search for: resamp

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 ve...

...rnorm(2000))) #Construct Y using X Y <- runif(1)*X[,1]*X[,2]^2+runif(1)*X[,3]/X[,4] #Convert Y to binary Y <- as.factor(ifelse(sign(Y)>0,'X1','X0')) #Create bootstrap samples for fitting models library(caret) print("Creating bootstrap samples") tmp <- createResample(Y,times = 25) myCtrl <- trainControl(method = "boot", index = tmp, timingSamps = 10) RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1) NNmodel <- train(X,Y,method='nnet',trControl=myCtrl,tuneLength=3, trace = FALSE) ## GLMnet = G...

...MNNUM) new.DF } #this function does one model I am interested in. modeltree<-function(DF){ MLM.plot <- multinom(MN_fact ~ Canpy + mean_dbh + num_beechoak + num_class5 + prop_hard , data=hfdata, trace=FALSE) MLM.plot } # this replicates the 'rand' function and applies a model resamp.funct<-function(DF,funct, n){ list<-replicate(n,rand(DF), simplify = FALSE) sapply(list, funct, simplify = FALSE) } #So if I paste below: l<-resamp.funct(hfdata, modeltree, 3) # I get > l<-resamp.funct(hfdata, modltree, 3) > l [[1]] Call: multinom(formula = MN_fact...

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]]

...=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 advance. Hanna Linear mixed model fit by REML Formula: values ~ (1 | lot) Data: resamp AIC BIC logLik deviance REMLdev -14.21 -9.459 10.1 -23.88 -20.21 Random effects: Groups Name Variance Std.Dev. lot (Intercept) 0.036077 0.18994 Residual 0.017278 0.13144 Number of obs: 36, groups: lot, 10 Fixed effects: Estimate Std. Error t val...

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), ylab="Probability", xlab="Concentration...

...reasons to not use formulas, such as functions that do not use a design matrix (eg. cforest()) or need factors to be handled in a non-standard way (eg. cubist()). (6) Don't require a test set when model building. (7) Control all written output during model-building time with a verbose option. Resampling can make a mess out of things if output/logging is always exposed. (8) Please use RSiteSearch to avoid name collisions between packages (eg. gam(), splsda(), roc(), LogitBoost()). Also search Bioconductor. (9) Allow the predict function to generate results from many different sub-models simul...

...x,2048,1,kaiser(2048,14)); spectrogram_floor=-96; pcolor(max(spectrogram_floor,20*log10(abs(X(end:-1:1,:))))); shading flat colorbar print('-dpng','-r300', [fn, '.png']) end ['Done'] I've posted the output for the filter-based and block-based resampler at different qualities at http://www.mumble.info/resamp/ The resampling was 96khz => 44.1khz. "chirp.png" is the original (unresampled) "perfect.png" is the result of a FFT/chop/iFFT of the whole file as one. "filter0.png" to "filter10.png" is the &q...

...my 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)] subsets <- c(2:8) ctrl<- rfeControl(functions = lmFuncs, method="cv", verbose=FALSE, returnResamp = "final") lmProfile <- rfe(w, y, sizes = subsets, rfeControl = ctrl) Returns: Error in `[.data.frame`(x, , retained, drop = FALSE) : undefined columns selected In addition: Warning message: In predict.lm(object, x) : prediction from a rank-deficient fit may be misleading When...

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