similar to: Rscript question

Hi, All,   My data collection is from 4 regions (a, b, c, d). Within each region, it has 2 or 3 units. Within each unit, it has measurement from about 25 sample site. I was trying to use lme function to discribe relationship between y and a few covariates. Both y and covariates were measured at the sample site level. My question is when I use exactlly the same model but choose different estimation

My data frame looks like: SightingID PA1 PA2 PlotID InOverlap Area1 2001 1 -99 392 Y 0.22 2002 1 -99 388 Y 0.253 2008 1 NA 104 N 0.344 2010 1 NA 71 N 0.185 2012 1 NA 61 N 0.166 2013 1 NA 61 N 0.227 2014 1 NA 62

Hi, this is not a new doubt, but is a doubt that I cant find a good response. Look this output: > m.lme <- lme(Yvar~Xvar,random=~1|Plot1/Plot2/Plot3) > anova(m.lme) numDF denDF F-value p-value (Intercept) 1 860 210.2457 <.0001 Xvar 1 2 1.2352 0.3821 > summary(m.lme) Linear mixed-effects model fit by REML Data: NULL AIC BIC

Background: OS: Linux Mandrake 10.1 release: R 2.0.0 editor: GNU Emacs 21.3.2 front-end: ESS 5.2.3 --------------------------------- Colleagues I am having some trouble extracting results from the function by, used to average variables in a data.frame first by one factor (depth) and then by a second factor (station). The real data.frame is quite large > dim(data.2001) [1] 32049 11 Here is a

Hi All, Please pardon me if I am missing something obvious here. How do I get the Kaplan-Meier estimate function that is created by survfit and plotted by the code. fit <- survfit(Surv(time, status) , data=aml) plot(fit) That is, I need a function that will give me the survival estimate at a given time: \hat{S}(t). Thanks in advance. Ritwik Sinha ritwik.sinha at gmail.com | +12033042111 |

Dear Metafor users, I'm conducting a metaanalysis of prevalence of a particular behaviour based on someone elses' code. I've been labouring under the impression that this: summary(rma.1<-rma(yi,vi,mods=cbind(approxmeanage,interviewmethodcode),data=mal,method="DL",knha=F,weighted=F,intercept=T)) is doing the multivariate analysis that i want, but have read that

Dear R-listers Is there anyone who knows why I get different eigenvectors when I run MatLab and R? I run both programs in Windows Me. Can I make R to produce the same vectors as MatLab? #R Matrix PA9900<-c(11/24 ,10/53 ,0/1 ,0/1 ,29/43 ,1/24 ,27/53 ,0/1 ,0/1 ,13/43 ,14/24 ,178/53 ,146/244 ,17/23 ,15/43 ,2/24 ,4/53 ,0/1 ,2/23 ,2/43 ,4/24 ,58/53 ,26/244 ,0/1 ,5/43) #R-syntax

Memory fragmentation introduced by ballooning might reduce significantly the number of 2MB contiguous memory blocks that can be used within a guest, thus imposing performance penalties associated with the reduced number of transparent huge pages that could be used by the guest workload. This patch-set follows the main idea discussed at 2012 LSFMMS session: "Ballooning for transparent huge

Memory fragmentation introduced by ballooning might reduce significantly the number of 2MB contiguous memory blocks that can be used within a guest, thus imposing performance penalties associated with the reduced number of transparent huge pages that could be used by the guest workload. This patch-set follows the main idea discussed at 2012 LSFMMS session: "Ballooning for transparent huge

Memory fragmentation introduced by ballooning might reduce significantly the number of 2MB contiguous memory blocks that can be used within a guest, thus imposing performance penalties associated with the reduced number of transparent huge pages that could be used by the guest workload. This patch-set follows the main idea discussed at 2012 LSFMMS session: "Ballooning for transparent huge

Memory fragmentation introduced by ballooning might reduce significantly the number of 2MB contiguous memory blocks that can be used within a guest, thus imposing performance penalties associated with the reduced number of transparent huge pages that could be used by the guest workload. This patch-set follows the main idea discussed at 2012 LSFMMS session: "Ballooning for transparent huge

Memory fragmentation introduced by ballooning might reduce significantly the number of 2MB contiguous memory blocks that can be used within a guest, thus imposing performance penalties associated with the reduced number of transparent huge pages that could be used by the guest workload. This patch-set follows the main idea discussed at 2012 LSFMMS session: "Ballooning for transparent huge

Memory fragmentation introduced by ballooning might reduce significantly the number of 2MB contiguous memory blocks that can be used within a guest, thus imposing performance penalties associated with the reduced number of transparent huge pages that could be used by the guest workload. This patch-set follows the main idea discussed at 2012 LSFMMS session: "Ballooning for transparent huge

https://bugs.freedesktop.org/show_bug.cgi?id=110660 Bug ID: 110660 Summary: GeForce GT 750M Mac Edition fullscreen issues Product: Mesa Version: unspecified Hardware: Other OS: All Status: NEW Severity: normal Priority: medium Component: Drivers/DRI/nouveau Assignee: nouveau

Hi, I am using version 2.1-1 of the ff package. I have a data set with 80 million rows and I need to create a new ffdf object, subseting by values in one of the original ffdf's columns. Here is my code: bigData <- read.table.ffdf(file="/data/demodata/data/smallData.txt", next.rows=1e5, head=TRUE, sep="|") dim(bigData) N <- nrow(bigData);N select <- ff(