similar to: Errors melt()ing data...

Hi, I am new to R. I have a matrix that I have assigned to the object ?colon?. > colon<-read.table("c:\\alon.txt",header=T,row.names=1) attach(colon) names(colon) The dimenstions are 2000 62. Each of the 62 columns (titled norm1, norm2, norm3, etc) has 2000 different numbers (?continuous? values) within it. I have also assigned a name for each of the 2000 rows of the

Hi, See below I reply your message for <https://stat.ethz.ch/pipermail/r-help/2008-April/160966.html>[R] predict.glm & newdata posted on Fri Apr 4 21:02:24 CEST 2008 You say it ##works fine but it does not: if you look at the length of yhat2, you will find 100 and not 200 as expected. In fact predict(reg1, data=x2) gives the same results as predict(reg1). So I am still looking for

Hello, I have two problems in automating multiple glm(s) operations. The data file is tab delimited file with headers and two columns. like "ABC" "EFG" 1 2 2 3 3 4 dat <- read.table("FILENAME", header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE) dataf <- read.table("FILENAME", header=FALSE,

Hello All: This series is an update version of multiqueue virtio-net driver based on Krishna Kumar's work to let virtio-net use multiple rx/tx queues to do the packets reception and transmission. Please review and comments. Test Environment: - Intel(R) Xeon(R) CPU E5620 @ 2.40GHz, 8 cores 2 numa nodes - Two directed connected 82599 Test Summary: - Highlights: huge improvements on TCP_RR

Hello All: This series is an update version of multiqueue virtio-net driver based on Krishna Kumar's work to let virtio-net use multiple rx/tx queues to do the packets reception and transmission. Please review and comments. Test Environment: - Intel(R) Xeon(R) CPU E5620 @ 2.40GHz, 8 cores 2 numa nodes - Two directed connected 82599 Test Summary: - Highlights: huge improvements on TCP_RR

Hello, I want to use pearson's correlation as distance between observations and then use any centroid based linkage distance (ex. Ward's distance) When linkage distances are formed as the Lance-Williams recursive formulation, they just require the initial distance between observations. See here: http://en.wikipedia.org/wiki/Ward%27s_method It is said that you have to use euclidean

Hi, I'm having a bit of trouble with the axes in my plots. I don't like the way R does not have them cross in the origin. Is there another plot/axis function? i tried using abline as suggested by someone from this list, but in my case this gives no satisfactory result, as the abline does sometimes lie on top of the y axis and sometimes not, depending on how i scale the image... #bild16

Hi all - I'm stumped by the following mdl <- glm(resp ~ . , data = df, family=binomial, offset = ofst) WORKS yhat <- predict(mdl) WORKS yhat <- predict(mdl,newdata = df) FAILS Error in drop(X[, piv, drop = FALSE] %*% beta[piv]) : subscript out of bounds I've tried without offset, quoting binomial. The offset variable ofst IS in df. Previous postings indicate possible

Hi I Have a very large dataset that I would like to conduct ANOVA tests on. Im not a very strong programmer so any help would be appreciated. the format is Identifier             A1       A2        B1      B2       C1   C2      Norm1         Norm2 1234                  1        1            NA     NA      4       3        NA               NA 4567                  2        2              4     

Hello All: This series is an update version of multiqueue virtio-net driver based on Krishna Kumar's work to let virtio-net use multiple rx/tx queues to do the packets reception and transmission. Please review and comments. Test Environment: - Intel(R) Xeon(R) CPU E5620 @ 2.40GHz, 8 cores 2 numa nodes - Two directed connected 82599 Test Summary: - Highlights: huge improvements on TCP_RR

Hello All: This series is an update version of multiqueue virtio-net driver based on Krishna Kumar's work to let virtio-net use multiple rx/tx queues to do the packets reception and transmission. Please review and comments. Test Environment: - Intel(R) Xeon(R) CPU E5620 @ 2.40GHz, 8 cores 2 numa nodes - Two directed connected 82599 Test Summary: - Highlights: huge improvements on TCP_RR

Hi Hadley et all, I am struggling with reshape2 and melt works and melt_check (filtered151) seems fine My cast command was acast (filtered151, Time ~ Species ~ Number) > melt_check (filtered151) Using time, Species as id variables $id [1] "time" "Species" $measure [1] "Number" When I execute cast the data matrix is in the correct order but the data

Dear R-helpers, We marked 6000 leaves from 5 SPECIES - 10 individuals/species - in two different TREATMENTs: a control and a dry-plot from which 50% of incoming precipitation was excluded. We followed those leaves for 42 months and noted the presence and absence at each visit. I then carried out a Cox Harzard model to see differences in leaf mortality between parcels and among species over time: