similar to: CESA-2014:0866 Moderate CentOS 5 samba3x Update

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CentOS Errata and Bugfix Advisory 2013:0866 Upstream details at : https://rhn.redhat.com/errata/RHBA-2013-0866.html The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) x86_64: efe7b5b8ad917803dcdb69ff8c4d69d6a9ef070395ae16f57720b83a9d1c6acf spice-server-0.12.0-12.el6_4.1.x86_64.rpm

CentOS Errata and Security Advisory 2014:0866 Moderate Upstream details at : https://rhn.redhat.com/errata/RHSA-2014-0866.html The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) i386: 0c0ec9a01e0e657d1a9e429e32752883eda95e668b5ce606c1523069b06c4cbf libsmbclient-3.6.9-169.el6_5.i686.rpm

I have a TrippLite SmartPro UP (SMART3000RM2U) that shipped with the DB9 cable# 73-0866. nut doesn't recognize the UPS using the tripplite driver. I found this page: http://www.tripplite.com/support/download/PA12/help/device.htm The link lists the SmartPro series as using the cables 73-0724 or 73-0844 and the Internet 350/525 is listed as using a cable 73-0866. I have a few 73-0844s

Dear all, One can use ks.test(x,y) for a one-sample kolmogorov-smirnov test: x being the data sample y being a string specifying a distribution I notice the help on ks.test does not tell you how to get such a list. Is this a hole in my R knowledge? Where can I get a list of the strings specifying the possible distributions? and more specifically What would be the string and following

Given two numeric vectors of possibly unequal length, I'd like to pair each element of the shorter vector with an element of the longer vector such that the sum of squared differences between the pairs is minimized. Can someone point me to an R function or an algorithm for accomplishing this? All the best, Tom Thomas S. Dye, Ph.D. T. S. Dye & Colleagues, Archaeologists, Inc.

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Aloha all, I love using ggplot. It took a while to get used to the grammar of graphics, but it is starting to get easy now that I am thinking in a more structured way. A question. I'm making a wind rose that I'd like to be oriented with due north straight up. I've discovered that the orientation is sensitive to how north is represented. When north is represented as 0,

Aloha all, I have a data frame with 4 columns. The first three are factors (f1, f2, f3) and the fourth is numeric. I'd like to explore these data using median polish. To do that I plan to use medpolish() on the matrix[f1,f2xf3], then medpolish on the resulting matrix[f2,f3]. This approach is described by Cook on page 141 of Exploring Data Tables, Trends, and Shapes. split()

I currently work on a draft of an aquatic bioassessment. The conditions tested are the following: ER river water T dechlorinated water control 0.5 + 0.5mg / L of malate T + 1 dechlorinated water control + 1g / L of malate T ED dechlorinated water control SED + ER + river water sediment SED ED + sediment + water dechlorinated. It is the result of AChE in muscle (fillet of fish). The production of

Dear R community members, I think I am asking a very simple question, but I really looked up in the faqs and manuals and found nothing helpful. I am trying to plot a data frame with the following structure (this is just a small extract): glo conc odor line series X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 1 0 AIR LN1 UP -0.488

Hello. I am trying to get multi paths running, i.e I want to specify 2 paths from a particular node to another. To test this, I am setting up the following scenario: I create 2 tap devices tap0 = 10.1.1.1/32 netmask 255.255.255.0 tap1 = 10.2.2.2/32 netmask 255.255.255.0 I then issue the route command: ip route add 10.3.3.3/32 nexthop via 10.1.1.2 nexthop via 10.2.2.3 The route table

Hello, Does anyone know the settings to get this UPS talking to nut? I'm trying to get it working over the serial port, and I've tried using genericups type 5. When I do this, the driver seems to initialize, but then upsc shows the status to be OB LB. When I unplugged the UPS, there was no change in the status. I noticed on the Tripp Lite web site that this uses a different cable. It is

Dear all, I have a multivariate dataset containing 100,000 or more points. I want find the p-value for the dataset of points coming from a particular multivariate normal distribution With mean vector u Covariance matrix s2 So H0: points ~ MVN( u, s2) H1: points not ~ MVN( u, s2) How do I find the p-value in R? To me this is a likelihood ratio test problem. In H0 the parameters are

Hi all: I finished cox analysis like this: fit_cox<-coxph(Surv(dat$Time, dat$death) ~ dat$CD4 + strata(dat$gender),data=dat); > fit_cox Call: coxph(formula = Surv(data_ori$Time, data_ori$death) ~ data_ori$drug + strata(data_ori$gender), data = data_ori) coef exp(coef) se(coef) z p data_ori$drugddI 0.216 1.24 0.146 1.47 0.14 Likelihood ratio test=2.17 on

https://bugzilla.netfilter.org/bugzilla/show_bug.cgi?id=473 Summary: 2.6.16.x translates addresses of RELATED packets incorrectly Product: netfilter/iptables Version: linux-2.6.x Platform: i386 OS/Version: Debian GNU/Linux Status: NEW Severity: normal Priority: P2 Component: NAT

Hi, I'm reading part of a table from postgres, so I'm getting a data frame. how can I extract the numerica values so I can operate on them. > res <- dbGetResult(mydata) > str(res) `data.frame': 5 obs. of 8 variables: $ cyx.1: num 0.149 -0.278 0.114 0.060 0.109 $ cyx.2: num 0.158 -0.070 0.063 0.149 0.150 $ cyx.3: num 0.052 -0.350 0.114 0.126 0.238

Dear all, I have a beginner's R question for coxph() and survfit(). I have a data set with survival time (no censoring) and treatment indicator(0/1). I did something like fit<-coxph( Surv(t)~treat, method="breslow",data=data) > fit Call: coxph(formula = Surv(t) ~ treat, data = data, method = "breslow") coef exp(coef) se(coef) z p treat -1.96

Hello, I have used R in the past to conduct multiple comparisons on standard linear models, but am a bit confused as to how to go about doing it with a mixed effects model. I am conducting a bioindication study using carabid beetles in which I have four treatment types (forest harvest types with varying levels of canopy structure retention), and am using canopy closure percent as a covariate in

In addition to the major points that others made: if you care about speed, don't use compression. With today's fast disks it's an order of magnitude slower to use compression: > d=lapply(1:10, function(x) as.integer(rnorm(1e7))) > system.time(saveRDS(d, file="test.rds.gz")) user system elapsed 17.210 0.148 17.397 > system.time(saveRDS(d,