search for: confusionmatrix

...ethod. I know that there is an option (?cross?) for cross validation but still I wanted to make a function to Generate cross-validation indices using pls: cvsegments method. ##################################################################### Code (at the end) Is working fine but sometime caret:confusionMatrix gives following error: stat_result<- confusionMatrix(pred_true1,species_test) Error in confusionMatrix.default(pred_true1, species_test) : The data and reference factors must have the same number of levels My data: total number=260 Class = 6 ##################################### Sorry if...

I've been updating a package and, when installing a local devel version, I get an error "object 'confusionMatrix' not found whilst loading namespace". Looking around online, it appears that this might be related to loading a specific RData file, but it doesn't seem to be the case AFAICT. I've installed the devel version in the last week without issues and the confusionMatrix code has been to...

Hello, As obtained from a table svyglm clasificaion, sensitivity and specificity. The funtion ConfusionMatrix () of the library (caret) gives these results but not how to apply it to svyglm. thanks [[alternative HTML version deleted]]

...ech[-train, ] E. Use the naiveBayes function in the e1071 package to fit the model. > model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain) > prob <- predict(model, newdata = InvestTechVal, type = ?raw?) > pred <- ifelse(prob[, 2] >= 0.3, 1, 0) F. Use the confusionMatrix function in the caret package to output the confusion matrix. > confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode = ?everything?, positive = ?1?) > accuracy <- confMtr$overall[1] > valError <- 1 ? accuracy > confMtr G. Classify the 18 new (out-of-sample) readers...

...ckage now contains a function (splsda) that extends the spls function to classification (in the same manner than caret's plsda function extends plsr). Also, fixed a bug where the MSE (instead of RMSE) was reported for random forest OOB resampling There are more examples in ?train. Changes to confusionMatrix, sensitivity, specificity and the predicative value functions: - each was made more generic with default and table methods - confusionMatrix "extractor" functions for matrices and tables were added - the pos/neg predicted value computations were changed to incorporate prevalence - pr...

...ckage now contains a function (splsda) that extends the spls function to classification (in the same manner than caret's plsda function extends plsr). Also, fixed a bug where the MSE (instead of RMSE) was reported for random forest OOB resampling There are more examples in ?train. Changes to confusionMatrix, sensitivity, specificity and the predicative value functions: - each was made more generic with default and table methods - confusionMatrix "extractor" functions for matrices and tables were added - the pos/neg predicted value computations were changed to incorporate prevalence - pr...

...yes function in the e1071 package to fit the model. > > > model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain) > > prob <- predict(model, newdata = InvestTechVal, type = ?raw?) > > pred <- ifelse(prob[, 2] >= 0.3, 1, 0) > > F. Use the confusionMatrix function in the caret package to output the > confusion matrix. > > > confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode = > ?everything?, positive = ?1?) > > accuracy <- confMtr$overall[1] > > valError <- 1 ? accuracy > > confMtr > > G....

...unction in the e1071 package to fit the model. > >> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain) >> prob <- predict(model, newdata = InvestTechVal, type = ?raw?) >> pred <- ifelse(prob[, 2] >= 0.3, 1, 0) > > F. Use the confusionMatrix function in the caret package to output the > confusion matrix. > >> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode = > ?everything?, positive = ?1?) >> accuracy <- confMtr$overall[1] >> valError <- 1 ? accuracy >> confMtr > &g...

Hi. Can anyone suggest how to use the output of pcaNNet() to construct a confusion matrix possibly using confusionMatrix()? Thank you. -- View this message in context: http://www.nabble.com/pcaNNet-confusion-tp24422000p24422000.html Sent from the R help mailing list archive at Nabble.com.

...0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 I recently learned, that I can easily create a confusion matrix out of this data using the following commands: pairs<-data.frame(pred=factor(unlist(input[2:21])),ref=factor(input[,22])) pred<-pairs$pred ref <- pairs$ref library (caret) confusionMatrix(pred, ref, positive=1) - where column 21 is the reference/goldstandard. My problem is now, that I analyse data.frames with an unknown count of columns. So to get rid of the first and last column for the "pred" variable and to select the last column for the "ref" variable, I ha...

...ackage to fit the model. > > > >> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain) > >> prob <- predict(model, newdata = InvestTechVal, type = ?raw?) > >> pred <- ifelse(prob[, 2] >= 0.3, 1, 0) > > > > F. Use the confusionMatrix function in the caret package to output the > > confusion matrix. > > > >> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode = > > ?everything?, positive = ?1?) > >> accuracy <- confMtr$overall[1] > >> valError <- 1 ? accuracy >...

...>> > >> >> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain) >> >> prob <- predict(model, newdata = InvestTechVal, type = ?raw?) >> >> pred <- ifelse(prob[, 2] >= 0.3, 1, 0) >> > >> > F. Use the confusionMatrix function in the caret package to output the >> > confusion matrix. >> > >> >> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode = >> > ?everything?, positive = ?1?) >> >> accuracy <- confMtr$overall[1] >> >> valError...

...>>>>> model <- naiveBayes(`Purchase (1=yes, 0=no)` ~ ., data = InvestTechTrain) >>>>> prob <- predict(model, newdata = InvestTechVal, type = ?raw?) >>>>> pred <- ifelse(prob[, 2] >= 0.3, 1, 0) >>>> >>>> F. Use the confusionMatrix function in the caret package to output the >>>> confusion matrix. >>>> >>>>> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode = >>>> ?everything?, positive = ?1?) >>>>> accuracy <- confMtr$overall[1] >>&gt...

Hi R users, I am getting the following error when using the splsda function in R v2.12.1: "Error in switch(classifier, logistic = { : EXPR must be a length 1 vector" What does this mean and how do I fix this? Thank you in advance! Best, Savi

...0=no)` ~ ., data = >>>>>> InvestTechTrain) >>>>>> prob <- predict(model, newdata = InvestTechVal, type = ?raw?) >>>>>> pred <- ifelse(prob[, 2] >= 0.3, 1, 0) >>>>> >>>>> >>>>> F. Use the confusionMatrix function in the caret package to output >>>>> the >>>>> confusion matrix. >>>>> >>>>>> confMtr <- confusionMatrix(pred,unlist(InvestTechVal[, 3]),mode = >>>>> >>>>> ?everything?, positive = ?1?) >>...

Hello, When evaluating different learning methods for a categorization problem with the (really useful!) caret package, I'm getting confusing results from the Kappa computation. The data is about 20,000 rows and a few dozen columns, and the categories are quite asymmetrical, 4.1% in one category and 95.9% in the other. When I train a ctree model as: model <- train(dat.dts,

*Hi, *Does anyone know how can I show an *ROC curve for R-SVM*? I understand in R-SVM we are not optimizing over SVM cost parameter. Any example ROC for R-SVM code or guidance can be really useful. Thanks, Angel. [[alternative HTML version deleted]]

Dear all, I have a couple of short noob questions for whoever can take them. I'm from a very non-stats background so sorry for offending anybody with stupid questions ! :-) I have been using logistic regression care of glm to analyse a binary dependent variable against a couple of independent variables. All has gone well so far. In my work I have to compare the accuracy of analysis to a C4.5