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Hi, im little bit confused about Cohen's Kappa and i should be look into the Kappa function code. Is the easy formula really wrong? kappa=agreement-chance/(1-chance) many thanks christian ############################################################################### true-negativ:7445 false-positive:3410 false-negativ:347 true-positiv:772 classification-aggrement:68,6%

Hello! I?m fitting a model with glm(family binomial). The best model counts 9 Variables and includes an interaction term that was generated by the product of to continuous variables (a*b). All variables are correlated under a value of 0.7 (Spearman rank order) While the estimates of both main effects are negativ, the resulting interaction term is positiv. This change of sign makes it difficult to

Hello! I?m fitting a model with glm(family binomial). The best model counts 9 Variables and includes an interaction term that was generated by the product of to continuous variables (a*b). All variables are correlated under a value of 0.7 (Spearman rank order) While the estimates of both main effects are negativ, the resulting interaction term is positiv. This change of sign makes it difficult to

I have a data frame in wide format. There are six variables that represent two factors in long format 3x2, Valence and Temperature: > head(dpts) File Subj Time Group PainNeg.hot PainNeg.warm SociNeg.hot SociNeg.warm Positiv.hot Positiv.warm Errors 1 WB101_1_1_dp.txt 101 1 MNP 30.700000 13.75000 16.319048 35.166667 30.18333 14.383333 1 2

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 community, I'm performing svm-regression with svm at library e1071. As I wrote in another post: "svm e1071 call - different results", I get different results if I use the svm.default rather than the svm.formula, being better the ones at svm.formula I've debugged both options. While debugging the svm.formula, I've seen that when I reach the call: ret <-

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

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

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

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

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

Dear R Community, I have a dataframe like this dat product1 product2 ... productn 01.1.2008 1 1 1 02.1.2008 1 1 2 . 15.2.2008 2 2 NA . 04.4.2008 2 2 1 05.4.2008 NA 2 NA (date ascending order, 1:n products with status 1, 2 or NA) and want to produce a graphic like

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(