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Dear all, I am running R 2.4.1. > library(siggenes); > library(multtest); > cl<-rep(c(0,1),c(3,3)); > sub<-exprs(AffyExpData[,c(1:3,7:9)]); > gn<-geneNames(AffyRAwData); > sam.out<-sam(sub,cl,rand=123,gene.names=gn); We're doing 20 complete permutations > sam.out SAM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances Delta p0

Dear List, Is the test for proportional hazards valid when the model contains a cluster 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)

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

I want to know how accurate are the p-values when you do linear regression in R? I was looking at the variable x3 and the t=10.843 and the corresponding p-value=2e-16 which is the same p-value for the intercept where the t-value for the intercept is 48.402. I tried to calculate the p-value in R and I got 0 x<-2*(1-pt(10.843,2838)) > x [1] 0 > G<-lm(y~x1+x2+x3+x4+x5) >

I am using acf() to get the autocorrelations of a time series. It works but I want to then get the autocorrelations into a simple list of numbers. > x <- acf(price_changes, lag.max = 12,type = "correlation",plot = FALSE) > x Autocorrelations of series ‘price_changes’, by lag 0 1 2 3 4 5 6 7 8 9 10 11 12 1.000

library(plyr) n<-100000 grp1<-sample(1:750, n, replace=T) grp2<-sample(1:750, n, replace=T) d<-data.frame(x=rnorm(n), y=rnorm(n), grp1=grp1, grp2=grp2) system.time({ d$avx1 <- ave(d$x, list(d$grp1, d$grp2)) d$avy1 <- ave(d$y, list(d$grp1, d$grp2)) }) # user system elapsed # 39.300 0.279 40.809 system.time({ d$avx2 <- ave(d$x, interaction(d$grp1, d$grp2, drop =

Dear R-Helpers, I've looked high and low for a function that provides frequencies, proportions and cumulative proportions side-by-side. Below is the table I need. Is there a function that already does it? Thanks, Bob > # Generate some test scores > myValues <- c(70:95) > Score <- ( sample( myValues, size=1000, replace=TRUE) ) > head(Score) [1] 77 71 81 88 83 93 > >

I have a table statement that returns the following: [10.839,10.841] (10.841,10.843] (10.843,10.846] (10.846,10.848] (10.848,10.85] 0 0 0 0 1 (10.85,10.852] (10.852,10.854] (10.854,10.857] (10.857,10.859] (10.859,10.861] 0 0 0 0 0 What I want to do is get the upper bound

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Dear friends, In R, the help of "bic.glm" tells the difference between postmean(the posterior mean of each coefficient from model averaging) and condpostmean(the posterior mean of each coefficient conditional on the variable being included in the model), But it's still unclear about the results explanations, and the artile of Rnews in 2005 on BMA still don't give more detail on

Hi! Some background: I am trying to create an application that would encode video taken by USB camera using Theora and then send it to the client. I have almost succeeded, but I have one problem. When I grab video frames from the camera and encode them they form 4KB OGG pages, then I send them over TCP/IP to the client application. Since I want to achieve as small latency as possible I

Hi, In my analysis of impacts of insecticide-treated bednets on malaria, I look at the relationship between malaria incidence and mosquito behaviors. The condensed data set is copied here. Ordinary regression (lm) shows that Incidence was negatively related to Mortality. This makes sense because the latter reflected the strength of killing mosquitoes by insecticide-treated nets. Since the

I have searched google high and low for answers to this...and I have gotten many examples, howto, etc...but they all seem to have a slightly different configuration, and therefore, slightly different problems. Unfortunately, not enough of them show the network layout, along with the configuration, so it's hard to tell why certain IP are being used, and were they are on the network. I have

Hi, I am analyzing a data set with greater than 1000 independent cases (collected in an unrestricted manner), where each case has 3 variables associated with it: one, a factor variable with 0/1 levels (called XX), another factor variable with 8 levels (X) and a third response variable with two levels (Y: 0/1). I am trying to see if X1 has an effect on the relationship between X2 and the

I try to get working together vlan and bonding both for dom0 and domU. I lost packets sent to dom0 while domU is OK. Nightly stats for dom0: 52879 packets transmitted, 45293 received, 14% packet loss, time 52879599ms rtt min/avg/max/mdev = 0.144/0.224/717.306/5.129 ms Nightly stats for domU: 52952 packets transmitted, 52952 received, 0% packet loss, time 52952554ms rtt min/avg/max/mdev =

Dear All- I have a dataset that is comprised of the following: doy yr mon day hr hgt1 hgt2 hgt3 co21 co22 co23 sig1 sig2 sig3 dif flag 244.02083 2005 09 01 00 2.6 9.5 17.8 375.665 373.737 373.227 3.698 1.107 0.963 -0.509 PRE 244.0625 2005 09 01 01 2.6 9.5 17.8 393.66 384.773 379.466 15.336 11.033 5.76 -5.307 PRE 244.10417 2005 09 01 02 2.6 9.5 17.8 411.162 397.866 387.755 6.835 5.61 6.728

Hello, I am new to the R package. After I use R to perform the hierarchical clustering, I am only interested in retrieving the leaf nodes that share the last common ancestors. As illustrated below, I'd like to retrieve (B, C) as a cluster and then (D, E) as another cluster. Any chance to do this in R? Thanks! BTW, I just subscribed to this list (not sure if the subscription is

I have a data frame (narrow) with 431 rows and 6 columns containing information on chromosome, position, lod1, lod2, lod3, lod4, looking like this: > narrow chr pos lod1 lod2 lod3 lod4 1 1 3.456 -0.025 -0.003 -0.209 -0.057 2 1 5.697 -0.029 -0.005 -0.200 -0.058 3 1 8.434 -0.049 -0.012 -0.247 -0.092 4 1 9.466 -0.074 -0.025 -0.300 -0.136 5 1 9.706

Hi everyone, I have been working with the ccf function recently, and in particular to do my calculations I have been using "na.action = na.pass". I noticed that the help documentation mentions that with this option the computed estimate may not be a valid autocorrelation sequence and was wondering if anyone could clarify what this means. In particular, the example below gives

Hi everyone, I have a quick and probably easy question about lme for this list. Say, for instance you want to model growth in pituitary distance as a function of age in the Orthodont dataset. fm1 = lme(distance ~ I(age-8), random = ~ 1 + I(age-8) | Subject, data = Orthodont) You notice that there is substantial variability in the intercepts (initial distance) for people at 8 years, and that