search for: 6.000000

Dear all, When calling rlm with the following data, I get an error. (R v.1.8.1, WinXP Pro 2002 with service pack 1.) > d <- na.omit(data.frame(CPRATIO, HEIGHTZ, FAMILYID)) > c <- tapply(d$CPRATIO, d$FAMILYID, mean) > h <- tapply(d$HEIGHTZ, d$FAMILYID, mean) > c 1 2 3 6 7 9 10 11 6.000000 2.500000 3.250000

Hello R-helpers: I have data like this: sample replicate height weight age A 1.00 12.0 0.64 6.00 A 2.00 12.2 0.38 6.00 A 3.00 12.4 0.49 6.00 B 1.00 12.7 0.65 4.00 B 2.00 12.8 0.78 5.00 C 1.00 11.9 0.45 6.00 C 2.00 11.84 0.44 2.00 C 3.00 11.43 0.32 3.00 C 4.00 10.24 0.84 4.00 D

I am using BM25 with k1=0 and min_normlen=1 to get weights unaffected by document length and term frequency in the document (min_normlen=1 isn't necessary I guess) and am expecting single-term weights to be identical for all matches. I have added a document value to steer such general search queries and it works fine, except that for some search terms, I get results like:

R-help, I have a character vector whose elements are the names of matrixes. Something like this: > test <- ls(pattern="Oki") [1] "aaOki" "aOki" "bOki" "c1Oki" "c2Oki" "c3Oki" "cOki" "dOki" "eOki" "fOki" "gprsOki" "hOki"

Hi, I have been able to generate association rules for Market Basket Analysis using the following codes: **************************************************************************** ******************************************* library("arules") rules <- read.csv("write1.csv",na.strings=c(".", "NA", "", "?"),header=TRUE)

Hi all, I have a large dataset with ~8600 observations that I want to compress to weekly means. There are 9 variables (columns), and I have already added a "week" column with 51 weeks. I have been looking at the functions: aggregate, tapply, apply, etc. and I am just not savvy enough with R to figure this out on my own, though I'm sure it's fairly easy. I also have the Dates

I'm using R on Windows v2.0.1 with the nlme package (v3.1-53) and am finding some unexpected discrepancies in the output of intervals.lme and coef.lme. I've included a toy dataset at the end, but briefly, the data are longitudinal data from couples in marital therapy. Each spouse's relationship satisfaction is measured 4 times; I've fit both linear and quadratic models to the

Dear all, I have a matrix with positive and negative values. >From this I would like to produce 2 matrices: 1st - retaining positives and putting NA in other positions 2nd - retaining negatives and putting NA in other positions and then apply rowMeans for both. I am trying to use the function ifelse in the exemplified form: ifelse(A>0,A,NA) but by putting A as a 2nd parameter it

UseRs, I am working on a dataset (see small example below) where individuals were followed on a specific date-time combo and multiple repeated measurements were taken (e.g., height in meters, behavior class in 2 letter code). Observation numbers varied between individual (ranging from 1 observation for each date-time combo to >50) I am trying to summarize the data into 1 row per

Hi all, I try to interpolate a data set in the form: time Erg 0.000000 48.650000 1.500000 56.080000 3.000000 38.330000 4.500000 49.650000 6.000000 61.390000 7.500000 51.250000 9.000000 50.450000 10.500000 55.110000 12.000000 61.120000 18.000000 61.260000 24.000000 62.670000 36.000000 63.670000 48.000000 74.880000 I want to get smoothed splines by using the class gam The first way I tried , was

Listmates, I installed 8.2 on opensuse 10.3 from the opensuse XGL repository to replace 7.6. Compiz works with the nvidia 8600 GT card (512M of GDDR3) on the box, but the ctrl+alt <- -> switching behavior is really bad. In 7.6, the ctrl+alt <- -> desktop switching changed desktops crisply with just the slightest hint of the cube detected during the very short and nice switch.

Hello. I have the following distance matrix between 8 points: [1,] 0.000000 3.162278 7.280110 8.544004 7.071068 9.899495 6.403124 8.062258 [2,] 3.162278 0.000000 5.000000 6.403124 4.472136 8.944272 6.082763 8.062258 [3,] 7.280110 5.000000 0.000000 1.414214 1.000000 5.000000 4.242641 5.830952 [4,] 8.544004 6.403124 1.414214 0.000000 2.236068 4.123106 4.472136 5.656854 [5,] 7.071068 4.472136

Hi all I have a 3d array containing missing values. > Xa , , 1 [,1] [,2] [1,] 1 3 [2,] NA 4 , , 2 [,1] [,2] [1,] 5 7 [2,] NA NA , , 3 [,1] [,2] [1,] 9 11 [2,] 10 12 I want to replace the missing values with the mean, but the mean of each 'page' in the array (wrong terminology I'm sure). So - for the array above - [2,1,2] and

Hi all, I have a vector, most of which is missing. The data is always increasing, but may do so in jumps. I would like to interpolate the NAs with 'best guesses', using something like filter(), which doesn't work due to the NAs. Here is an example: > x <- c(2,3,NA,NA,NA,3.2,3.5,NA,NA,6,NA) > x [1] 2.0 3.0 NA NA NA 3.2 3.5 NA NA 6.0 NA I would like a function that

Dear Rui, Thanks again for resolving this. I have already started using the version that works for me. But to clarify the second part, please let me paste the what I did and the error message: > set.seed(2024) > data <- data.frame( + Date = sample(seq(Sys.Date() - 5, Sys.Date(), by = "1 days"), 100L, + TRUE), + count = sample(10L, 100L, TRUE) + ) > > # coerce

I see no problem with your nvidia temps But, i have seen this exact behavior when 'indirect rendering' is enabled. On Wed, Aug 19, 2009 at 2:00 PM, <compiz-request at lists.freedesktop.org>wrote: > Send compiz mailing list submissions to > compiz at lists.freedesktop.org > > To subscribe or unsubscribe via the World Wide Web, visit >

?s 01:43 de 29/03/2024, Ogbos Okike escreveu: > Dear Rui, > Thanks again for resolving this. I have already started using the version > that works for me. > > But to clarify the second part, please let me paste the what I did and the > error message: > >> set.seed(2024) >> data <- data.frame( > + Date = sample(seq(Sys.Date() - 5, Sys.Date(), by = "1

Warm greetings to you all. Using the tapply function below: data<-read.table("FD1month",col.names = c("Dates","count")) x=data$count f<-factor(data$Dates) AB<- tapply(x,f,mean) I made a simple calculation. The result, stored in AB, is of the form below. But an effort to write AB to a file as a data frame fails. When I use the write table, it only produces

Hello, I'm using the stable amd64 port of Debian Linux. I installed (the latest) nut-2.0.1-4 and nut-usb packages for utilizing an APC Smart-UPS 1500 USB. I tried "apcupsd" first but "nut" makes an even more sophisticated impression on me and has more security options. I had problems with the newhidups driver. It didn't find a device with matching VendorID. And

Fellow R Users: I'm not extremely familiar with lda or R programming, but a recent editorial review of a manuscript submission has prompted a crash cousre. I am on this forum hoping I could solicit some much needed advice for deriving a classification equation. I have used three basic measurements in lda to predict two groups: male and female. I have a working model, low Wilk's lambda,