similar to: adressing dataframes

Hi everyone, I am desperately looking for a method to interpolate strings within an SQL statement as follows: I get a lot of rows out of a database (in my example POSITION_to_ZIPCODE Database with holds records for German ZIP Code <--> Gauss-Krueger Coordinate System ) and want this to be selected and computed individually row by row as follows: library(RODBC) channel <-

Hello all, I'm fitting an allometric equation that looks like a really clean fit in the log-log space, but when I back transform the fit of the curve appears to need an adjustment - the fitted curve appears to predict values a good deal higher than those from the data. I included a bias correction per Newman, M. (1993). Regression analysis of log‐transformed data: Statistical bias and its

Hello everybody, I would like to replace or recode a list of numbers between 1 and 12 (total 100). I have tried to make it with recode, but i have two types of replacements. For 1,2,3,4,11,12 => invierno and for 5,6,7,8,9 and 10 => verano. recode(datos.mx1[,7], "1='invierno'; 2='invierno';

Hi R experts, I'm attempting to run Linear Discriminant Analysis using the lda function in the MASS package. I've got around 50 predictor variables and one response variable. My response variable has 5 numeric categories that represent different clusters of fish abundance data (clusters were developed using Bray-Curtis and NMDS), and my predictor variables are environmental variables that

Dear R-Users, my dataset contains timeseries that are structured into task-specific epochs as labelled by a factor and a boolean variable for labelling each data-point within the series as artefact or non-artefact. Now there's no problem in addressing continuous task-specific time-series that are free from artefacts as long as artefacts extend to either boundary of adjacent timeseries but

Hi all, I'm trying figure out how to implement support for Small Data Addressing (SDA) for an out-of-tree PowerPC target. The short description of SDA is that small globals are gathered in the same memory area and addressed using a base register + an offset. From what I've seen the Hexagon target already support this but as that target doesn't seem to support writing object files it

Dear all, I'm using R 2.8.1 under Vista. I programmed a Simulation with the code enclosed at the end of the eMail. After the simulation I want to analyse the columns of the single simulation-runs, i.e. e.g. Simulation[[1]][,1] sth. like that but I cannot address these columns... Can anybody please help? Best, Thomas ############################ CODE ############################

Dear all, I'm sitting here just in front of my new PC@work and wonder about the following question: * How can I adress multiple CPUs (cores) out of R to speed up the simulations I run? * What are the prerequisites to do so? Maybe anyone could give me a hint where to start reading? Regards, Thomas P.S.: I searched the R-archive to find an answer but did find none.

Hi, would like to use LLVM as backend for a compiler. One of the features I would like to implement is segment based addressing for position independent data. For some it may sound strange, for others the opposite. No need to write complex story. Imagine you have a custom alocator that manages an area of 1GB of memory. Your application uses a custom allocator to allocate memory inside this area,

#this is my stab at - I am sure that I am missing something. If this doesn't make sense then please ask for more details. #This may show my low level of programing knowledge hester. <- c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4) value <- rnorm(16) x <- data.frame(value, hester.) z <- (if(x[,"hester."]==1){ x[,"value"]*6.250} else if(x[,"hester."]==2){

Thanks again for your help! > >> > >> Probably fairly minimal in most cases (on x86). On ARM there is > >> definitely a cost. > >> > > hm... why? You cannot have indexed addressing? > What I need is a way to force > The code that needs to be emitted is roughly: > [..."segment"-offset into x1...] > mrs x0, tpidr_el0 >

Hi, I've lost my mind on it... I have to scatterplot two vectors, grouped by a third variable, with two different dimensions according to whether each cell line in the plot is sensitive or resistant to a given drug, and with a different color for each of 9 tissues of origin. Here's what I've done:

please add the following diffs to the vorbis/vq dir. - include files changed so things actually compile in new scheme - _ogg_...alloc cleanups caught a half-dozen typos or so - minor Makefile touchup. (stuff is still not tested, but this will compile at least) Would someone with cvs write access commit them for me please? Erik diffs: ------------------------ diff -bBu2r vorbis/vq/Makefile

I have a file, each column of which is a separate year, and each row of each column is mean precipitation for that month. Looks like this (except it goes back to 1964). month X2000 X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009 1 1.600 1.010 4.320 2.110 0.925 3.275 3.460 0.675 1.315 2.920 2 2.960 3.905 3.230 2.380 2.720 1.880 2.430 1.380

I have a data matrix that contains site data for paired infested and control plots. For example: ab ac ad af ag ah 1 .024 x x x x x 2 .9 x x x x x 3 1.5 x x x x x 4 2.3 x x x x x 5 1.0 x x x x x 1c .75 x x x x x 2c 2.1 x x x x x 3c 1.7 x x x x x 4c 10.2 x x x x

Dear All: Could you please tell me how I can draw figure formatted like figure 4.18 of MASS (4th) with the attached data set? Thanks Zhongming Yang --------------------------------- -------------- next part -------------- An embedded and charset-unspecified text was scrubbed... Name: sample.txt Url: https://stat.ethz.ch/pipermail/r-help/attachments/20050316/abfdb85e/sample.txt

Dear R people, This is probably a FAQ, and a silly question besides, but... a lot of R packages have datasets contained within them. cf. spatial has "topo". Once I have loded the appropriate library, how do I access the datasets contained within? I know this must be very simple, but I have spent some (fruitless, frustrated time) trying to do this, so am asking for help.

How can I from the summary function, decide which glm (fit1, fit2 or fit3) fits to data best? I don't know what to look after, so I would please explain the important output. > fit1 <- glm(Y~X, family=gaussian(link="identity")) > fit2 <- glm(Y~X, family=gaussian(link="log")) > fit3 <- glm(Y~X, family=Gamma(link="log")) > summary(fit1) Call:

I am hoping for some advice regarding limiting decimal points to 3. 'Round' produces the desired results except for the 97.5% confidence interval. Any advice as to how I modify the code to obtain output to 3 decimal points for all ouput is appreciated, regards Bob Green mod.multgran <-multinom(offence ~ grandiose * violent.convictions, data = kc, na.action = na.omit)

Looking for a little help figuring out what's driving gaps in data after merging two xts objects (msci.m and x2). The merge statement I'm using is ... y <-merge(x2,msci.m, all=FALSE). Here's info on the output , y: head(y) t-bill msci Sep 1985 7.310 316.963 Mar 1986 6.560 463.471 Jun 1986 6.180 498.791 Jul 1987 6.200 778.898 Aug 1987 6.400 833.519 Nov 1987