similar to: gbm function

Buenas días me gustaria saber cual es la libreria mas actual que contiene al algoritmo de maxima entropia que permite modelar nichos ecologicos; a su vez que libreria presenta mejor temática en la representacion de mapas. Saludos¡ [[alternative HTML version deleted]]

I applied bootpred for multinomial logistic reg. (with nnet package). I used same as theta.fit and theta.predict of R for my data. but give me error. Can I do this with response vriable;7 levels predictor variables:5 (1 classifier, 4 continuous)?   Thanks alot Azam   [[alternative HTML version deleted]]

Hi   I want to do leave-one-out cross-validation for multinomial logistic regression in R. I did multinomial logistic reg. by package nnet in R. How I do validation? by which function? response variable has 7 levels   please help me   Thanks alot Azam [[alternative HTML version deleted]]

Good afternoon Has anyone tried using Dr. Elith's BRT script? I cannot seem to run gbm.step from the installed gbm package. Is it something external to gbm? When I run the script itself <- gbm.step(data=model.data, gbm.x = colx:coly, gbm.y = colz, family = "bernoulli", tree.complexity = 5, learning.rate = 0.01, bag.fraction = 0.5) ... I

Dear All   Pleas help me to increase the memory in R.   In order increase memory, I read the FAQ and follow the instruction as below     Close R, then right-click on your R program icon (the icon on your desktop or in your programs directory). Select ``Properties'', and then select the ``Shortcut'' tab. Look for the ``Target'' field and after the closing quotes around the

Hi   I did validation for prediction by logistic regression according to following:   validationsize <- 23 set.seed(1) random<-runif(123) order(random) nrprofilesinsample<-sort(order(random)[1:100]) profilesample <- data[nrprofilesinsample,] profilevalidation <- data[-nrprofilesinsample,] salich<-profilesample$SALIC.H.1 salic.lr<-glm(salich~wetnessindex, profilesample,

Hi   I want to draw a plot from observed and predicted data and also shows threshold and data before threshold are identified with different color from data after threshold.   Suppose: abserved data are 0 or 1 predicted data= 0 to 1 threshold=0.5   Thanks alot     [[alternative HTML version deleted]]

Dear All   I want to import the vector file (   .shp) to R. I could import the file by rgdal package before, by following:   geology<-readOGR('C:/geology//saga/geo.geom','finalgeology')   but now there is an error:   Error in ogrInfo(dsn = dsn, layer = layer, input_field_name_encoding = input_field_name_encoding) :           GDAL Error 4: .shx file is unreadable, or corrupt.

Hello all   I have a geology map that has three level, bellow   <-geology lithology    landscape   landform     landform level is used as covariate (with codes=1,2,3,4,5) for training of neural network, but this level has missing data as NA. I want to replace the missing data of landform level with 0 (zero). Finally, landform will have codes  0,1,2,3,4,5.   please help me   Thanks alot.

Dear gbm users, When running predict.gbm() on a "large" dataset (150,000 rows, 300 columns, 500 trees), I notice that the memory used by R grows beyond reasonable limits. My 14GB of RAM are often not sufficient. I am interpreting this as a memory leak since there should be no reason to expand memory needs once the data are loaded and passed to predict.gbm() ? Running R version 2.9.2 on

Dear all, the help file for predict.gbm states that "The predictions from gbm do not include the offset term. The user may add the value of the offset to the predicted value if desired." I am just not sure how exactly, especially for a Poisson model, where I believe the offset is multiplicative ? For example: library(MASS) fit1 <- glm(Claims ~ District + Group + Age +

Hello, In the attached file training.csv (I apologize for the large file) I have 238 objects belonging to 13 classes, which are described by 183 properties. I would like to find a svm model for these objects. I tried the following R statements. library('e1071') datatraining <- read.csv("training.csv",head=TRUE) names<-names(datatraining) print("before

Hi, there: I am wondering if I can find some detailed explanation on gbm or explanation on examples of gbm. thanks, Ed

HI, Dear Greg, I AM A NEW to GBM package. Can boosting decision tree be implemented in 'gbm' package? Or 'gbm' can only be used for regression? IF can, DO I need to combine the rpart and gbm command? Thanks so much! -- Sincerely, Changbin -- [[alternative HTML version deleted]]

Hi, there: I am always experiencing the scalability of some R packages. This time, I am trying gbm to do adaboosting on my project. Initially I tried to grow trees by using rpart on a dataset with 200 variables and 30,000 observations. Now, I am thinking if I can apply adaboosting on it. I am wondering if here is anyone who did a similar thing before and can provide some sample codes. Also any

I recently use gbm for a binary classification problem. As expected, it gets very good results, based on Area under ROC with 7-fold cross validation. However, the application (malware detection) is cost-sensitive, getting a FP (classify a clean sample as a dirty one) is much worse than getting a FN (miss a dirty sample). I would like to tune the gbm model biased to very low FP rate. For this

Dear All, I am far from being a guru about parallel programming. Most of the time, I rely or randomForest for data mining large datasets. I would like to give a try also to the gradient boosted methods in GBM, but I have a need for parallelization. I normally rely on gbm.fit for speed reasons, and I usually call it this way gbm_model <- gbm.fit(trainRF,prices_train, offset = NULL, misc =

Hi, I have a geforce 4mx 440 agp 8x, and I'm trying to use the GBM library, (as jbarnes in: http://virtuousgeek.org/blog/index.php/jbarnes/2011/10/ and David Hermann in KMSCON: https://github.com/dvdhrm/kmscon), without success. when I try to create a gbm_device, I get: (below the code.) nouveau_drm_screen_create: unknown chipset nv18 dri_init_screen_helper: failed to create pipe_screen

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

Hi R User, I was trying to find a final model in the following example by using the Boosted regression trees (GBM). The program gives the fitted values but I wanted to calculate the fitted value by hand to understand in depth. Would you give moe some hints on what is the final model for this example? Thanks KG ------- The following script I used #----------------------- library(dismo)