similar to: Predictions with 'missing' variables

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

Dear R users, My apologies for the simple question, as I'm starting to learn the concepts behind the Cox PH model. I was just experimenting with the survival and rms packages for this. I'm simply trying to obtain the expected survival time (as opposed to the probability of survival at a given time t). I can't seem to find an option from the "type" argument in the predict

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 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 &

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 =

I'm trying to get the following section of code to work, I think the problem is being caused by the assignment of data to the lm function not evaluating to "train" in the parent environment but I can't seem to figure out how to do this. fitmodel <- function(trial,data) { wrap.lm <- function(formula,data,...) { cat("in wrap lm",NROW(data),"\n");

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

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 <-

Hey List, I did a multiple regression and my final model looks as follows: model9<-lm(calP ~ nsP + I(st^2) + distPr + I(distPr^2)) Now I tried to predict the values for calP from this model using the following function: xv<-seq(0,89,by=1) yv<-predict(model9,list(distPr=xv,st=xv,nsP=xv)) The predicted values are however strange. Now I do not know weather just the model does not fit

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 all, I have 100 files which are used as input.and I have to input the name of my files again and again.the name of the files are 1.out, 2.out......100.out. I want to know if there is anything like perl so that i can use something like this- for($f = 1; $f <= 100; $f++) { $file = $f.".out"; I have tried this thing in R but it does not work.Can somebody please help me.

i am trying to fit a linear model with both continuous covariates and factors. When fitted with the intercept term the first level of the factor is treated by R as intercept and the estimate of the effects of remaining levels(say i th level) are given as true estimate of i th level - estimate of 1st level.can any please help me? thanks in advance..... -- View this message in context:

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, ...) {

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, This is an embarrassing question, but I can seem to make this work…How do I change the font size on the xlab and on the numbers shown in the x-axis on the time series plot below. The arguments cex.lab and cex.axis do not seem to be 'passing' to the plot function. plot(ts(rnorm(100), start=2004, freq=12), ylab="RQI", xlab="My X lab",

Some help with how to re-label the vertical axis in a histogram would be appreciated. qplot(off.sc,weight=rel.freq,binwidth=.29,main="test Figure"+ylab("New from inside"))+ylab("New from outside")+ xlab("off.sc\nAggregated frequency plots for 17 equal intervals.") The code

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]]

Hi helpeRs, I have inherited a set of data files that use the file system as a sort of poor man's database, i.e., the data files are nested in directories that indicate which city they come from. For example: dir.create("deleteme") for(i in paste("deleteme", c("New York", "Los Angeles"), sep="/")) { dir.create(i) for(j in