similar to: Questions about 'igraph' package.......

Hi all, Can some one tell me the difference between the following two formulas? 1. epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) 2.epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) [[alternative HTML version deleted]]

Dear Mme/Mr. Hope you are doing well. I am doing some genetic analysis using The R software and I have difficulties to find how I can perform an Interaction/epistasis analysis using 3 or more SNPs (=markers) ? (In the instructive manual, there is only an interaction/epistasis analysis with 2 markers). In addition can you please inform me how I can perform Haplotype analysis and if there is an

This is a request to increase MAX_NUM_DLLS in Rdynload.c in from 100 to 500. On line 131 of Rdynload.c, changing #define MAX_NUM_DLLS 100 to #define MAX_NUM_DLLS 500 In development of the mlr package, there have been several episodes in the past where we have had to break up unit tests because of the "maximum number of DLLs reached" error. This error has been an inconvenience that

Thanks Henrik this is very helpful! I will try this out on our tests and see if gcDLLs() has a positive effect. mlr currently has tests broken down by learner type such as classification, regression, forecasting, clustering, etc.. There are 83 classifiers alone so even when loading and unloading across learner types we can still hit the MAX_NUM_DLLS error, meaning we'll have to break them

Dear Developers, I am a data science student. I wish to implement Neuro fuzzy Classifier, Adaboost, MLkNN Multi label algorithms for web document classification. I could not find packages and steps for above mention algorithms in mlr <https://mlr-org.github.io/mlr-tutorial/devel/html/multilabel/index.html>. Hence i request you to give your valuable suggestions. In need of your guidance,

I apologize in advance for posting a question not related to R. I need references to papers that use multiple linear regression in an industrial application and also in a biological experiment. This is aimed to biologists/chemists (non-statisticans) so if anyone has written a brilliant paper where the use of multiple regression is important and also understandable to people with little

Hi, I had a piece of code which looped over a decimal vector like this: for( i in where ){ thisdata <- subset(herde, herde$mlr >= i) # do stuff with thisdata.. } 'where' is a vector like seq(-1, 1, by=0.1) My problem was: 'nrow(thisdata)' in loop repetition 0.4 was different if 'where' was seq(-1, 1, by=0.1) than when 'where' was seq(-0.8, 1,

On 20 December 2016 at 17:40, Martin Maechler wrote: | >>>>> Steve Bronder <sbronder at stevebronder.com> | >>>>> on Tue, 20 Dec 2016 01:34:31 -0500 writes: | | > Thanks Henrik this is very helpful! I will try this out on our tests and | > see if gcDLLs() has a positive effect. | | > mlr currently has tests broken down by learner type

Multilingual Rails v0.6 is released! Here is the changelog. Documentation and download at the homepage: http://www.tuxsoft.se/oss/rails/multilingual v0.6 - 2005-08-13 * String case-manipulation functions replaced with ruby-unicode equivalents (if ruby-unicode is installed): String#downcase, String#upcase and String#capitalize now fully handle Unicode. * String normalization

Hi R Users, I am going to run a multiple linear regression with around 57 independent variables. Each time I run the model with just 11 variables, the results are reasonable. With increasing the number of independent variables more than 11, the coefficients will get ?NA? in the output. Is there any limitation for the number of independent variables in multiple linear regressions in R? I attached

Hi all, I have a dataset with a response variable with three categories (1, 2, 3) and a lot of continuous variables. I'd like to make a MLR with these variables. I've been watching the libraries nnet and zelig for this purpose but I don't understand them well. I use a training sample data to make the MLR. train.set <- sample(1:1000,1000*0.7) I have done this: library(nnet) net

Hi, I am running OS X Snow Leopard on the 'sender' side and Linux (2.6.32, possibly fedora but unsure) on the 'receiver' side. Both with rsync 3.0.9. If I run the following command from the command line: /usr/local/bin/rsync -av -e "ssh -i \ /Users/pedrofortunyayuso/.ssh/backup_pfortuny_net" \ --filter ': rsync.dirs' ~/ pfortuny at

Hi, Dirk: On 12/20/2016 10:56 AM, Dirk Eddelbuettel wrote: > On 20 December 2016 at 17:40, Martin Maechler wrote: > | >>>>> Steve Bronder <sbronder at stevebronder.com> > | >>>>> on Tue, 20 Dec 2016 01:34:31 -0500 writes: > | > | > Thanks Henrik this is very helpful! I will try this out on our tests and > | > see if gcDLLs()

Estimadísimo Carlos: Muchísimas gracias por responderme y hacerlo tan rápido. Contemplé esa posibilidad, es decir, que el hiperparámetro estuviera suponiendo un problema, y probé de esta forma: > learner <- lrn("classif.ranger", num.trees = 5, mtry = NULL) Error: Element with key 'classif.ranger' not found in DictionaryLearner!

Hi there, I have been using the example provided bellow for a while, and It was working without any problem. Nevertheless, just since 2-3 days is not working, probably because I did update.packages(). I have tried to re-install the older versions of the packages Hmisc() and xtable(), but still does not work. Can you run this example, and tell me if you got the same problems? I use

>>>>> Steve Bronder <sbronder at stevebronder.com> >>>>> on Tue, 20 Dec 2016 01:34:31 -0500 writes: > Thanks Henrik this is very helpful! I will try this out on our tests and > see if gcDLLs() has a positive effect. > mlr currently has tests broken down by learner type such as classification, > regression, forecasting,

Estimados amigos: Esta tarde he estado probando la librería mlr3, que me resulta muy interesante para trabajos de clasificación. En concreto, para entender su funcionamiento he probado un ejemplo simple (viene en CRAN). Sin embargo, cuando cambio el parámetro de clasifiación en la función de aprendizaje, me aparece el error siguiente: *Error: Element with key

Hello, I'm with a conceptual doubt regarding Rsquared of both lm() and postResample(library caret). I've got a multiple regression linear model (lets say mlr) with anR² value of 67.52%. Then I use this model pro make predictions with predict() function using the same data as input , that is, use the generated model to predict the value associated with data that I used as input. Next, if

Dear R- and Omega-list-members, I am trying to make statistical inference about the following design: A dependent variable y has been measured multiple times, i.e. 4 times (y1,y2, y3, y4), unfortunately suffering from some successive dropouts (i.e. the sample sizes varies for y1, y2, y3, and y4). For every y, two other variables (covariates) were also measured: x & z, and both do presumably

On reason for hitting the MAX_NUM_DLLS (= 100) limit is because some packages don't unload their DLLs when they being unloaded themselves. In other words, there may be left-over DLLs just sitting there doing nothing but occupying space. You can remove these, using: R.utils::gcDLLs() Maybe that will help you get through your tests (as long as you're unloading packages). gcDLLs() will