search for: naively

Hi, I have a small doubt regarding naive Bayes. I am able to classify the data's properly but i am just stuck up with how to get the probability values for naive bayes. In the case of SVM we have "attr" function that helps in displaying the probability values. Is there any function similar to "attr" in naive Bayes that can be used for displaying the attribute values. my

Several bugs are present in R-2.15.3 and R-alpha due to naive copying of list elements. The bug below is due to naive copying in subset.c (with similar bugs for matrices and arrays): a<-list(c(1,2),c(3,4),c(5,6)) b<-a[2:3] a[[2]][2]<-9 print(b[[1]][2]) Naive copying in mapply.c leads to the following bug: X<-1+1 f<-function(a,b) X A<-mapply(f,c(1,2,3),c(4,5,6),SIMPLIFY=FALSE)

Hi, I've been trying to work with the gnls() function in the "nlme" package. My decision to use gnls() was so that I could fit varPower and such to some of the data. However, in working with a small dataset, I've found that the results given by gnls() don't seem to make any sense and they differ substantially from those produced by nls(). I suspect that I am just

Dear r-users, I am looking for an implementation of the Naive Bayes classifier for a multi-class classification problem. I can not even find the Naive Bayes classifier for two classes, though I can not believe it is not available. Can anyone help me? Uschi -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

hi there!! i am trying to implement the code of the e1071 package for naive bayes, but it doens't really work, any ideas?? i am very glad about any help!! i need a naive bayes with 10-fold cross validation: code: library(e1071) model <- naiveBayes(code ~ ., mydata) tune.control <- tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = min, sampling = c("cross"),

Hi, Currently I testing the packets that contain built-in features for classification. Actually I looked packages such as: e1071, Klar, Caret, CORElearn. However, from what I noticed when building a naive Bayesian classifier, that they package use of the finite mixture model to estimate P (x | C) and using a normal distribution. In my research I use binary data and I want modeled P (x | C), eg the

I am wondering if anybody here have a simple example in R for Naive Bayes. For example, I can do k-means clustering on the "iris" data - data(iris) cl <- kmeans(iris[,1:4], 3) cl$cluster cbind(1:150,iris$Species) =========== But how to do Naive Bayes classification in the same "iris" data? Many thanks! -- View this message in context:

Howdy everyone I?m trying to get Odds ratio and OR confidence intervals using a probit model, but I'm not getting. Do you think you can help me? I?m new with R L naive = summary(glm(pcr.data[,7]~boldBeta_individual+pcr.data$age,family=binomial(link=probit))) naive_answer = c(naive$coefficients[,1:3]) #naive estimates for

Please don't post in HTML. Your post is almost unreadable Also,lease have a look at http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example and/or http://adv-r.had.co.nz/Reproducibility.html for some suggestions about what you should include in your question. Welcome to R. John Kane Kingston ON Canada > -----Original Message----- > From: rosita21 at

The 3.2.4-revised version turned out to give trouble for some of CRAN's subsystems. Accordingly, a rebadged version 3.2.5 is now released; it only differs in the version number and a few clean-up items. If you have a working install of 3.2.4-revised there should be no reason to upgrade it. You can get the source code from http://cran.r-project.org/src/base/R-3/R-3.2.5.tar.gz or wait for it

Dear R-user: After the fitting the Tobit model using zelig, if I use the following command then I can get the regression coefficents: beta=coefficients(il6.out) > beta (Intercept) apache 4.7826 0.9655 How may I extract the "Naive SE" from the following output please? > summary(il6w.out) Call: zelig(formula = il6.data$il6 ~ il6.data$apache, model =

...2.000e+00 3.205e-17 6.241e+16 <2e-16 *** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 Residual standard error: 7.166e-17 on 2 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 3.895e+33 on 1 and 2 DF, p-value: < 2.2e-16 Naively, should not the column Std. Error be different from zero?? What I have in mind, and sure is not what Std. Error means, is that if I carried out a large simulation, assuming each response y_i a Gaussian random variable with mean y_i and standard deviation 2*error=0.6, and then making an ordinary...

Am 07.01.2018 um 19:41 schrieb halfdog: > Hello list, > > I created a page to demonstrate, what would happen when chroot > root directory is writeable. In fact, code execution is possible > already, when only /etc and /bin are writable. I also tried to > escape the chroot jail, but that did not work for non-root users. > > As the 2009 CVE activities mention, that creating

On 05.07.20 12:21, Roman Lebedev via llvm-dev wrote: > On Sun, Jul 5, 2020 at 12:18 PM Shawn Landden via llvm-dev > <llvm-dev at lists.llvm.org> wrote: >> >> >> >> Carry-less multiplication[1] instructions exist (at least optionally) on many architectures: armv8, RISC-V, x86_64, POWER, SPARC, C64x, and possibly more. >> >> This proposal is to add a

Hi, and my apologies for the following very naive question : I would like to read a column of numbers in R and plot a histogram. eg : x<-read.table("txSTART"); y<-as.numeric(x); and I do obtain the error : Error: (list) object cannot be coerced to type 'double'. Please could you let me know the way to fix it. thanks, bogdan <r-help@r-project.org> [[alternative

Hi, I need to classify, using Naive Bayes and Bayes Networks, and estimate their performance using cross validation. How can I do this? I tried the bnlearn package for Bayes Networks, althought I need to get more indexes, not only the error rate (precision, sensitivity, ...). I also tried the *e1071* package, but I could not find a way to do cross-validation. Thanks for everyone. Guilherme.

Hi, The standard error from logistic regression is slightly different from the naive SE from GEE under independence working correlation structure. Shouldn't they be identical? Anyone has insight about this? Thanks, Qiong a<-rbinom(1000,1) b<-rbinom(1000,2,0.1) c<-rbinom(1000,10,0.5) summary(gee(a~b, id=c,family="binomial",corstr="independence"))$coef

Hi, I have written a code in R for classifying microarray data using naive bayes, the code is given below: library(e1071) train<-read.table("Z:/Documents/train.txt",header=T); test<-read.table("Z:/Documents/test.txt",header=T); cl <- c(c(rep("ALL",10), rep("AML",10))); cl <- factor(cl) model <- naiveBayes(train,cl);

Hallo! I would need a code for 10-fold cross validation for the classifiers Naive Bayes and svm (e1071) package. Has there already been done something like that? I tried to do it myself by applying the tune function first: library(e1071) tune.control <- tune.control(random =F, nrepeat=1, repeat.aggregate=min.,sampling=c("cross"),sampling.aggregate=mean, cross=10, best.model=T,

Hi, Sorry for the question, but there is any package that contains the Naive Bayes classifier? Thanks Hugo -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: