similar to: how to preserve trained model in LDA?

Hi all: A short question: For example, a=[3,4,6,2,3], obviously the 3rd entry of the array has the maxium value, what I want is index of the maxium value: 3. is there a neat expression to get this index? Thank you! Best, Feng [[alternative HTML version deleted]]

Dear Sir I would appreciate recieving the manul instruction of the program foe geochemical calculations.please what are the requirmentsof using the program Thanks Tanat university faculty of science , geology department ,tanta Egypt Prof.Mohamed Fouad Ghoneim Ph.D- D.Sc. Head of Geology Department Faculty of Science Tanta University, Egypt www.profghoneim.tk

I used the following command to train a randomForest model train.rf <- randomForest(grp ~ ., data=tr, ntree=100, mtry=50) My question is how to save the trained model so that it can be loaded later for testing new samples? Thanks, Luk --------------------------------- [[alternative HTML version deleted]]

Hi All: I'm developing an application program using C++. From my C++ code, I would call some R program I have written. I' wondering if R provide some compiler that can compile R program into executable program. I searched R-help, there are a lot of posts talking about writing C++ code in R program, but few about calling R from C++. I might be wrong that R

HI, Dear community, I am using the linear discriminant analysis to build model and make new predictions: > dim(train) #training data [1] 1272 22 > dim(valid) # validation data [1] 140 22 lda.fit <- lda(out ~ ., data=train, na.action="na.omit", CV=TRUE) # model fitting of linear discriminant analysis on training data > predict(lda.fit, valid) # make prediction on

When I import a simple dataset, run LDA, and then try to use the model to forecast out of sample data, I get a forecast for the training set not the out of sample set. Others have posted this question, but I do not see the answers to their posts. Here is some sample data: Date Names v1 v2 v3 c1 1/31/2009 Name1 0.714472361 0.902552278 0.783353694 a 1/31/2009 Name2 0.512158919 0.770451596

I'm trying to classify some observations using lda and I'm getting a strange error. I loaded the MASS package and created a model like so: >train <- mod1[mod1$rand < 1.7,] >classify <- mod1[mod1$rand >= 1.7,] >lda_res <- lda(over_win ~ t1_scrd_a + t1_alwd_a, data=train, CV=TRUE) That works, and all is well until I try to do a prediction for the holdouts:

Dear r-help I am trying to run LDA on a training data set, and test it on another data set with the same variables. I found examples using crossvalidation, and using training and testing data sets set up with sample, but not when they are preassigned. Here is what I tried # FIRST SET UP A DATAFRAME WITH ALL THE DATA AND CREATE NEW VARIABLES traintest1 <-

I didn't get any responses to this question on stats.SE: - http://stats.stackexchange.com/questions/34415/optimization-of-models-ann-radial-basis-etc-in-r-to-target-predictor-levels What I'm looking for, using neuralnet as an example, is how to guide a model toward an output profile once the model is trained. For example: model1 <- neuralnet(formula=out1 ~ intput1 + input2 + input3 +

Hi everyone: Polyclass is a polytomous logistic regression model using linear splines and their tensor products. It provides estimates for conditional class probabilities which can then be used to predict class labels. I know there is Polyclass package in S-plus. So I'm wondering if there is a corresponding package in R? I have been searching for it for quite a while, but still

Hello Using R 2.7.0 on Windows. I am running a linear discriminant analysis as follows <<<< discrim1 <- lda(normvar~ mafmahal+ mrfmahal+ mffmahal+ bafmahal+ brfmahal+ cofmahal+ bmfmahal+ cfmahal+ fractmahal+ antmahal+ absmifmahal+ absifmahal, subset = train) prediction <- predict(discrim1, traintest1[-train,])$class >>> When I do this, I get a warning

Dear all, can anybody help me with the program below? The function predict.lda seems to be defined but cannot be used by errortest. The R version is 1.7.1 Thanks in advance, Stefan ---------------- library("MASS"); library("ipred"); data(iris3); tr <- sample(1:50, 25); train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]); test <- rbind(iris3[-tr,,1],

Hi, First, when I try the example Prostate with bound 0.44 (as in the manual), I got a different result: > l1c.P <- l1ce(lpsa ~ ., Prostate, bound=0.44) > l1c.P .... Coefficients: (Intercept) lcavol lweight age lbph svi 1.0435803 0.4740831 0.1953156 0.0000000 0.0000000 0.3758199 lcp gleason pgg45 0.0000000 0.0000000

On Tue, Mar 06, 2018 at 11:29:40AM +0100, Daniel Vetter wrote: > On Sat, Mar 03, 2018 at 10:53:24AM +0100, Lukas Wunner wrote: > > Modernize vga_switcheroo by using a device link to enforce a runtime PM > > dependency from an HDA controller to the GPU it's integrated into, v2. > > > > https://github.com/l1k/linux/commits/switcheroo_devlink_v2 > > This all

Given a large data.frame, a function trains a series of models by looping over two steps: 1. Create a model-specific subset of the complete training data 2. Train a model on the subset data The function returns a list of trained models which are later used for prediction on test data. Due to how models and closures work in R, each model contains a lot of data that is not necessary for

Dear R:ers, I'm using the svm from the e1071 package to train a model with the option "probabilities = TRUE". I then use "predict" with "probabilities = TRUE" and get the probabilities for the data point belonging to either class. So far all is well. My question is why I get different results each time I train the model, although I use exactly the same data.

I work with Windows, R version 2.4.1 I'm trying to do a discriminant analysis and, in trying to figure out how to do it following the example from R help, I'm getting an error that says 'subscript out of bounds'. I don't know what this means and how to solve it (I'm very new with R) I'm doing everything in this made-up test matrix: group var1 var2 var3 1 1

Hi, I use the lda function from the MASS package to classify some samples according to some chemical properties. If I run lda without cross validation all is ok but, if I run lda with cross validation, the R consol say: resLDA <- ldaRedOx <- lda(Activity ~ TRedOx[,1:6], CV=TRUE, data=dfDataRedOx, subset=train) predLDA <- predict(resLDA, newdata=dfDataRedOx[-train,])$class >

hello, I am using LDA and QDA function of MASS library. I understand Fisher LDA is a method non-probabilistic in nature, so I wonder what happens when I try to predict my test set examples as in: > fit <- lda(labels~., data=train.table, prior=c(.5,.5)) > pred <- predict(fit, data=test.table, prior=c(.5,.5)) Specifically I ask this because in my problem there are 700 examples

> > library(MASS) > Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), + Sp = rep(c("s","c","v"), rep(50,3))) > train <- sample(1:150, 75) > table(Iris$Sp[train]) c s v 22 23 30 > z <- lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train) Error in if (targetlist[i] == stringname) { : argument is of length