search for: resamps

I looked at the documentation of source() and summary(), and I could not find the reason why calling something like: > summary(resamps) from the command line, works (it prints the summary) whereas calling summary(resampls) from a file that I source with source("my_file.r") does not print anything. How can I get summary(resamps) to print when I source a file with this command? Thanks, James [[alternative HTML ver...

...;- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1) NNmodel <- train(X,Y,method='nnet',trControl=myCtrl,tuneLength=3, trace = FALSE) ## GLMnet = GLMmodel, ... #Assess re-sampled (out of sample) accuracy print("Assessing re-sampled (OOB) accuracy") resamps <- resamples( list( RF = RFmodel, NN = NNmodel )) print("Summarizing resamps") summary(resamps) dotplot(resamps, metric = "Accuracy") ======================================== Thanks, James On Thu, Feb 7, 2013 at 4:37 PM, Nordlund,...

Hello List and thanks in advance for all of your help, I am trying implement a permutation test of a multinomial logistic regression ('multinom' within the nnet package). In the end I want to compare the parameter estimate from my data to the distribution of randomized parameter estimates. I have figured out how to permute my dependent variable (MNNUM) x number of times, apply

Say I train a model in caret, e.g.: RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1) How can I save this to disk and load it later in R? How about an object of the class "resamples"? resamps <- resamples( list( RF = RFmodel, SVM = SVMmodel, KNN = KNNmodel, NN = NNmodel )) Thanks, James [[alternative HTML version deleted]]

Dear all, I want to fit a random effect model with only one random factor. I do not want to include the intercept term either. The model I using now is lmer(values ~ (1|lot), data=tmp) The results are as below. How do I take out the intercept term? Or if this is not possible for the lmer function, is it possible using lme function in the "nlme" package? Thank you very much in

Dear R gurus, I have the following code, but I still not know how to estimate and extract confidence intervals (95%CI) from resampling. Thanks! ~Adriana #data penta<-c(770,729,640,486,450,410,400,340,306,283,278,260,253,242,240,229,201,198,190,186,180,170,168,151,150,148,147,125,117,110,107,104,85,83,80,74,70,66,54,46,45,43,40,38,10) x<-log(penta+1) plot(ecdf(x),

Working on the caret package has exposed me to the wide variety of approaches that different authors have taken to creating predictive modeling functions (aka machine learning)(aka pattern recognition). I suspect that many package authors are neophyte R users and are stumbling through the process of writing their first R package (or R code). As such, they may not have been exposed to some of the

Using the following MATLAB snippet: fnames={'chirp','perfect','block','filter'} for k=1:length(fnames) fn=fnames{k}; myfile=fopen([fn, '.fl'], 'r', 'ieee-le'); x=fread(myfile, Inf, 'float32', 0, 'ieee-le'); fclose(myfile); X=specgram(x,2048,1,kaiser(2048,14)); spectrogram_floor=-96;

I'm trying to run "rfe" for variable selection in the caret package, and am getting an error. My data frame includes a dummy variable with 3 levels. x <- chlDescr y <- chl #crate dummy variable levels(x$State) <- c("AL","GA","FL") dummy <- model.matrix(~State,x) z <- cbind(dummy, x) #remove State category variable w <- z[,c(-4)]

Hi, I have 35 data files for reading. I would like get a program for performing reading of 35 files at once. All are of the type: Dados1.raw, Dados2.raw and so on. If the files have the same number of columns, I can read with the following commands: rm(list=ls()) filenames = list.files(path="~/Silvano/Arq", pattern="Dados+.*raw") names = substr(filenames, 1, 7) for(i in