similar to: svm.formula versus svm.default - different results

Full_Name: Noel O'Boyle Version: 2.1.0 OS: Debian GNU/Linux Sarge Submission from: (NULL) (131.111.8.96) (1) Description of error The 10-fold CV option for the svm function in e1071 appears to give incorrect results for the rmse. The example code in (3) uses the example regression data in the svm documentation. The rmse for internal prediction is 0.24. It is expected the 10-fold CV rmse

1. This is _not_ a bug in R itself. Please don't use R's bug reporting system for contributed packages. 2. This is _not_ a bug in svm() in `e1071'. I believe you forgot to take sqrt. 3. You really should use the `tot.MSE' component rather than the mean of the `MSE' component, but this is only a very small difference. So, instead of spread[i] <- mean(mysvm$MSE), you

Hi everyone, Can someone please help me in these questions?: 1)if I use crossvalidation with svm, do I have to use this equation to calculate RMSE?: mymodel <- svm(myformula,data=mydata,cross=10) sqrt(mean(mymodel$MSE)) But if I don’t use crossvalidation, I have to use the following to calculate RMSE: mymodel <- svm(myformula,data=mydata) mytest

Hi, I am trying to create 3 mds plots: one with axis 1 vs axis 2, one with axis 2 vs axis 3, and one with axis 1 vs axis 3. When inputting my code, I only end up with one diagram and when inputting mdsg.mds$dims, the program returns 2 for 2 dimensions. How can I create the other two plots? Any help would be greatly appreciated, Calla Carbone The table I use is number of each taxa on each

Dear Researches, I am using RF (in regression way) for analize several metrics extract from image. I am tuning RF setting a loop using different range of mtry, tree and nodesize using the lower value of MSE-OOB mtry from 1 to 5 nodesize from1 to 10 tree from 1 to 500 using this paper as refery Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2007). Random Forest Models

Hello R-gurus, I'm trying to adjust different growth curves to a rather extensive dataset. I wrote up a function to go through all of them, but am encountering a problem : among the more than 1000 curves I have, obviously for some of them I encounter conversion problems. I'd like for my function to keep going to the next curve and store the fact that for curve number X I had a convergence

I have two directories https://echange-fichiers.inra.fr/get?k=AcHKdNI4No44GEsj7PK with 12 (global maps)binary files in each.I used the code given below to calculate the spatial correlation between these files and it worked well(the output is a global correlation map). I wonder if there is a simple way to calculate RMSE and bias along with cor.so finally we get three outputs (bias map,RMSE map,cor

Dear Researches, I am using RF (in regression way) for analize several metrics extract from image. I am tuning RF setting a loop using different range of mtry, tree and nodesize using the lower value of MSE-OOB mtry from 1 to 5 nodesize from1 to 10 tree from 1 to 500 using this paper as refery Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2007). Random Forest Models

Dear all, I was hoping someone could help with a ggplot question. I would like to generate a faceted bar chart, but missing data are causing problems. g<-structure(list(Date = structure(c(11322, 11687, 12052, 11322, 11687, 12052, 11322, 11687, 12052, 11322, 11687, 12052), class = "Date"), variable = c("Govt Revenues to GDP", "Govt Revenues to GDP",

I'm trying to use a self-written function with the summaryBy function (doBy package). I have lots of data from Monte Carlo experiments comparing different estimators across different (combinations of) parameter values, similar to the following form: colnames(mydata) <- c("X", "b0", "b1", # parameter combination, corresponding (true) parameter values

Need Help in Debugging below script:-------------------------------- dat <- get_majorlandmarks(dat,Dmin,Per) fit_xts <- xts(dat$fit,order.by = dat$Date,frequency = 365) close_xts <- xts(dat$Close, order.by = dat$Date, frequency = 365 ) majorlandmarks_xts <-xts(dat$Close[dat$majorlandmarks==TRUE], order.by = dat$Date[dat$majorlandmarks==TRUE], frequency = 365 ) minorlandmarks_xts

Hi, I am doing predictive modelling of Multivariate Time series Data of a Motor in R using various models such as Arima, H2O.Randomforest, glmnet, lm and few other models. I created a function to select a model of our choice and do prediction. Model1 <- function(){ .. return() } Model2 <- function(){ ... return() } Model3 <- function(){ ... return() } main <-

I'm a week-old R user, and have become stuck trying to create usable CSV outputs for post-processing. I am using the package Rioja, which provides small datasets of results. I am running several analyses in a loop and iteratively adding the results to a *list* ("combined"). Within each iteration I use the following: > combined[[i]] <- performance(fit) With two iterations I

Hi R users This is not related to R but general stats terms. I f any one help me to figure out how they differ and when I need to use which one, i will appreciate it. I read several simulation studies and found that RMSE (Root Mean Square Error) and RMSD (Root Mean Square Difference) appered to be used interchangeablely. The formula for calculating for both RMSE and RMSD are, to me, the

I own a lot to the folks on r-help list, especially arun who answered every of my question and was never wrong. I am disinclined to once again ask this question, since it is more arithmatic than technical. But, having worked 2 days on it, I realized my brain is just not juicy enough.... Here is the problem. Trust not for freedom to the Franks--- They have a king who buys and sells. -

I am new to R, can anyone help with boxplot for a dataset like: file1 col1 col2 col3 col4 col5 050350005 101 56.625 48.318 RED 051010002 106 50.625 46.990 GREEN 051190007 25 65.875 74.545 BLUE 051191002 246 52.875 57.070 RED 220050004 55 70 80.274 BLUE 220150008 75 67.750 62.749 RED 220170001 77 65.750 54.307 GREEN file2 col1 col2 col3 col4 col5 050350005 101 56.625 57 RED 051010002 106 50.625 77

Dear R-experts, Doing cross-validation for 2 robust regressions (HBR and fast Tau). I can't get the 2 errors rates (RMSE and MAPE). The problem is to predict the response on the testing data. I get 2 error messages. Here below the reproducible (fictional example) R code. #install.packages("MLmetrics") # install.packages( "robustbase" ) # install.packages(

Hola!! Estoy intentando evaluar mi modelo de series temporales (uso auto.arima). Para ello he implemetado el método "rolling window" que se basa en ir añadiendo progresivamente datos al conjunto de train para testar el modelo. Por ejemplo: - Train: 1 año, test: día 1 (24 observaciones, una por hora) --> evalúo ese día (RMSE por ejemplo) - Train: 1 año + 1 día, test: día 2 -->

I ran into a problem with titles on graphs. I wanted a graph with multiple subplots, with each having a title that involved both a Greek letter and an identifier for each graph. Below is a simplified version of code to do this. The graph appears fine, with the first graph having "i=1" in the title, and the second graph having "i=2" in the title. However, when I resize the

I would like the text plotted with 'mtext' to be alighned like it is for printing on the console. Here is what I have: > print(emt) ME RMSE MAE MPE MAPE MASE original -1.034568e+07 1.097695e+08 2.433160e+07 -31.30554 37.47713 1.5100050 xreg 1.561235e+01 2.008599e+03 9.089473e+02 267.05490 280.66734