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...o display the y-axis values where the two histograms are back to back, rather than on the y-axis? Below, I've pasted the graph that I'm trying to recreate, as well as the code and graph from my current (unsuccessful) attempt. options(digits=1) require(Hmisc) out <- histbackback(split(data8$TB144, data8$PRES), probability=TRUE, main="Back to Back Histogram", ylab="TB144 1min rates") barplot(-out$left, col="ivory2", horiz=TRUE, space=0, add=TRUE, axes=FALSE) barplot(out$right, col="ivory4", horiz=TRUE, space=0, add=TRUE, axes=FALSE) grid(nx=NULL,...

...argument to survfit with no success. Here is my model statement, the newdata and the ensuing error. What am I doing wrong? > summary(fit) Call: coxph(formula = Surv(Start, Stop, Event, type = "counting") ~ Week + LagAOO + Prior.f + cluster(interaction(Station, Year)), data = data8, method = "breslow", x = T, y = T) n= 1878 coef exp(coef) se(coef) robust se z p Week 0.00582 1.01 0.0323 0.0239 0.244 8.1e-01 LagAOO 0.71929 2.05 0.1238 0.1215 5.918 3.3e-09 Prior.f2 0.12927 1.14 0.4402 0.4025 0.321 7.5e-01 Pr...

..., the more its confusing me. I have a server with 6 (750G each) SATA disks with H/W Raid 5. I plan to allocate the space as follows swap 8G /boot 100M / 20G -- and remaining space to /data{1,2,3,N} (equal sizes) However after the installation and reboot, i got an error about bad partition for /data8 I had hit the 2T limit. Then i found this page at http://www.knowplace.org/pages/howtos/linux_large_filesystems_support.php which speaks of using Parted/LVM2 and XFS. If i understand this correctly, I need to have 1 disk to host the CentOS installation. And i can use the other 5 disks in a RAI...

...atanam i <- i+1 } The function "names(data)[length(data)]" doesn't seem to work. I need it to be named like data1,data2,,,,,data20..... Also to append them into a big dataset, I think there should be something simpler than all <- rbind(data1,data2,data3,data4,data5,data6,data7,data8.........data20) can you help me on this? I hope this is not some simplest R question..... I am a beginner. Thanks a ton! Grace Confidentiality Notice: This e-mail message including at...{{dropped:12}}

Hi, I am using security=share, and have just upgraded samba server from 3.0.2 to 3.0.4, after upgrading write list option had stopped working. Below is the contents of smb.conf file, which was working with 3.0.2 but not with 3.0.4 [global] workgroup = CSW netbios name = DATA8 security = share server string = Test Samba 3.0 os level = 42 guest account = smbguest log file = /var/log/samba/%m.log max log size = 100 socket options = TCP_NODELAY SO_RCVBUF=8192 SO_SNDBUF=8192 remote announce = 192.168.8.255,192.168.9.255 wins server = 192.168.8.1...

Hi, I am trying to use CART to find an ideal cut-off value for a simple diagnostic test (ie when the test score is above x, diagnose the condition). When I put in the model fit=rpart(outcome ~ predictor1(TB144), method="class", data=data8) sometimes it gives me a tree with multiple nodes for the same predictor (see below for example of tree with 1 or multiple nodes). Is there a way to tell it to make only 1 node? Or is it safe to assume that the cut-off value on the primary node is the ideal cut-off? Thanks! Katie http://n4.nab...

...6/x86_64/entry.S: .quad do_xen_version ./xen/arch/x86/x86_64/entry.S: .byte 2 /* do_xen_version */ Binary file ./xen/arch/x86/built_in.o matches Binary file ./xen/arch/x86/hvm/hvm.o matches Binary file ./xen/arch/x86/hvm/built_in.o matches ./xen/arch/ia64/linux-xen/entry.S: data8 do_xen_version ./xen/arch/powerpc/powerpc64/hypercall_table.S: .quad do_xen_version Binary file ./dist/install/boot/xen-syms-3.1.0 matches ./tools/libxc/xc_private.c: rc = do_xen_version(xc_handle, cmd, arg); ./tools/libxc/xc_private.h:static inline int do_xen_version(int xc_handle, int c...

Suppose I start building nodes with (say) 24 drives each in them. Would the standard/recommended approach be to make each drive its own filesystem, and export 24 separate bricks, server1:/data1 .. server1:/data24 ? Making a distributed replicated volume between this and another server would then have to list all 48 drives individually. At the other extreme, I could put all 24 drives into some

...ead.delim("t (4).txt", quote="", as.is=TRUE) data5 <-read.delim("t (5).txt", quote="", as.is=TRUE) data6 <-read.delim("t (6).txt", quote="", as.is=TRUE) data7 <-read.delim("t (7).txt", quote="", as.is=TRUE) data8 <-read.delim("t (8).txt", quote="", as.is=TRUE) data9 <-read.delim("t (9).txt", quote="", as.is=TRUE) data10 <-read.delim("t (10).txt", quote="", as.is=TRUE) data11 <-read.delim("t (11).txt", quote="", as...

Hi all, Not really sure what to put in the title so hope people still open this! What I want to do is produce something like this Col1 Col2 Col3 data1 data2 data3 data4 data5 data6 data7 data8 data9 data.. data.. data.. data.. data.. data.. data-n data-n+1 data-n+2 I can get the data back from my db happily, but I cant for the life of me work out how to actually display it. Single column is easy, but anything else has me stumped. Can anyone he...

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on

This patch set is for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on native

This patch set is for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on native

...r variable? The output looks strange with the cluster variable. My intervals are based on calendar time and the clustering variable is related to the season the event occurs in. model1<-coxph(Surv(Start,Stop,Event)~LagAOO+I(LagAOO^2)+ FirstSeen+TSLE+strata(CumPOO.rc)+cluster(quarter),data=data8, x=T) >Cox.zph(model1) LagAOO 0.209 5.61 0.0179 I(LagAOO^2) -0.209 5.61 0.0179 FirstSeen 0.209 5.61 0.0179 TSLE 0.209 5.61 0.0179 GLOBAL NA 5.61 0.2304 In this example there is strong evidence for non-proportional hazards for each of my covariates. By...

This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect call into in-place execution or direct call. The first patch imports helper functions which themselves doesn't interesting things. The second patch replaces the indirect function calls with a

This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect call into in-place execution or direct call. The first patch imports helper functions which themselves doesn't interesting things. The second patch replaces the indirect function calls with a

Hi, I want to do automatic reading of a number of tables (files) stored in ascii format without having to specify the variable name in R each time. Below is an example of how I would like to use it (I assume files pair1,...,pair8 exist in spec. dire.) for (i in 1:8){ name <- paste("pair",i,sep="") ? ? ? <-