to generate testbenches from SystemC models represented with LLVM IR. It was accepted and presented at International Conference on Hardware/Software Codesign and System Synthesis (CODES/ISSS), 2012. A link to the paper is accessible from acm or Alper's website: