search for: resamping

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?

Forgot to send to R-help From: Nordlund, Dan (DSHS/RDA) Sent: Thursday, February 07, 2013 2:09 PM To: 'James Jong' Subject: RE: [R] Sourcing my file does not print command outputs James, Your code seems to have ‘…’ sitting on a line all by itself (maybe should be at the end of the preceding comment? Anyway, when I eliminated that problem and sourced the script using the following call

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,

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),

...ent in future data sets for prediction. This may be a problem when the formula interface to models is used, but it looks like many functions reference columns by position and not name. (13) Packages that have their own cross-validation functions should allow the users to pass in the specific folds/resamping indicators to maintain consistency across similar functions in other packages. (14) [OCD] For binary classification models, model the probability of the first level of a factor as the event of interest (again, for consistency) Note that glm() does not do this but most others use the first level....

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;

...dict.lm(object, x) : prediction from a rank-deficient fit may be misleading When I remove the dummy variables the function runs fine. #remove State variable Desc <- chlDescr[,-c(1)] lmProfile <- rfe(Desc, y, sizes = subsets, rfeControl = ctrl) Returns: Recursive feature selection Outer resamping method was 10 iterations of cross-validation. Resampling performance over subset size: Variables RMSE Rsquared RMSESD RsquaredSD Selected 1 0.2462 0.7454 0.09529 0.17362 2 0.2408 0.7680 0.07860 0.12543 3 0.2134 0.8285 0.06649 0.0904...

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