similar to: proportions confidence intervals

R-experts: A quick question, please. >From a lab exp, I got 12 positives out of 50. To get 90% CI for this , I think binom.test might be the one to be used. Is there a better way or function to calculate this? > binom.test(x=12, n=50, p=12/50, conf.level = 0.90) Exact binomial test data: 12 and 50 number of successes = 12, number of trials = 50, p-value = 1 alternative

Dear All I am trying to graph a proportion and CI95% by a factor with ooplot (any other better solution ?) It works well until I try to add the confidence interval. this is the error message and and a description of the data: > dat1 PointEst TT1 1 3.6 TT2 2 5.0 TT3 3 5.8 TT4 4 11.5 TT5 5 7.5 TT5 6 8.7 TT7 7 17.4 > dat2

Hello Please see the example below > class(testX) [1] "matrix" > class(testX[1,]) [1] "numeric" Why not matrix? What am I missing here? Is there a way to keep the same class? The reason for the question is that I want to implement a k-step ahead prediction for my own routines and R wrecks does not seem to like [1,] as shown below. >

Hi folks, Wish y'all a Happy New Year 2010! I need some help with the following: Say I have lots of data sets, on which I have to apply a certain function on the same set of columns in each of the data set. Let's take, for ex, the typical data set is: df1 <- as.data.frame(cbind(rnorm(10),rnorm(10))) names(df1)[1] <- "A" names(df1)[2] <- "B" There are many

Hello all, I've been using binconf (package Hmisc) at a range of alpha values and noticed that using the 'Wilson' method when alpha is larger (i.e. narrow CI), results in the upper value being smaller than the lower value. The 'exact' and 'asymptotic' methods give results in the realm I'd expect. But the help file suggests: "Following Agresti and Coull, the

Windows Vista R 2.10.1 Does the following use of groupedData and lme produce an analysis with both random intercept and slope, or only random slope? zz<-groupedData(y~time | Subject,data=data.frame(data), labels = list( x = "Time", y = "y" ), units = list( x = "(yr)", y = "(mm)") ) plot(zz)

Have you tried to use NetworkManager ? After all ,anything network related should be done by it. [root at system ~]# nmcli connection add con-name dummy0 ifname dummy0 type dummy ? Connection 'dummy0' (9fdd74fa-c143-4991-9bac-0e542704ac89) successfully added. [root at system ~]# reboot Shared connection to glustera closed. [root at system ~]# uptime 03:23:44 up 0 min, ?1 user, ?load

Anyone have any ideas? It's rather annoying that I can't get these to persist across reboots without using some kind of helper script. On Fri, Oct 16, 2020 at 6:37 AM Frank Even <lists+centos.org at elitists.org> wrote: > > Hello all, hoping someone can help me out here. > > I cannot get dummy interfaces on a new Cent8 build to persist across reboots. > > On Cent7

Hi, I am looking at the change in N concentration in plant roots over 4 time points and I have fit a spline to the data using ns and lme: fit10 <- lme( N~ns(day, 3), data = rcn10G) I may want to adjust the model a little bit, but for now, let's assume it's good. I get output for the fixed effects: Fixed: N ~ ns(day, 3) (Intercept) ns(day, 3)1 ns(day, 3)2 ns(day, 3)3

Hello all, hoping someone can help me out here. I cannot get dummy interfaces on a new Cent8 build to persist across reboots. On Cent7 - this is the process I use: Create Dummies: # cat /etc/modules-load.d/dummy.conf dummy # cat /etc/modprobe.d/dummyopts.conf options dummy numdummies=4 # ip link add dummy0 type dummy ## - repeating a/ ascending dummyN adapters for as many needed # service

Hi, I was wondering if there was some sort of package or function that calculated asymmetric confidence intervals for small proportions. I thought of both the epicalc and epitools package, but I am hoping to find something where you can just plug in a standard error and point estimate and it will output the upper and lower CI bounds. Thanks! Sarah

Hi R users, Can anyone share some example code using merge_all (from the reshape package) to merge 10 data frames into 1 file. Thanks in advance for any help! -- View this message in context: http://www.nabble.com/Merge-10-data-frames-with-3-id-columns-that-are-common-to-all-data-frames-tp22402493p22402493.html Sent from the R help mailing list archive at Nabble.com.

Dear All, I have the following data coming out from s <- with(final, summarize(norm, llist(gtt,fdiab), function(norm) { n <- sum(!is.na(norm)) s <- sum(norm, na.rm=T) binconf(s, n) }, type='matrix') ) ie gtt fdiab norm.norm norm.norm2 norm.norm3 18

Hello sir: As to the 2*2 table format for reporting results comparing a new test to true diagnosis,when I got the sensitivity and specificity,how can I calculate the exact 95% confidence intervals (based on the binomial distribution) for sensitivity and specificity via R? Thanks a lot! My best! ------------------------------ ******************************************* Xin Meng Capitalbio

No. Network Manager is always disabled on our builds since at least Cent5 days. The network stack has always been able to be managed properly without relying on Network Manager. Is that now an absolute requirement? It never has been prior. On Mon, Oct 26, 2020 at 6:26 PM Strahil Nikolov via CentOS <centos at centos.org> wrote: > > Have you tried to use NetworkManager ? > After

Requirement is a very strong word , but you should consider using it and here is a short demo why: - By default, RHEL uses NetworkManager to configure and manage network connections, and the /usr/sbin/ifup and /usr/sbin/ifdown scripts use NetworkManager to process ifcfg files in the /etc/sysconfig/network-scripts/ directory. [root at system ~]# ls -l /usr/sbin/ifup lrwxrwxrwx. 1 root root 22 21

Hello R-Users, I have recently run into several problems using vgam() in the VGAM package. I am hoping someone might have some solutions... Briefly, I have been trying to fit GAM models for zero-altered negative binomial models. 1. When fitting smoothed parameters (e.g. s(X, df=2)) changing the degrees-of-freedom has no effect on the level of smoothing (e.g. number of knots for the

Hi, I am looking at the change in N concentration in plant roots over 4 time points and I have fit a spline to the data using ns and lme: fit10 <- lme( N~ns(day, 3), data = rcn10G) I may want to adjust the model a little bit, but for now, let's assume it's good. I get output for the fixed effects: Fixed: N ~ ns(day, 3) (Intercept) ns(day, 3)1 ns(day, 3)2 ns(day, 3)3 1.15676524