similar to: Linear model predictions, differences in class

Hi, I tried to inverse a matrix but it doesn`t work. I hope somebody can help me. This is what I did. > kurse <- read.table("kurse.txt", header=T, dec=",") > x <- cbind(1,kurse[,-c(1)]) > y <- kurse$index > t(x) %*% x Error in t(x) %*% x : requires numeric matrix/vector arguments > x <- as.matrix(x) > xtxi <- solve(t(x) %*% x) Error in

Hi, In a function, I compute 10 (un-named) vectors of reasonable length (4471 in the particular example I have to hand) that I want to combine into a data frame object, that the function will return. This is very slow, so *I'm* doing something wrong if I want it to be quick and efficient, though I'm not sure what the best way to do this would be. I know it is the combining into data

Hej, i need the which() funktion to find the positions of an entry in a matrix. the entries i'm looking for are : seq(begin,end,0.01) and there are no empty spaces i'm searching in the right range. so i was looking for the results R can find and i recieved this answer. for (l in

I've been using parApply() in snow package for parallel computing with the following lines in R 2.8.1: library(snow) nNodes <- 4 cl <- makeCluster(nNodes, type = "SOCK") fm <- parApply(cl, myData, c(1,2), func1, ...) Since I have a Mac OS X (version 10.4.11) with two dual-core processors, I thought that I could run 4 simultaneous clusters. However with the 1st job

Hi, Is nchar function knowingly slow in R? I'm doing some string formatting that requires multiple call to nchar, and nchar seems to be very slow. Experiment 1, pass nchar inside sprintf, and it takes 0.7 seconds > system.time(for (i in 1:10000) + str = sprintf('0005%020d', nchar(op)) + )[3] [1] 0.7 Experiment 2, get the length of op separately using nchar, and then pass

Dear R users, I have more of a statistical/econometric question than straight R one. I have a data set with the discrete hazard rate of small firms survival on 400 counties over a period of 9 years. This data was generated using census information from the VAT registration number of each one of these business. I would like to analyze the effect of regional factors (deprivation index, real

Thanks Don and Jim, "Get" did the trick! That command is new to me, and is exactly what this problem needed. The syntax that worked is actually slightly simpler than what was suggested. This is what worked: all.comps <- ls(pattern="^res") for(i in all.comps){ obj = get(i); ... Thanks again. --Kelly V. -----Original Message----- From: MacQueen, Don [mailto:macqueen1 at

Dear All, 1.I have been trying to aggregate my data but I have not been getting it correctly. In the data I want to convert hourly data into daily averages. Here is a sample of the data: Neph_no Date Time Temp_C Pressure_kPa RH 9 2014/03/28 10:00:00 38.4 95.9 29.7 9 2014/03/28 11:00:00 37.8 95.8 29.2 9 2014/03/28 12:00:00 36.7 95.8 35.1 9 2014/03/28 13:00:00 35.4 95.8 38.9 9

Dear all, 1) I have a very large matrix of  str(keep)  num [1:153899, 1:3415] -98.6 -95.8 -96.4 -95.8 -98 ... that  I would like to reduce its size to something like str(keep)  num [1:1000, 1:3415] -98.6 -95.8 -96.4 -95.8 -98 ...  or anything similar in size as this is a matrix that needs plotting (so is ok if it is 1000 row, 995, or 1123) I think what I need here is a way of selecting

Dear all, I have a zoo object that has following structure: > str(bldata) zoo [1:5219, 1:12] 91.9 91.8 91.7 91.8 91.7 ... - attr(*, "index")=Classes 'dates', 'times' atomic [1:5219] 7305 7306 7307 7308 7309 ... .. ..- attr(*, "format")= chr "m/d/y" .. ..- attr(*, "origin")= Named num [1:3] 1 1 1970 .. .. ..- attr(*,

Hi, We did some performance test and found the create/delete files performance of btrfs is very poor. The test is that we create 50000 files and measure the file-create time first, and then delete these 50000 files and measure the file-delete time. (The attached file is the reproduce program) The result is following: (Unit: second) Create file performance BtrFS Ext4 Total times:

Hello, I have a small problem with dovecot 1.0RC2. This didn't happen with former versions. There are imap processes that are left open a long while after the client disconnected. I am running dovecot on OpenBSD 3.8. Below is the output of a process list. As you can see, there are many imap processes for the same login running. simon 11593 0.0 0.1 924 1140 ?? I 12:21PM

I've been running classic icecast + liveice for some time now (several years) without problems. A couple days ago, the station manager (NPR affiliate) came to me and asked if we could 'inject' a static file into the stream. Basically what he wants is for the client to first receive a static file (requesting donations of course) and then on to the live stream (*not* intermittently).

glm(S ~ -1 + Mdif, family=quasipoisson(link=identity), start=strt, sdat) gives error: > Error in glm.fit(x = X, y = Y, weights = weights, start = start, etastart > = > etastart, : > cannot find valid starting values: please specify some strt is set to be the coefficient for a similar fit glm(S ~ -1 + I(Mdif + 1),... i.e. (Mdif + 1) is a vector similar to Mdif. The error

Hello, dear R-community. This is a question about TukeyHSD between factor combinations of a Three-Way ANOVA, which is - since it is a multi measure ANOVA - not a simple ANOVA but a Generalised Linear Mixed Model (GLMM), calculated with "lmer". > growth <- groupedData(length~meas|box_id,outer=~spec*comp*water,data=all.spec) > model <-

Hi , I would like to use ggplot2 to plot a matrix as an image. You can copy paste the following Data<-matrix(data=rnorm(900,80,20),nrow=30,ncol=30) lengthOut<-5 Lengths<- 15 library(reshape2) library(ggplot2) tdm <- melt(Data) ggplot(tdm, aes(x = Var2, y = Var1, fill = factor(value)),levels=seq(0,1,by=0.1)) +                   labs(x = "MHz", y =

i know with icecast2/ices , karl heyes has some example config files for mixing live/static content ( if i recall correctly ) . however, what i had done in the past is add the static file to the .pls prior to the live mount . if you generate the .pls files on demand , this would work . ALSO , i'm quite sure that both icecast1/2 support intro/outtro files , etc .. this may achieve the same

Dear Community, I have been using two types of survival programs to analyse a data set. The first one is an R function called aftreg. The second one an STATA function called streg. Both of them include the same analyisis with a weibull distribution. Yet, results are very different. Shouldn't the results be the same? Kind regards, J -- View this message in context:

My box plot below is drawing its upper whisker all the way to the last point, instead of showing the point as an outlier. Am I misunderstanding, or is it a bug? Help(boxplot) states for the parameter ?range? that ?this determines how far the plot whiskers extend out from the box. If range is positive, the whiskers extend to the most extreme data point which is no more than range times the

Dear R People: I'm using R2HTML but having a strange result. Here is the original data: resp trt block 90.3 A I 89.2 A II 98.2 A III 93.9 A IV 87.4 A V 97.9 A VI 92.5 B I 89.5 B II 90.6 B III 94.7 B IV 87.0 B V 95.8 B VI 85.5 C I 90.8 C II 89.6 C III 86.2 C IV 88.0 C V 93.4 C VI 82.5 D I 89.5 D II 85.6 D III 87.4 D IV 78.9 D V 90.7 D VI And here are the commands: > resin1.df <-