similar to: RandomForest vs. bayes & svm classification performance

Dear all, I am trying to train a randomForest using all my control data (12,000 cases, ~ 20 explanatory variables, 2 classes). Because of memory constraints, I have split my data into 7 subsets and trained a randomForest for each, hoping that using combine() afterwards would solve the memory issue. Unfortunately, combine() still runs out of memory. Is there anything else I can do? (I am not using

You need to give us more details, like how you call randomForest, versions of the package and R itself, etc. Also, see if this helps you: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/32918.html Andy From: Eleni Rapsomaniki > > Dear all, > > I am trying to train a randomForest using all my control data > (12,000 cases, ~ 20 explanatory variables, 2 classes). > Because

Dear R users, I have started work in a Statistics government department and I am trying to convince my bosses to install R on our computers (I can't do proper stats in Excel!!). They asked me to prove that this is a widely used software (and not just another free-source, bug infected toy I found on the web!) by suggesting other big organisations that use it. Are you aware of any reputable

Dear R-helpers, I have a huge data-set so need to avoid for loops as much as possible. Can someone think how I can compute the result in the following example (that uses a for-loop) using some version of apply instead (or any other similarly super-efficient function)? example: #Suppose a matrix: m1=cbind(1:5,1:5,1:5) #The aim is to create a new matrix with every column containing the

Dear mailing list, For two normal distributions, e.g: r1 =rnorm(20,5.2,2.1) r2 =rnorm(20,4.2,1.1) plot(density(r2), col="blue") lines(density(r1), col="red") Is there a way in R to compute/estimate the point(s) x where the density of the two distributions cross (ie where x has equal probability of belonging to either of the two distributions)? Many Thanks Eleni

Dear R users, The package 'mfp' that fits fractional polynomial terms to predictors. Example: data(GBSG) f <- mfp(Surv(rfst, cens) ~ fp(age, df = 4, select = 0.05) + fp(prm, df = 4, select = 0.05), family = cox, data = GBSG) print(f) To describe the association between the original predictor, eg. age and risk for different values of age I can plot it the polynomials

Dear R-users Has anybody implemented a function/package that will compute an individual's risk of an event in the presence of competing risks, adjusted for the individual's covariates? The only thing that seems to come close is the cuminc function from cmprsk package, but I would like to adjust for more than one covariate (it allows you to stratify by a single grouping vector). Any

Dear R users, I know one way to see the code for a hidden function, say function_x, is using default.function_x (e.g. summary.default). But how can I see the code for imported packages that have no namespace (in this case Design)? Many Thanks Eleni

Hallo! I would need a code for 10-fold cross validation for the classifiers Naive Bayes and svm (e1071) package. Has there already been done something like that? I tried to do it myself by applying the tune function first: library(e1071) tune.control <- tune.control(random =F, nrepeat=1, repeat.aggregate=min.,sampling=c("cross"),sampling.aggregate=mean, cross=10, best.model=T,

Dear R users, How do I drop multiplication terms from a formula using update? e.g. forml=as.formula("Surv(time, status) ~ x1+x2+A*x3+A*x4+B*x5+strata(sex)") #I would like to drop all instances of variable A (the main effect and its interactions). The following: updated.forml=update(forml, ~ . -A) #gives me this: #Surv(time, status) ~ x1 + x2 + x3 + x4 + B + x5 + strata(sex) + A:x3 +

Hey guys, I can't get my Naive Bayes model to predict. Forgive me if its simple... I've tried about everything and can't get it to work. Reproduceable code below. Thank you, Mike -- Michael Schumacher Manager Data & Analytics - ValueClick mike.schumacher@gmail.com * Functional Example Code from UCLA:

Hi I found on google that there is an implementation of hot-deck imputation in SAS: http://ideas.repec.org/c/boc/bocode/s366901.html Is there anything similar in R? Many Thanks Eleni Rapsomaniki

I am wondering if anybody here have a simple example in R for Naive Bayes. For example, I can do k-means clustering on the "iris" data - data(iris) cl <- kmeans(iris[,1:4], 3) cl$cluster cbind(1:150,iris$Species) =========== But how to do Naive Bayes classification in the same "iris" data? Many thanks! -- View this message in context:

hi there!! i am trying to implement the code of the e1071 package for naive bayes, but it doens't really work, any ideas?? i am very glad about any help!! i need a naive bayes with 10-fold cross validation: code: library(e1071) model <- naiveBayes(code ~ ., mydata) tune.control <- tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = min, sampling = c("cross"),

Good afternoon! I need to evaluate the goodness-of-fit (aka calibration) for survival probability estimates from a Cox model. I tried to use 'calibrate' in the Design package but I'm not sure if it should/would produce what I need (ie a chi-sq type statistic with a table of expected vs observed probabilities). Any other functions I should be aware of? Also, has anybody come across

Dear R-users, While making a prediction using the randomForest function (package randomForest) I'm getting the following error message: "Error in predict.randomForest(model, newdata = CV) : No forest component in the object" Here's my complete code. For reproducing this task, please find my 2 data sets attached ( http://www.nabble.com/file/p17855119/data.rar data.rar ).

Dear R users, I need to come up with an efficient method to compute the correlation (or at least, the euclidean distance if that's easier) between specific rows in a data frame (46,232 rows, 29 columns). The pairs of rows between which I want to find the correlation share a common value in one of the columns. So for example, in the following

Hi I am trying to impute missing values for my data.frame. As I intend to use the complete data for prediction I am currently measuring the success of an imputation method by its resulting classification error in my training data. I have tried several approaches to replace missing values: - mean/median substitution - substitution by a value selected from the observed values of a variable - MLE

I am using the package Design for survival analysis. I want to plot a simple Kaplan-Meier fit of survival vs. age, with age grouped as quantiles. I can do this: survplot(survfit(Surv(time,status) ~ cut(age,3), data=veteran) but I would like to do something like this: survplot(survfit(Surv(time,status) ~ quantile(age,3), data=veteran) #will not work ideally I would like to superimpose

Hi, I am using the function "nomogram" in the rms package for survival analysis. How is the order in which variables determined and how can I change it? I use it with a cph() model. Many Thanks Eleni Rapsomaniki Clinical Epidemiology Group Department of Epidemiology and Public Health University College London