similar to: Help with Time Series Plot‏

R-listers: I may be asking too much from R, but is there a way to use time indexing on a time series object. For instance: > tsobject <- ts(1:12, start =1999, freq = 4) > tsobject Qtr1 Qtr2 Qtr3 Qtr4 1999 1 2 3 4 2000 5 6 7 8 2001 9 10 11 12 > tsobject[1999,Qtr4] Error in NextMethod("[") : Object "Qtr4" not found I would

Dear List, Is there a way I can sort a sample based on a sort index constructed from the data from which the sample is taken? Basically, I need to take 'many' samples from the same source data and sort them. This can be very time consuming for long vectors. Is there any way I can sort the data only once initially, and use that sort order for the samples? I believe that idea is what is

Hi All, I'm planning to submit my first package to R, and although I read all the documentation, I'm not very clear on the following 2 items, from which I'd appreciate your guidance: 1)I understand it is suggested to use the R dev version to build the package. Which one specifically should I use to build a package on a Mac OS? How about package dependencies, which version should I

Hi All, I didn't have much success through my Google search in finding any active R-related projects to create a wrapper around TensorFlow in R. Anyone know if this is on the go? Thanks, Axel. [[alternative HTML version deleted]]

Hi All, I didn't have much success through my Google search in finding any active R-related projects to create a wrapper around TensorFlow in R. Anyone know if this is on the go? Thanks, Axel. [[alternative HTML version deleted]]

Dear List, The gbm package on Win 7 produces different results for the relative importance of input variables in R 32-bit relative to R 64-bit. Any idea why? Any idea which one is correct? Based on this example, it looks like the relative importance of 2 perfectly correlated predictors is "diluted" by half in 32-bit, whereas in 64-bit, one of these predictors gets all the importance

Dear List, I'm trying to solve this simple optimization problem in R. The parameters are the exponents to the matrix mm. The constraints specify that each row of the parameter matrix should sum to 1 and their product to 0. I don't understand why the constraints are not satisfied at the solution. I must be misinterpreting how to specify the constrains somehow. library(alabama) ff <-

Dear R users, I know this issue came up in the list several times. I’m currently running R on 32-bit on Windows and due to memory limitation problems would like to move to a 64-bit environment. I’m exploring my options and would appreciate your expertise: 1) Windows 64-bit: Prof. Brian Ripley recently posted the experimental built of R for win 64-bit. I’ll appreciate any feedback on

Dear List, I'm trying to add a function that calls a Shiny App in my R package. The issue is that within my function, I'm creating objects that I'd like to pass to the app. For instance, from the example below, I'm getting "Error: object 'xs' not found". How can I pass "xs" explicitly to shinyApp()? *Under R directory:* myApp <- function(x, ...) {

Hi, I'm looking to move from Windows into a 64-bit Linux environment. Which is the best Linux Flavor to use within R? To install R on this environment, do I need to do any compiling? Thanks all! Axel. [[alternative HTML version deleted]]

Dear Axel, I've used environment for such problems. assign("xs", xs, envir = my.env) in the myApp function get("xs", envir = my.env) in the server function Best regards, ir. Thierry Onkelinx Statisticus/ Statiscian Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie &

Dear list, For a reason it would take me long to explain, I need to do something along the lines of what's shown below -- i.e., create an object from dplyr::summarise, and then evaluate it on a data frame. I know I could directly do: df %>% dplyr::summarise(x1_mean = mean(x1)) but this is not what I'm looking for. library(dplyr) df <- data.frame(x1 = rnorm(100), x2 =

Dear List, I have a wrapper function that creates a Shiny App, as illustrated below. I'd like to include the function myApp() inside a package. I'd appreciate your guidance here, as I could not find good instructions on this online. myApp <- function(x) { require(shiny) shinyApp( ui = fluidPage( sidebarLayout( sidebarPanel(sliderInput("n",

Thank you Thierry. I'm trying to following your suggestion in the example below, but getting: Error in get("xs", envir = my.env) : object 'my.env' not found. library(shiny) library(shinydashboard) myApp <- function(x, ...) { xs <- scale(x) my.env <- new.env() assign("xs", xs, envir = my.env) shiny::runApp(app) } app = shinyApp( ui =

Dear users, I'll appreciate your help with this (hopefully) simple problem. I have a model object which was fitted to inputs X1, X2, X3. Now, I'd like to use this object to make predictions on a new data set where only X1 and X2 are available (just use the estimated coefficients for these variables in making predictions and ignoring the coefficient on X3). Here's my attempt but, of

I'll appreciate your help on this. I have values stored in a list as in "mylist" below. I need to sum the values over all elements of the list aggregated by the names of the matrices. mylist <- list(matrix(c(0.2, 0.4), 1, 2, dimnames = list(NULL, c("1", "2"))), matrix(c(0.1, 0.5), 1, 2, dimnames = list(NULL, c("1",

Hi, My apologies for asking this question, but could not find the answer elsewhere. I understand the glmnet package uses Fortran code. For example, the lognet.R file includes the lines of code shown below. But how can I see the Fortran code that is being referenced in the code below? Is that provided somewhere in the package source code? .Fortran("lognet",

Dear users, My apologies for the simple question. I'd like to create a function where the number of arguments is as big as the size of my data set. Supose I have n observations in my data, how can I write a function like fun <- function (x1,x2,....,xn) {x1*x2*.......*xn} Thanks in advance for your help! Axel. [[alternative HTML version deleted]]

Dear List, When I try to install tweetR on Ubuntu, I get the error message below. It is a problem with the dependency RCurl. This package is not available for Windows on CRAN, but I would assume that I should have no problem with linux. Any help is much appreciated. R version: 2.12.1 Platform: x86_64-pc-linux-gnu (64-bit) * installing *source* package ‘RCurl’ ... checking for curl-config... no

Dear List, I think I'm going crazy here...can anyone explain why do I get the same predictions in train and test data sets below when the second has a missing input? y <- rnorm(1000) x1 <- rnorm(1000) x2 <- rnorm(1000) train <- data.frame(y,x1,x2) test <- data.frame(x1) myfit <- glm(y ~ x1 + x2, data=train) summary(myfit) all(predict(myfit, test) == predict(myfit, train))