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...54.3") as.numeric(test) # fails as.numeric(gsub(",","",test)) # works Any suggestions? Or is this as good as it gets? I'm not complaining ... just curious! Drew Tyre School of Natural Resources University of Nebraska-Lincoln 416 Hardin Hall, East Campus Lincoln, NE 68583-0974 phone: +1 402 472 4054 fax: +1 402 472 2946 email: atyre2@unl.edu http://snr.unl.edu/tyre [[alternative HTML version deleted]]

...ompt and there is no help entry for it. Also, there is no obvious file name in boot_1.2-24.zip at http://cran.r-project.org/src/contrib/Descriptions/boot.html for the specific function. Thank you in advance! Chris Bilder University of Nebraska-Lincoln Department of Statistics Lincoln, NE 68583 E-mail: cbilder3@unl.edu or chris@chrisbilder.com Website: www.chrisbilder.com [[alternative HTML version deleted]]

...s.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base loaded via a namespace (and not attached): [1] tools_2.14.2 -- Drew Tyre School of Natural Resources University of Nebraska-Lincoln 416 Hardin Hall, East Campus 3310 Holdrege Street Lincoln, NE 68583-0974 phone: +1 402 472 4054 fax: +1 402 472 2946 email: atyre2@unl.edu http://snr.unl.edu/tyre http://aminpractice.blogspot.com http://www.flickr.com/photos/atiretoo [[alternative HTML version deleted]]

Honestly I don't understand it. First Oneiric Ocelot splits Ubuntu users right down the middle, next thing I know WINE is very unstable after the 1.3.30 update. Here's my problem. I run WINE in Ubuntu Natty Narwhal (11.04). All my apps were running quite happily on version 1.3.28/29 this includes apps: Photoshop CS5 extended Live For Speed Cheat Engine and various other little mods

Hello, I used the glm function in R to fit a dose-response relationship and then have been using dose.p to calculate the LC50, however I would like to calculate the NOEL (no observed effect level), ie the lowest dose above which responses start occurring. Does anyone know how to do this? [[alternative HTML version deleted]]

Hey all, I need some help with a cross validation. I'm new with R and as well with statistics. I had a group work to create a tool for remote sensing class that extracts the best bands of hyperspectral satellite images that describe vegetation. Its a regression between a linear function of using a normalized differenced index (i-j)/(i+j) while i and j are the bands (in the data these are the

...ommented, minimal, self-contained, reproducible code. >>> >> >> >> >> -- >> Drew Tyre >> >> School of Natural Resources >> University of Nebraska-Lincoln >> 416 Hardin Hall, East Campus >> 3310 Holdrege Street >> Lincoln, NE 68583-0974 >> >> phone: +1 402 472 4054 >> fax: +1 402 472 2946 >> email: atyre2 at unl.edu >> http://snr.unl.edu/tyre >> http://aminpractice.blogspot.com >> http://www.flickr.com/photos/atiretoo >> > > ? ? ? ?[[alternative HTML version deleted]] >...

Hi all, I am looking for low cost online education in statistics. I am thinking of taking online classes on time series analysis and cointegration, etc. Of course, if there are free video lectures, that would be great. However I couldn't find any free video lectures at upper-undergraduate and graduate level which formally going through the whole timeseries education... That's why I would

Hi All In the past I have worked with parallel processing in R where a function F is applied to the elements of a list L. The more cpu cores one has, the faster the process will run. At the time of launching the process for (F,L) I will have a certain fixed number of cpu's that I can use. I have tested this approach and it works fine (i.e. package 'multicore' , using 'mapply'

Dear list, I am trying to reclaim what I think is lost memory in R, I have been using gc(), rm() and also using Rprof to figure out where all the memory is going but I might be missing something. I have the following situation basic loop which calls memoryHogFunction: for i in (1:N) { dataset <- generateDataset(i) fit <- try( memoryHogFunction(dataset, otherParameters)) } and

Hello: While running R doing the analysis of my data I (using packages such as BIOMOD or e1071) get the following error as a result of several of my analysis: Error: cannot allocate vector of size 998.5 Mb In addition: Warning messages: 1: In array(c(rep.int(c(1, numeric(n)), n - 1L), 1), d, dn) : Reached total allocation of 4095Mb: see help(memory.size) 2: In array(c(rep.int(c(1,

...login. Dec 13 08:42:38 mailserver pop3-login: Login: robin [::ffff:192.168.1.103] Dec 13 08:42:53 mailserver pop3-login: Disconnected [::ffff:192.168.1.246] Dec 13 08:43:04 mailserver sendmail[16422]: jBD6fU3g016421: to=<Luis@dubaisa.co.za>, delay=00:01:32, xdelay=00:00:30, mailer=local, pri=68583, dsn=2.0.0, stat=Sent Dec 13 08:43:05 mailserver pop3-login: Disconnected [::ffff:192.168.1.217] Dec 13 08:43:08 mailserver pop3-login: Disconnected [::ffff:192.168.1.81] Dec 13 08:43:16 mailserver sendmail[16463]: jBD6hFwK016463: from=<russells@dubaisa.co.za>, size=2112751, class=0, nrcpts=1...

Hi, I have a query which I would like to translate into R, but I do not know how to do it in an easy way. Assume a data frame has columns A, B and C: A B C 1 1 3 1 1 4 1 1 5 1 2 6 1 2 7 1 3 8 The query is as follows: select A, B, count(*) from data.frame group by A, B order by count(*) desc How do I translate this into R statements in such way that the result is a data frame structured as

Everyone, I'm very new to R, especially when it comes to loops and functions, so please bear with me if this is an elementary question. I cannot seem to figure out how to construct a loop which runs a function until a certain value is computed. For example, say I have the following: num = numeric (10) num [1] = 0 for (i in 2:10) { num [i] = num [i-1] + 5 } This adds 5 to the

I am interested in testing two similar nls models to determine if the lines are statistically different when fitted with two different data sets; one corn, another soybean. I know I can do this in linear models by testing for interactions. See Introductory Statistics with R by Dallgaard p212-218 for an example. I have two different data sets I am comparing to lai. ci.re should have very

I have data that looks like this: > df1 group id 1 red A 2 red B 3 red C 4 blue D 5 blue E 6 blue F I want a list of the groups containing vectors with the ids. I am avoiding subset(), as it is only recommended for interactive use. Here's what I have so far: df1 <- data.frame(group=c("red", "red", "red", "blue",