similar to: Random Forests in R

Displaying 20 results from an estimated 50000 matches similar to: "Random Forests in R"

2002 Apr 02
2
random forests for R
Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the
2002 Apr 02
2
random forests for R
Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the
2011 Oct 27
1
Question about .Fortran in glmnet package
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",
2017 Sep 21
0
Add wrapper to Shiny in R package
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 &
2017 Sep 21
1
Add wrapper to Shiny in R package
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 =
2017 Sep 21
3
Add wrapper to Shiny in R package
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, ...) {
2010 Feb 21
4
R on 64-Bit…
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
2017 Sep 17
2
Shiny App inside R Package
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",
2010 Apr 09
1
Question on implementing Random Forests scoring
So I've been working with Random Forests ( R library is randomForest) and I curious if Random Forests could be applied to classifying on a real time basis. For instance lets say I've scored fraud from a group of transactions. If I want to score any new incoming transactions for fraud could Random Forests be used in that context. Linear Regression is nice in that it is very easy to
2009 Apr 20
1
Random Forests: Predictor importance for Regression Trees
Hello! I think I am relatively clear on how predictor importance (the first one) is calculated by Random Forests for a Classification tree: Importance of predictor P1 when the response variable is categorical: 1. For out-of-bag (oob) cases, randomly permute their values on predictor P1 and then put them down the tree 2. For a given tree, subtract the number of votes for the correct class in the
2007 Dec 18
1
Random forests
Dear all, I would like to use a tree regression method to analyze my dataset. I am interested in the fact that random forests creates in-bag and out-of-bag datasets, but I also need an estimate of support for each split. That seems hard to do in random forests since each tree is grown using a subset of the predictor variables. I was thinking of setting mtry = number of predictor variables,
2016 Apr 01
3
TensorFlow in R
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]]
2016 Apr 01
3
TensorFlow in R
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]]
2014 Jan 18
6
My first package
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
2011 Feb 26
2
Reproducibility issue in gbm (32 vs 64 bit)
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
2013 Feb 10
3
Constrained Optimization in R (alabama)
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 <-
2010 Oct 22
2
Random Forest AUC
Guys, I used Random Forest with a couple of data sets I had to predict for binary response. In all the cases, the AUC of the training set is coming to be 1. Is this always the case with random forests? Can someone please clarify this? I have given a simple example, first using logistic regression and then using random forests to explain the problem. AUC of the random forest is coming out to be
2004 Mar 02
1
some question regarding random forest
Hi, I had two questions regarding random forests for regression. 1) I have read the original paper by Breiman as well as a paper dicussing an application of random forests and it appears that the one of the nice features of this technique is good predictive ability. However I have some data with which I have generated a linear model using lm(). I can get an RMS error of 0.43 and an R^2 of
2011 Nov 30
1
R Interface to C / C++‏
Dear List, I’d like to modify the R rpart package source code to add a new split criterion. I’m familiar with R, but not at all with C or C++. I understand C and C++ are quite different, and I don’t have the time to learn both, so my questions are (i) which one should I learn for the specific task mentioned above? (I understand rpart routines are written in C, but want to check this), (ii) more
2017 Sep 08
2
quote()/eval() question
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 =